The group, which includes the VetPlus, Agri-Lloyd and Farmsense brands, is chaired by David Haythornthwaite who also owns National League football club AFC Fylde

Staff handed bonuses of £10,000 by millionaire football club owner

The biannual event will be staged at the ICC Wales can is expected to generate millions in new orders for Welsh firms

Blas Cymru showcasing the best in the Welsh food and drinks sector

A national pub company has announced it will complete on the purchase of 17 pubs from St Austell Brewery's leased and tenanted estate. Red Oak Taverns, which comprises 212 mainly leased and tenanted pubs will complete net week (October 31).

National company to acquire 17 pubs from St Austell Brewery

National law firm TLT has advised a pan-European solar energy company on its £70m debt facility from Bayerische Landesbank to fund the construction and operation of four ground mounted solar PV projects in the UK.

Law firm TLT advises ILOS on solar PV projects funding

Matillion was co-founded by Mr Scullion and chief technology officer Ed Thompson in 2011 after quitting their jobs in a bid to "revolutionise business intelligence in the cloud". Like any start-up, the move involved risk - something that Mr Scullion says is not celebrated enough in the UK.

'It really pisses me off, actually': Matillion CEO on the UK celebrating failure, super frothy valuations and IPO plans

Matillion recently relocated its UK head office from Altrincham to Salford's Two New Bailey. The office sits alongside the company's sister-HQ in Denver, Colorado. The 28,000sq ft space sits on the sixth and seventh floors of the building, which is also home to Eversheds and Clyde & Co.

But cloud services giant Matillion could surpass them all if it continues the meteoric rise it has enjoyed in recent years. After achieving unicorn status two years ago, the tech firm is drawing up plans to grow even further and is weighing up a possible IPO.

New site will be UK's fourth Fairmont Hotel, alongside The Savoy in London, St Andrews Golf Course and Fairmont Windsor Park, Berkshire

Conversion work set to begin on Mere golf resort hotel scheme

Data productivity provider Matillion has cut the ribbon on its new HQ in the heart of Manchester. The two-story site at Two New Bailey becomes the tech unicorn’s first engineering-focussed centre and European HQ, alongside its sister-HQ in Denver, Colarado.

Matillion launches new HQ to drive data and AI innovation

Matillion

matillion

Top 5 Factors Behind Data Analytics Costs

Over the last year, our team has interviewed more than 200 companies about their data integration use cases. What we discovered is that data integration in 2021 is still a mess. The Unscalable Current Situation At least 80 of the 200 interviews were with users of existing ETL technology, such as Fivetran, StitchData and Matillion. We found that every one of them were also building and maintaining their own connectors even though they were using an ETL solution (or an ELT one - for simplicity, I will just use the term ETL). Why? We found two reasons: Incomplete coverage for connectors Significant friction around database replication Inability to cover all connector needs Many users’ ETL solution didn’t support the connector they wanted, or supported it but not in the way they needed. An example for context: Fivetran has been in existence for eight years and supports 150 connectors. Yet, in just two sectors -- martech and adtech -- there are over 10,000 potential connectors. The hardest part of ETL is not building the connectors, it is maintaining them. That is costly, and any closed-source solution is constrained by ROI (return on investment) considerations. As a result, ETL suppliers focus on the most popular integrations, yet companies use more and more tools every month and the long tail of connectors goes ignored. So even with ETL tools, data teams still end up investing huge amounts of money and time building and maintaining in-house connectors. Inability to address the database replication use case Most companies store data in databases. Our interviews uncovered two significant issues with database connectors provided by  existing ETL. Databases are  huge and serve growing amounts of data. A database with millions of rows, with the goal of serving hundreds of millions of rows, is a common sight.The issue with current ETL solutions is their volume-based pricing. It’s easy for an employee to replicate a multi-million row database with a click. And that simple click could cost a few thousand dollars! Volume-based pricing: With today’s concerns over privacy and security, companies place an increasing importance on control of their data. The architecture of existing ETL solutions often end up pulling data out of a company’s private cloud. The closed source offerings prevent companies from closely inspecting the underlying ETL code/systems. The reduced visibility means lesser trust Data privacy: Both of these points explain why companies end up building additional internal database replication pipelines. Inability to scale with data The two points mentioned above about volume-based pricing and data privacy also apply as companies scale. It becomes less expensive for companies to have an internal team of data engineers to build the very same pipelines maintained in ETL solutions. Why Open-Source Is the Only Way Forward Open-source addresses many of the points raised above. Here is what open source gives us. Having access to and being able to edit the code to your needs is a privilege open-source brings. For instance, what if the Salesforce connector is missing some data you need? With open source, such a change is as easy as submitting a code change. No more long threads on support tickets! The right to customize: You no longer need to convince a proprietary ETL provider that a connector you need is worth building. If you need a connector faster than a platform will develop it, you can build it yourself and maintain it with the help of a large user community. Addressing the long tail of connectors: Because an open source product must support a wide variety of stacks and workflows for orchestration, deployment, hosting, etc., you are more likely to find out-of-the-box support for your data stack and workflow (UI-based, API-based, CLI-based, etc.) with an open source community. Some of them, like Airbyte’s open source Airflow operator, are contributed by the community. To be fair, you can theoretically do that with a closed-source approach, but you’d likely need to build a lot of the tooling from scratch. Broader Integrations with data tools and workflows: If you experience any connector issues, you won’t need to wait for a customer support team to get back to you or for your fix to be at the top of the priorities of a third-party company. You can fix the issue yourself. Debugging autonomy: If the open-source project is open enough (MIT, Apache 2.0, etc.), any team can directly address their integration needs by deploying the open-source code in their infrastructure. Out-of-the-box security and privacy compliance. The Necessity of a Connector Development Kit However, open source itself is not enough to solve the data integration problem.This is because the barrier to entry for creating a robust and full-featured connector is too high. Consider for example a script that pulls data from a REST API. Conceptually this is a simple `SELECT * FROM entity` query over some data living in a database, potentially with a `WHERE` clause to filter by some criteria. But anyone who has written a script or connector to continuously and reliably perform this task knows it’s a bit more complicated than that. First, there is authentication, which can be as simple as a username/password or as complicated as implementing a whole OAuth flow (and securely storing and managing these credentials). We also need to maintain state between runs of the script so we don’t keep rereading the same data over and over. Afterwards, we’ll need to handle rate limiting and retry intermittent errors, making sure not to confuse them with real errors that can’t be retried. We’ll then want to transform data into a format suitable for downstream consumers, all while performing enough logging to fix problems when things inevitably break. Oh, and all this needs to be well tested, easily deployable... and done yesterday, of course. All in all, it currently takes a few full days to build a new REST API source connector. This barrier to entry not only means fewer connectors created by the community, but can often mean  lower quality connectors. However, we believe that 80% of this hardship is incidental and can mostly be automated away. Reducing implementation time would significantly help the community contribute and address the long tail of connectors. If this automation is done in a smart way, we might also be able to improve standardization and thus maintenance across all connectors contributed. What a Connector Development Kit Looks Like Let’s look again at the work involved in building a connector, but from a different perspective. Incidental complexity Setting up the package structure Packaging the connector in a Docker container and setting up the release pipeline Lots of repeated logic: Reinventing the same design patterns and code structure for every connector type (REST APIs, Databases, Warehouses, Lakes, etc.) Writing the same helpers for transforming data into a standard format, implementing incremental syncs, logging, input validation, etc. Testing that the connector is correctly adhering to the protocol. Testing and edge cases happy flows You can see that a lot can be automated away, and you’ll be happy to know that Airbyte has made available an open source Connector Development Kit (CDK) to do all this. We believe in the end, the way to To make a parallel with the concept that is well known in DevOps/Infrastructure, a connector is cattle code, and you want to spend as little time on it as possible. This will accelerate productivity tremendously. build thousands of high-quality connectors is to think in onion layers. pet/cattle Abstractions as onion layers Maximizing high-leverage work leads you to build your architecture with an onion-esque structure: The center defines the lowest level of the API. Implementing a connector at that level requires a lot of engineering time. But it is your escape hatch for very complex connectors where you need a lot of control. Then, you build new layers of abstraction that helps tackle families of connectors very quickly. For example, sources have a particular interface, and destinations have a different kind of interface. Then, for sources you have different kinds like HTTP-API based connectors and Databases. HTTP connectors might be split into REST, GraphQL, and SOAP, whereas Databases might split into relational, NoSQL, and graph databases. Destinations might split into Warehouses, Datalakes, and APIs (for reverse ELT). The CDK is the framework for those abstractions! What Is Already Available Airbyte’s CDK is still in its early days, so expect lots of improvements to come over time. Today, the framework ships with the following features: A Python framework for writing source connectors A generic implementation for rapidly developing connectors for HTTP APIs A test suite to test compliance with the Airbyte Protocol and happy code paths A code generator to bootstrap development and package your connector In the end, the CDK enables building robust, full-featured connectors within 2 hours versus 2 days previously. The Airbyte team has been using the framework internally to develop connectors, and it is the culmination of our experience developing more than 70+ connectors (our goal is 200 by end of the year with help from the user community!). Everything we learn from our own experience, along with the user community go into improving the CDK. Conclusion - The Future Ahead Wouldn’t it be great to bring the time needed to build a new connector down to 10 minutes, and to extend to more and more families of possible integrations. How’s that for a moonshot! If we manage to do that together with our user community, then at long last the long tail of integrations will be addressed in no time! Not to mention that data integration pipelines will be commoditized through open-source If you would like to get involved, we hope you’ll join our - the most active one around data integration - as we connect to the future of open source for the benefit of all! Slack community Also published on: https://airbyte.io/blog/why-etl-needs-open-source-to-address-the-long-tail-of-integrations

Open Source is the Only Way to Address the Long Tail of Integrations

In early March 2021, we announced our $5.2M seed round with Accel for - our open-source data integration platform; 2 months later, we announced our $26M Series-A round led by Benchmark. Airbyte In this article, we want to tell you about what happened behind the scenes, including the deck we presented to the Benchmark team. We hope this will give you some insights about the fundraising process, even though our case might be atypical. If you like what we do, don’t hesitate to subscribe to our or star ! newsletter our GitHub project If you're mostly interested in the deck, here's a . Slideshare link to the Series-A deck The Timeline Some additional context: We actually signed the term sheet for our seed round with Accel in December 2020, but we just closed the round (after the due diligence and all the negotiations on the long form contract) in early February. It then took us about a month to prepare the , which happened on March 2, 2021. announcement for the seed The First Call Benchmark contacted us at the end of February, and we had our first call with them on March 10. Although our seed round wasn’t public information, they already knew about it, as most VCs did. Private news travels fast in the VC world. We witnessed that again during the Series-A, as we got contacted by a lot of VCs after we signed the term sheet, even though we kept it confidential. One hypothesis is that the information leaks during the We’ll come back to that later. vetting process. How did the first contact happen? Chetan Puttagunta and Peter Fenton both reached out to Michel and John, via different mutual connections. Having both of them try to reach out to us was a strong signal about how serious they were about talking with us. Leading up to the Partnership Meeting After the first call on March 10, we had several video calls over the next month. Each one ranging from an hour to several hours. These long form conversations allowed us to get to know Benchmark and, at the same time, allowed Benchmark to get to know us as founders as well as our plans for Airbyte. We knew that we were going to take on a board member with the Series A and it’s critical to have clarity before starting the 10+ year journey together. We felt that this discussion had to happen over several weeks. We presented to the Benchmark partnership on April 12. That’s when we presented the deck below. We sent it the day before so they could prepare their questions and so we could leverage that hour the best. After that, we had calls on April 13 and 14 to negotiate the term sheet, and we got it signed on April 15. During that time, we didn’t interact with any VCs other than Accel. We didn’t need to raise money. And Accel was a great sounding board and was putting our company first. Vetting Process While you can be sure the VC will contact people you have worked with in the past, you want to talk to companies have invested in as well. We try to get feedback from companies in various states of progress: hugely successful, successful but in a slow growth mode, and some that didn’t work out. The goal is to obtain a well-rounded view of the VC’s support and behavior in all scenarios. We made all these calls between the 7th and 14th of April. they It might be possible the information leak happens at this stage. After all, the CEOs that you’re talking to are well connected with other VCs. The Term Sheet A piece of advice here: Try to get a maximum amount of information about the terms of the round written in the term sheet. Once you have signed the term sheet, most of the negotiation you will do will be with the VC’s lawyers rather than the VC partner. So, if you get it written in the term sheet, it’s one less thing that is open to negotiation. And don’t accept a one-page term sheet. Try to get the full set of terms clearly written. This is the only way to know as entrepreneurs that there will not be an egregious term in the final documents. Benchmark clearly spelled out the terms of their financing in their term sheet, which made the closing process smooth and on schedule. Post-Term Sheet As founders, you have two jobs to do after the term sheet is signed: Negotiate the details of the long-form investment agreement with the VC’s lawyers. That’s when having great lawyers helps, as they can make sure to represent your best interests. Complete the remainder of the round. You might have pro-rata rights to abide by for your earlier investor, and you might want to involve some high-profile business angels who can bring a ton of value, too. In the end, we got about a dozen more investors in the round, including Shay Banon (co-founder & CEO of Elastic), Dev Ittycheria (CEO of MongoDB), Auren Hoffman (co-founder of LiveRamp and Safegraph), and SV Angel. Why We Decided to Raise Even Though We Didn’t Need the Money Entrepreneurs often think in terms of valuation and the equity they own in the company. But there is a third factor that is the most important of all: the which goes from 0 (bankruptcy) to 1 (IPO or huge exit). success factor, success factor * ownership * valuation = value you own We did YC in January-March 2020 because we thought it would have a significant impact on our success factor. We chose to raise with Accel (we didn’t need that money then as we still had a full year of runway) because we thought they were the ideal partner to bring us closer to the 1 for our success factor. This is what happened again with the Benchmark team. We were not convinced at all about raising a Series-A now at the start, but each interaction with Chetan and the Benchmark team was bringing immense value and extremely relevant advice. After the third call, we could no longer see Airbyte without them working closely with us to bring us closer to the 1. Now that we knew whom we wanted to partner with, it was all about getting terms (valuation, etc.) that worked for both us and Benchmark. And that would be our advice in fundraising: Optimize first for whom you want to partner with. And then, optimize the terms of the fundraising (valuation included) for that partner. Our Deck As the was public, we couldn’t just reuse it. And a lot of progress had been made since we signed the seed term sheet in December, and we wanted to reflect that. Airbyte seed deck So here’s the Series-A deck we presented to the Benchmark partnership team: https://www.slideshare.net/jeanlaf/airbyte-seriesa-deck Our deck was pretty standard, though some might consider it a bit short. That’s why we include the appendices along with the deck. The Structure and Our Pitch on Each Slide Here are more details on our slides - why we included them and what we were saying during our pitches. 0. Cover Although you might only spend 10 seconds discussing the cover slide, it is important. It’s about showing that you have identified your positioning. In our case, we identify EL(T) as . So, essentially, we want to identify ourselves as the future of the ETL industry here. the future of the ETL industry 1. Industry Context This is the only slide that is common to our seed deck. Thanks to Snowflake’s IPO and Segment’s acquisition, we knew that data infrastructure was hot from an investor point of view. We decided to start with some context on the industry and how we fit into this ecosystem. What we were saying: When you look at the data infrastructure industry, there is often a new category that emerges through a commercial product. Once the market matures, an open-source alternative gets created and ends up taking over the category. This behavior is often seen because data infrastructure requires privacy, security and scale - which cloud-based solutions can’t offer as well as open-sourced ones. There are many examples, such as Kafka, Spark, and now dbt. We want to be the open-source solution for data integration. You might wonder why an open-source approach would also win the format for data integration; sometimes a closed-source cloud-based approach works. This last sentence is a transition to the next slide. 2. Problem We’re Solving This is a rephrasing of our seed round slide with more details. What we were saying: In June and July 2020, we started reaching out to 250 of Fivetran’s, StitchData’s and Matillion’s customers. We ultimately managed to talk to 45 of them. We wanted to know whether an open-source approach would make sense to address data integration. What we learned is that a cloud-based closed-source solution will never be able to fully address the data integration problem. It has several inherent issues. 100% of the companies we talked to were using Fivetran, StitchData or other solutions, while also building and maintaining their own connectors. They did so because either (a) the ETL solution didn’t support the connector they wanted, or (b) the solution supported it, but not in the way they needed. When you look at Fivetran, for instance, you’ll see that after 8 years, they only support 150 connectors. The hard part about ETL/ELT is not about building the connectors, but maintaining them. It is costly, and any cloud-based closed-source solution will be restricted by a ROI (return on investment) consideration. It isn’t profitable for them to support the long tail of connectors, so they only focus on the most popular integrations. During those 45 interactions, we also identified a third issue. Some of the companies were about to stop using Fivetran for some connectors because it was becoming too pricey. The value of an ELT solution is about replacing a paid data engineer who builds and maintains a connector in-house. The amount of work required from an engineer is about the same, whether the volume of data being moved is low or high. So with volume-based pricing, at some point it just stops making sense to use an external solution. And the last inherent issue with a cloud-based approach: Although cloud data warehouses are winning the enterprise market, it is because they are considered part of the data infrastructure. All other solutions must go through a rigorous privacy compliance process that will take several months. 3. Our Solution That’s the slide where we start to get deeper into our differences from our seed deck. What we were saying: There are two main differences with our approach. 1. Being open-source: Enables us to address the long tail of integrations: We built a (CDK) that enables our users to build new connectors in a matter of hours (instead of days!) for our community. The idea is to standardize the way connectors are being built, thus making them much easier to maintain by us and the community. Connector Development Kit Enables our users to customize any connector to their specific needs, and to leverage Airbyte the way they need to, along with other tools in their data stack. Enables our users to start testing Airbyte without asking upper management for permission. So the speed to value in a testing use case is way better than any closed-source solution. Enables us to address the use case of all companies that can’t use a third-party vendor to handle their sensitive data. This includes healthcare, fintech and insurance, as well as enterprises. 2. Current solutions have adopted a -based pricing, which has always been counterproductive for data. Having a value-based pricing. volume We won’t detail much yet what our intentions are here, as this is something that we would like to keep under the hood. 4. Our Product What we were saying: Airbyte addresses two different audiences. With our , we want to empower , such as data analysts and scientists, to start replicating data in a no-code way. Airbyte essentially makes them autonomous; they are no longer dependent on the priorities of the data or software engineering team. It takes literally for a data analyst to replicate data from Salesforce to Snowflake, including the deployment through our Docker Compose. easy-to-use UI data consumers 2 minutes 5. Our Product [2] What we were saying: The second audience we’re addressing is With our and soon our , we enable them to leverage Airbyte to power the data replication they need in their software and workflow. Some will even leverage Airbyte to offer data integrations on their own platform to their customers. data and software engineers. API CLI Airbyte integrates with dbt, Airflow and Kubernetes for now. But our intention is to integrate with the whole stack by the end of the year. Our goal is that Airbyte just works - whatever your data volume, architecture, infrastructure, or connector needs - by the end of the year. 6. Our Team In this slide, we review the expertise of the team. In our case, we have six people coming from Liveramp, where they built and maintained more than 1,000 connectors and were moving more than 100TB of data every day. Our goal is to make investors understand that we’re experienced and have already done it in the past, but now the goal is to enable company to do it. every Since we presented these slides we’ve hired three more people: Natalie K. (Director of Operations), Jenny B. (Senior Software Engineer) and George C. (Senior Software Engineer). By the way, we are hiring! 7. Our Velocity This is possibly the most important slide, as it validates everything we’ve said with facts. Please keep in mind that these metrics are the ones we had on April 11, when we presented the deck to the Benchmark partnership team. The point we wanted to make is how far we’ve come since the seed round’s term sheet was signed. We deliberately didn’t choose the seed closing date, because Accel made the decision to invest in Airbyte in December, not in February. The number of companies using you is most likely the most important data point for investors. What we were saying: We actually started working on Airbyte at the end of July. We soft launched a MVP 2 months later (at the end of September), with only 6 connectors and no support for incremental replications. We wanted to have feedback as early as possible. Since then, we’ve been used by X companies to sync data, we have Y PR contributors and Z issue contributors, and we’ve ramped up the number of connectors to 60. 8. Some Growth Metrics Investors will always ask for the names of companies that are using you. So here, we’re just anticipating the question. Our point on this slide is to show that the conversion rate from users having synced data to users who are using us in prod every day is growing every week! 9. Our Roadmap Now that we’ve shown our velocity up to this point, it’s time to show what we can accomplish in the next few quarters. Investors want to see where you will be when it is time to raise the next round. That is this slide’s goal. 10. Our Ask This is the last slide of the pitch. The goal here is that you shouldn’t need to say much! It shouldn’t come as a surprise, and is a direct consequence of the roadmap slide. 11. Appendix! You need some sort of slide to separate the main deck from the appendix. No surprise here! We only showed the appendix slides if asked relevant questions, but we sent the deck with the appendix. 12. The Competitive Landscape Here are a few choices we made that distinguish Airbyte from other open-source solutions: What we were saying: So the velocity in terms of execution, team and community growth are not comparable. Airbyte’s connectors are usable out of the box through a UI and an API, with monitoring, scheduling and orchestration. It takes literally 2 minutes to start replicating data from Salesforce to Snowflake. Airbyte runs connectors as Docker containers, so they can be built in the language of your choice. Airbyte’s components are modular, and you can decide to use subsets of the features to better fit in with your data infrastructure (e.g., orchestration with Airflow or Kubernetes or Airbyte…) We integrate with dbt for the transformation piece and will soon let the community contribute normalization schemas for all connectors. Unlike Singer, Airbyte uses one single open-source repo to standardize and consolidate all developments from the community, leading to high quality connectors. We built a compatibility layer with Singer so that the Singer community can easily migrate their work to Airbyte. The point for us here was to demonstrate that we are well on our way to becoming the industry’s open-source standard. 13. Some Growth Metrics [1] Even though GitHub stars are essentially vanity metrics, they are an indicator of the awareness you create. And it adds more support in demonstrating growth to your investors. 14. Some Growth Metrics [2] These metrics are much more important to show, as our defensibility will come through the community. The exponential growth was what was exciting to investors. 15. Some Growth Metrics [3] We anticipated that Series-A investors might need more metrics than we included in the seed deck. We showed the growing usage per user. If we make our prod users successful, they will naturally put more and more of their connectors on our platform when moving away from other solutions. 16. Some Growth Metrics [4] Even though we never actually presented this slide, it was important for us to include it in the deck. As we sent the deck before the meeting, it was already answering the question, “Who is using you in prod?” 17. Our Investors This is probably the slide we showed the least often, as serious VCs will already know who your main investors are. But you should still have it ready just in case. That’s it, folks! Now you know why we included the slides we did, and what we were trying to accomplish with each one. Hope this helps you understand us better, and maybe helps you prepare for your own investor encounters. Let us know what you think or if you have any questions in the comments below! Previously published at https://airbyte.io/blog/how-airbyte-raised-its-series-a-round-2-months-after-its-seed

Valuable Insights About The Fundraising Process

There are quite a few decks already available online, and we found them all useful, each in different ways. That’s why we thought the deck we used for our seed round could be helpful to some companies, especially those that are open source or developer tools. For context: on that seed deck, we started working on our , , in the end of July, and raised our seed round with Accel in December, so only 5 months in. We’re assuming the deck was acceptable, as we raised the seed after only 13 days in the fundraising process. open-source data integration platform airbyte.io We will write a dedicated article about the process within a few days, so this article is really about the deck itself. Our Deck Here’s the Slideshare . link Our deck was pretty standard, though some might consider it a bit short. That’s why we always sent the appendixes along with the deck after our meetings. Here are more details on our slides – why we included them and what we were saying during our pitches. 0. Cover Although you might only spend 10 seconds discussing the cover slide, it is important. It’s about showing that you have identified your positioning. 1. Industry Context Thanks to Snowflake’s IPO and Segment’s acquisition, we knew that data infrastructure was hot from an investor point of view. We decided to start with some context on the industry and how we fit into this ecosystem. What we were saying: When you look at the data infrastructure industry, there is often a new category that emerges through a commercial product. Once the market matures, an open-source alternative gets created and ends up taking over the category. This behavior is often seen because data infrastructure requires privacy, security and scale, which cloud-based solutions can’t offer as well as open-sourced ones. There are many examples, such as Kafka, Spark, and now DBT. We want to be the open-source solution for data integration. You might wonder why an open-source approach would also win the format for data integration; sometimes a closed-source cloud-based approach works. This last sentence is a transition to the next slide. 2. Problem We’re Solving What we were saying: In June and July, we started reaching out to 250 of Fivetran’s, StitchData’s and Matillion’s customers. We ultimately managed to talk to 45 of them. We wanted to know whether an open-source approach would make sense to address data integration. What we learned is that a cloud-based closed-source solution will never be able to fully address the data integration problem. It has several inherent issues. 100% of the companies we talked to were using Fivetran, StitchData or other solutions, while also building and maintaining their own connectors. They did so because either (a) the ETL solution didn’t support the connector they wanted, or (b) the solution supported it, but not in the way they needed. When you look at Fivetran, for instance, you’ll see that after 8 years, they only support 150 connectors. The hard part about ETL/ELT is not about building the connectors, but maintaining them. It is costly, and any cloud-based closed-source solution will be restricted by a ROI (return on investment) consideration. It isn’t profitable for them to support the long tail of connectors, so they only focus on the most popular integrations. During those 45 interactions, we also identified a third issue. Some of the companies were about to stop using Fivetran for some connectors because it started to become too pricey. The value of an ELT solution is about replacing a paid data engineer that builds and maintains a connector in-house. The amount of work required from an engineer is almost the same whether a low volume or a high volume of data is being moved. So with volume-based pricing, at some point it just stops making sense to use an external solution. And the last inherent issue with a cloud-based approach: although cloud data warehouses are winning the enterprise market, it is because they are considered part of the data infrastructure. All other solutions must go through a rigorous privacy compliance process that will take several months. 3. Our Solution What we were saying: We’re building an open-source ELT platform that syncs data from SaaS apps, APIs and databases to data warehouses, data lakes and other databases. Our solution can fully integrate with your data infrastructure and stack, if you are using Kubernetes or Airflow for orchestration, or DBT for transformation. Our goal is to become the open-source standard for anything ELT by the end of 2021. 4. How it addresses the problem What we were saying: By making it trivial to build and maintain connectors using Airbyte rather than doing it in-house, we will become the new standard for building connectors. This will help us support the long tail of connectors. And since connectors run as Docker containers, you can build them in the language of your choice. As connectors are open-sourced, any team can edit a pre-built connector and tune it to their needs. If a connector breaks, anyone can jump on the code, submit a PR and, once approved, the change can be propagated across all the existing users of that connector. Airbyte focuses first on a self-hosted offer;  pricing will be based on the feature and number of connectors used. The pricing will not be indexed on the volume of data. Being open sourced enables us to have a bottom-up approach with a frictionless adoption from data teams, without going through privacy compliance, as we handle data security as a first-class citizen. 5. Who We’re Targeting / Our Go-To-Market What we were saying: With Airbyte, we wanted to address 2 different audiences: Data consumers, including data analysts and scientists.Data engineers who were building and maintaining the connectors themselves, or managing the data infrastructure. What made Fivetran successful is that they enabled data consumers to leverage the data without the help of data engineers – they made them autonomous (as much as Segment made product teams autonomous). Airbyte provides a UI that makes it very easy to start replicating data for non-technical users. It takes literally for a data analyst to replicate data from Salesforce to Snowflake, including the deployment through our Docker Compose. 2 minutes In addition, Airbyte will offer data integration connectors through an API that data engineers can leverage to build their own workflow and applications. This is also a way for Airbyte to address SaaS businesses that want to offer their own integrations to their customers through Airbyte. 6. Our Team In this slide, we review the expertise of the team. In our case, we have 4 people coming from Liveramp, where they built and maintained more than 1,000 connectors and were moving more than 100TB of data every day. Our goal is to make investors understand that we’ve already done it in the past, but now the goal is to enable every company to do it. 7. Our Velocity This is possibly the most important slide, as it validates everything you’ve said before with facts. The number of companies using you is most likely the most important data point for investors. What we were saying: We actually started working on Airbyte in the end of July. We soft launched a MVP 2 months later (at the end of September), with only 6 connectors. We wanted to have feedback as early as possible. It’s been 6-7 weeks since we soft launched, and we are now used by X companies, we have Y contributors, and we’ve ramped up the number of connectors to 43. 8. Our Roadmap Now that we’ve shown our velocity up to this point, it’s time to show what we can accomplish in the next few quarters. Investors want to see where you will be when it is time to raise the next round. This slide’s goal is to accomplish this. 9. Our Ask This is the last slide of the pitch. The goal here is that you shouldn’t need to say much! It shouldn’t come as a surprise, and is a direct consequence of the roadmap slide. 10. Appendix! You need such a slide to separate the main deck from the appendix. No surprise here! We only showed the appendix slides if asked relevant questions. 11. Some growth metrics [1] The point for us here was to insist that we hadn’t done a hard launch yet, only a soft launch on our social networks on 09/24. The rest of the growth is organic. 12. Some growth metrics [2] Investors will always ask for the names of companies that are using you. So you should always have such a slide ready. The more recognizable the names of the companies are, the better. 13. The competitive landscape What we were saying: Here are a few choices we made that distinguish Airbyte from other open-source solutions: Airbyte’s connectors are usable out of the box through a UI and an API, with monitoring, scheduling and orchestration. Airbyte runs connectors as Docker containers, so they can be built in the language of your choice.Airbyte’s components are modular, and you can decide to use subsets of the features to better fit in with your data infrastructure (e.g., orchestration with Airflow or K8s or Airbyte’s…)We intend to integrate with DBT for the transformation piece soon and let the community contribute normalization schemas for all connectors. Unlike Singer, Airbyte uses one single open-source repo to standardize and consolidate all developments from the community, leading to higher quality connectors. We built a compatibility layer with Singer so that Singer taps can run within Airbyte. 14. Our investors This is probably the slide we showed the least often, as serious VCs will already know who your main investors are. But you should still have it ready just in case. That’s it, folks! Also published here .

All 14 Slides from the Deck We Used to Raise Seed with Accel in 13 Days

Do you know why microservice design is so popular within the development of BI tools? The answer is clear: it helps to develop scalable and flexible solutions. But microservice architecture has a great drawback. Its performance usually requires great improvements. The FreshCode team also faced the problem and I’ve decided to show how we coped with it. The article is written together with FreshCode CTO and based on our recent case of development reporting microservice. You will find here its tech scheme, estimates, as well as a list of tools for on-premise and SaaS products. MEET MICROSERVICE DESIGN. WHAT SHOULD YOU CARE ABOUT? If you wonder why is microservice style so popular, you should think about the recent IT trends. The demand for Agile and DevOps practices led to microservice popularity. Today such great players as Uber, Airbnb, Netflix use microservices to solve their business problems. The best way to explain what does microservice design mean is to compare it with a common monolith app. The monolithic system uses one processor for all the logic. Meanwhile, microservice includes a few separate processors. They usually are: UI database server Any change in the system leads to the deployment of a new version of the server part of the system. Let’s consider the concept in detail. MICROSERVICE DESIGN IN DETAIL Microservice design means a set of services, but the definition is vague. I can single out 4 features that a microserver usually has: the decentralized control of languages and data responsibility for a specific business need automatic deployment endpoints On the picture below you can see microservice design compared to a monolith app. Scalability in microservices One of the main benefits of the microservice design is its scalability. You can scale several services without changing the whole system. So, you save resources and keep the app less complex. One of the most famous cases that prove this fact is Netflix user base. The company had to cope with the growing subscribers’ database. The microservice design was a great solution for scaling it. Each microservice needs its own database. Otherwise, you can’t use all the benefits of the modularization pattern. But the variety of databases leads to challenges in the reporting process. We will discuss the problem later. Microservice design speeds up app development and allows to launch the product earlier. Each part can be rolled out separately. So, the deployment of microservices is quicker and easier. Pros of microservices 1. The possibility of convenient horizontal system scaling 2. Increased development team members productivity 3. Simplification of the debugging and maintenance processes 4. The ability to work in smaller teams and use an Agile approach 5. Flexibility in continuous integration and deployment Cons of microservices Despite all these benefits, microservice architecture has its own drawbacks. I mean the necessity of operating many systems and completing various tasks in the distributed environment. So, the main microservice pitfalls are: 1. The complexity of microservice design makes developer plan and act more carefully. 2. The external API communication in microservice architecture leads to more significant risks of attacks. 3. Sometimes it’s difficult to switch between them in the development and deployment processes. REPORTING IN MICROSERVICE SYSTEM We worked on a legacy EdTech project. The system was very complex and included many microservices. Its main parts were: sophisticated financial and billing system multi-organisation structure for large group entities workflow management tool for business processes integrated bulk email, SMS and live chat online system for surveys, quizzes, examination flexible assessment and learning management system FreshCode worked on the project on the stage of migrating to a new interface. The product was preparing for the global launch. The microservice system was supposed to process great amounts of data. As for the app target audience, it was developed for that manage 100s of campuses large education networks that have up to 200k schools, colleges and universities governments Meanwhile, the EdTech app design was convenient both for great education networks and a of about 100 students. small school So, FreshCode development team faced the problem of managing and improving the performance of the complex microservice architecture. It should be mentioned that the client wanted to build both SaaS and self-hosted systems. So, we have chosen the technical solutions keeping this fact in mind. IMPROVING PERFORMANCE IN MICROSERVICES The process of generating reports required engagement with different services. Thus, it caused performance issues. That’s why Freshcode team decided to optimize the app architecture by creating a separate reporting microservice. It received data from all the databases. Then, it saved them and transformed into custom reports. On the picture below you can see the scheme of reporting microservices system and technologies for its implementation. Yellow color marks all microservices in the system. Each of them has its own database. The reporting module tracks all changes in them with the help of a messaging system. Then, it stores the new data in its own report database. 6 STEPS OF THE MICROSERVICE IMPLEMENTATION Let’s look at the 6 main part of the reporting system, technologies that can be used and the best solutions. Change Data Capturing (CDC) CDC tracks every single change (insert, update, delete) and performs some logic on it. There were 3 possible tools for the first step of implementing the microservice reporting system. 1. Apache NiFi It allows creating simple CDC without coding at all. Apache NiFi has a lot of built-in processors and supports data routing, transformation and system mediation logic. Pros: Support of cluster mode and easy scaling Built-in PutToKafka and PutToKinesis activities Implementation of custom activities on any JVM language User-friendly UI Cons: No predefined data format for messaging between activities Supports only JVM languages The quality of default activities isn’t perfect No Oracle CDC activity 2. StreamSets Data Collector Popular open source solution for continuous big data ingestion in a microservice reporting system. Its main advantages are simple creation of data pipelines and support of many widespread technologies. Pros: Built-in AWS S3, Kinesis, Kafka, Oracle, Postgres processors Open source software can be adjusted for your needs Simple and convenient UI Support of most of the popular tools Cons: It’s a new solution that is still actively developing It’s a little bit difficult to start working with StreamSets Data Collector 3. Matillion The innovative ELT architecture has an easy-to-use interface. It is built specifically for Amazon Redshift, Google BigQuery and Snowflake. Pros: A proprietary tool Support of the development team Well-tested solution Cons: Only several databases can be used with this tool ELT architecture doesn’t match to all projects Oracle was the main database of our microservice reporting system. So, we choose StreamSets Data Collector, because of Oracle CDC support out of the box. Messaging System It allows sending messages between computer systems, as well as setting publishing standards for them. 1. Apache Kafka One of the most famous tools for real-time analytics. Apache Kafka has high throughput and reliability characteristics. Pros: High throughput, fault tolerance, durable Great scalability, high concurrency Batch mode, native computation over stream A great choice for on-premise microservice reporting system Cons: Requires DevOps knowledge for correct setup No built-in monitoring tool 2. AWS Kinesis It simplifies collecting, processing, analyzing streaming data. Amazon Kinesis offers key capabilities for the cost-effective process at any scale. Pros: Easy to manage and scale Great integration with other AWS services Almost no DevOps effort Built-in monitoring and alert system Cons: Needs some cost optimizations No way to use for on-premise software Although Apache Kafka required a bit more effort to deploy and setup, we used it as a cost-efficient on-premise solution. Streaming Computation Systems The high-performance computer system analyzes multiple data streams from many sources. It helps to prepare data before ingestion. So, it’s possible to denormalize/join them and add any info if needed. 1. Spark Streaming Brings Apache Spark’s language-integrated API for stream processing. So, it allows writing streaming jobs the same way we write batch jobs. Pros: Stateful exactly-once semantics out of the box Fault-tolerance, scalability In-memory computation Cons: Pretty expensive to use Manual optimization No built-in state management 2. Apache Flink It is useful for stateful computations over unbounded and bounded data streams. Apache Flink suits for all common cluster environments and performs computations at in-memory speed. Pros: Exactly once state consistency SQL on Stream & Batch Data Low latency, scalability, fault-tolerance Support of very large state Cons: Requires high programming skills Complicated architecture Flink community is less than Spark but growing 3. Apache Samza The scalable data processing engine for real-time analytics that can be used in a microservice reporting system. Pros: Can maintain a large state Low latency, high throughput, mature and tested at scale Fault-tolerant and high performance Cons: At-least-once processing guarantee Lack of advanced streaming features (watermarks, sessions, triggers) 4. AWS Kinesis Services The set of tools includes Data Firehose, Data Analytics, and Data Streams. As a result, it helps to build powerful stream processing without implementing any custom code. Pros: Pay only for what you use The easiest way to process data streams in real time with SQL Handle any amount of streaming data Cons: No way to use on-premise Complicated to customize AWS provides a great set of tools for ETL and data procession. It’s a good start point. But there is no way to deploy it on custom servers. That’s why it doesn’t fit for on-premise solutions. Apache Flink is the most feature reach and performant solution. It allows storing large application state (multi-terabyte). But it requires more developers to be involved and should be deployed by yourself. Data Lake The central repository of integrated data from one or more disparate sources. It stores current and historical data in one single place. So, we can use them for creating analytical reports, machine learning, etc. 1. AWS S3 The object storage service offers industry-leading scalability, data availability, security and performance. Pros: Easy to integrate with other AWS services Designed for 99.999999999% (11 9’s) of data durability Cost-effective for rarely accessed data Has an open source implementation with full API support Cons: High network pricing Previously S3 met availability issues, but it’s not a problem for a Data Lake 2. Apache Hadoop The primary data storage system used by Hadoop applications. It allows storing and processing large amounts of data. Pros: Efficiently works with huge amounts of data Integration with many analytical and operational tools (Impala, Hive, HBase, etc) Cons: Complicated to deploy and manage Needs to set up monitoring and high availability We decided to start with AWS S3. It has an open source implementation. That’s why we could integrate it to the on-premise microservice reporting system. Report Databases 1. AWS Aurora It is up to 5 times faster than standard MySQL databases and 3 times faster than PostgreSQL databases. Pros: Pretty fast SQL database High Availability and Durability Fully Managed Easy to scale Cons: Bad performance for analytical reports in case of big data projectsThe minimally available instance is too big, but we can easily replace it by plain PostgreSQL 2. AWS Redshift Redshift delivers 10 times faster performance than other data warehouses. It is using machine learning, massively parallel query execution and columnar storage on high-performance disk. Pros: May run queries on external S3 files Easy to set up, use and manage Columnar storage Cons: Doesn’t enforce uniqueness Can’t be used as a live app database It’s mostly useful for run aggregation on a large amount of data 3. Kinetica The vectorized, columnar, memory-first database designed for analytical (OLAP) workloads. Kinetica automatically distributes any workload across CPUs and GPUs for optimal results. Pros: Pretty fast aggregation performance, run on GPU and CPU Supports materialized join views, and can update them incrementally Cons: GPU instances still cost a lot No way to join data between different partitions 4. Apache Druid It generally works well with any event-oriented, clickstream, time series, or telemetry data, especially streaming datasets from Apache Kafka. Druid provides exactly once consumption semantics from Apache Kafka and is commonly used as a sink for event-oriented Kafka topics. Pros: Druid can be deployed in any *NIX environment on commodity hardware Best for interactive dashboards with full drill-down capabilities Stores only pre-aggregated data Cons: Isn’t perfect for custom reports that may be built by users Works only on time series data No full join support All of these databases are amazing. But our client’s goal was to create reports based on all data from all microservices. So, the development team considered AWS Aurora as the best choice for this task. It simplified the workflow a lot. Report Microservice The report microservice was responsible for storing information about data objects and relations between them. It also stood for managing security and generating reports itself. Since these reports were based on the chosen data objects. TECH SOLUTIONS We prepared 2 variants of the technological stack for the microservice reporting system. As for the SaaS product on AWS, we used: StreamSets for CDC Apache Kafka as a messaging system AWS S3 Data Lake AWS Aurora as a reporting database AWS ElasticCache as an in-memory data store The reporting microservice was written in . You can see rough estimates for SaaS solution on the table below. NodeJS Note: These are calculations for production deployment. The development process required much smaller infrastructure. Such infrastructure was the most appropriate for the client’s requirements. Its main advantage was the easy way to replace AWS services with self-hosted solutions. It allowed us to avoid code/logic duplication for different deployment schemas. For on-premise one we used Minio, PostgreSQL, Redis accordingly. Their APIs were fully compatible. So, we didn’t have any significant problems in the microservice reporting system at all. RESULTS Our team solved the clients’ technical challenges. The reporting microservice module was effective and convenient. It was capable of: Generating clear and convenient reports Providing many standard reporting templates Adding a large number of filters Customizing report interface FreshCode client improved the microservice reporting system and achieved these goals: the app’s architecture and design to update the product by adding new features to improve performance, increase flexibility and scalability to optimize If you are interested in solving the same problem or have any other technical challenges, contact our . We provide free expert advice for startups, small business and enterprises. Check FreshCode portfolio to find out other interesting . team projects Would you like to read more case-based articles? Let me know in the comments below and stay in touch! *** The original article was published on FreshCode blog

Business Intelligence in microservices: improving performance

An engineer turned venture capitalist, Andy Vitus of Scale Venture Partners invests in companies transforming the data center. Andy has worked with companies such as CircleCI, DataStax, JFrog, Matillion, PubNub, Realm, Treasure Data, Unbabel, Unifi, and Stormpath. Davis Baer : What’s your background, and what are you working on? I’m actually an engineer by training. I have a Masters in Electrical Engineering from Stanford and a Bachelor’s Degree in Electrical Engineering from the University of Cape Town in South Africa (where I grew up). I was working on neural networks and computer vision in the 90’s, detecting stolen diamonds in x-ray images. Andy Vitus: After Stanford, I decided that I wanted to be a part of the innovation and creation happening around me in Silicon Valley. So, I went to work as an engineer for Electronics for Imaging, designing embedded print server platforms and video compression circuits. I spent about six or seven years in engineering and did a brief stint as a financial analyst before joining venture. I’ve been a Partner at Scale Venture Partners for about 15 years. My investment focus has always been on next-generation data center technologies. I started in semiconductors then moved to hardware, and have been investing in software and microservices for most of the last decade. I’m really bullish on horizontal technologies helping improve the software development and delivery lifecycle. What types of companies do you invest in? I’m interested in next-generation enterprise software and data center technologies, with a particular focus on microservices, machine learning (again!) and the DevOps process. In terms of company stage, I typically invest at the Series A or B. At Scale Venture Partners, we invest in early-in-revenue enterprise software companies looking to scale. This typically happens around the Series B stage, but can also come as early as the Series A and extend into the Series C, depending on the company. Essentially, we’re looking for companies that have a product in the market, are seeing early revenue, and are really looking to scale their operations to accelerate their growth and become an enduring brand. So, if what I’ve just described sounds like you… drop me a line! What motivated you to become a venture capitalist? I didn’t set out to become a venture capitalist. I was very happy working as engineer — it was an exciting time to be in tech in Silicon Valley. I also worked a bit as a financial analyst and discovered that I enjoyed the investing side of things, too. I had a few friends tell me, “If you like tech and investing, you should really check out venture.” Being from South Africa, I’d never really thought about it. But then the guy sitting next to me at work got approached by a headhunter for a VC firm. And he told the headhunter that he should talk to me. The more I learned about venture, the more I began thinking that I should give it a try. It ended up being a great fit. I’ve really enjoyed it. When I joined Scale Venture Partners 15 years ago, I was a bit of an outlier in the VC world. Back then, almost everyone in venture had finance backgrounds and seemed to have wanted to be in it from the start. There were very few engineers in venture. But it’s changed a bit as several successful founders (especially those from the dot-com era) have become investors, and as more venture firms realize that they need technical expertise. What do you look for when you’re making an investment? I wrote a a few years back with my scorecard for evaluating potential portfolio companies. It’s still very true to my investment thesis today. blog post In essence, I’ve found there are a few key characteristics of successful developer-oriented SaaS companies: In other words, they solve a problem felt by every single company. If every software application or DevOps team needs your technology, you have a much bigger addressable market opportunity. , for example, provides a universal repository manager that’s helping companies (everyone from Spotify and Netflix to Toyota and Invisalign) accelerate their entire DevOps process. Similarly, translation service helps companies scale their global operations to better reach and service customers around the world. They target broad, horizontal functionality. JFrog Unbabel’s Technologies that address complexities in processes that are required to work flawlessly all the time are a sure way to win and keep customers. Every company needs to process online payments and they need that infrastructure to be reliable — it’s no wonder that developers love . 2. They’re mission-critical. Stripe When you have to support traffic from thousands of enterprises, the scale can quickly become enormous. If you haven’t designed your infrastructure to handle massive, global scale from the outset, it’s going to be difficult to support your customers over time. , for example, built a global real-time network that can deliver missions of messages per second, with almost no perceptible latency. 3. They have instant global scale. PubNub Companies that seem boring often have less competition and are therefore highly profitable in the long run. started by helping developers make sense of log files, while helps companies move data from all their applications into public cloud data warehouses. Both of these companies have created technologies that address pain points that every developer team faces, and because they’re also complex and unglamorous, developer teams are usually happy to have someone else help them out. 4. They’re solving “unsexy” problems. Splunk Matillion What’s your advice for startups looking to get funded? In addition to the qualities I just mentioned, I would add that companies should focus on building a great product that customers love. There’s a perception that fast product development and product excellence are at odds with each other. I’ve found that if you have a fantastic product that engenders love from your customers — a polished, differentiated product that’s well-designed and meets customer needs — you’ll perform well on metrics like sales efficiency over the long run. What’s the biggest mistake entrepreneurs make? Related to my point above, I would say that it’s falling into the trap of releasing a product — under the guise of the “lean startup” model — before it’s completely ready. I’m not a fan of throwing out a half-baked concept and calling it an MVP just to see if it sticks. That’s missing the point of the product being viable! Releasing a product too early also shows a lack of respect for customers. The most successful companies are ones that are customer-first, who take the time to understand their customers and build something that they really love. You can bet those companies aren’t releasing products or new iterations that are broken. Refining your product while it’s out on the market only serves to damage your brand. Take pride in your product — pick one problem and then build a best-in-class solution. If you weren’t a VC, what would you be doing? If I weren’t an investor, I’d be an engineer. Even today, I’m a hobbyist engineer — I still love coding, testing and trialing different technologies. As I mentioned earlier, I started in machine learning at Stanford 24 years ago. It’s funny to see things like AI and computer vision becoming so popular again. Back then there was a lot of hype around those technologies, but they didn’t live up to the promise. We’re seeing a lot of breakthroughs now, which is exciting. And I believe that this time they truly will be transformative. This interview is brought to you by , a tool to schedule and automatically repeat your posts on Facebook, Instagram, Twitter, Pinterest, LinkedIn, and Google My Business OneUp

VC Interview: Andy Vitus of Scale Venture Partners

I recently joined as a data engineer and was getting ramped up on , a workflow tool that we used to manage ETL pipelines internally. If you’re totally new to Airflow, imagine it as a souped-up crontab with a much better UI. Plaid Airflow It’s fast! It’s flexible! It’s free! It’s Airflow! Around the time that I was joining, Plaid was migrating onto for visualizing SQL queries, and my immediate mission became to get more of the data people relied on for analytics insights into our nascent cluster, the data warehouse we query from Periscope. My colleague had spent some time harvesting our MongoDB tables into Redshift by tailing the oplogs and doing some post-processing in Spark, as well as parsing through a bunch of log dumps in S3 to create meaningful analytics tables. Periscope Data Redshift Plaid ETL pipeline circa early 2018 Motivation Kindly, my coworker left a more straightforward task to me to help me get ramped up with Airflow — moving data regularly from MySQL to Redshift. We had recently begun using Amazon Aurora instances on RDS, and needed to harvest the data from RDS and load it into Redshift to establish KPIs for these new datasets. Our goal was to timeframe this exercise to a week, so we set ourselves some constraints: We were going to use MySQL’s ability to select into s3, and Redshift’s UNLOAD command. We would only perform complete table-copies once every day, for every table. Since our largest table took only a few hours to copy, we decided to accept pulling all of the data every night as both easier to implement and more resilient to changes or incongruities in the two data sources. We would work on getting the most important data, and not worry about details like deeply nested json columns or binary image files stored in the database. Designing a workflow We wanted to design a translation from MySQL to Redshift, and knew that there would have to be a translation of the schema. Fortunately, AWS provides a resource We can pull the schema out of MySQL using a straightforward query: comparing MySQL and Redshift types. table_name, column_name, data_type, ordinal_position, character_maximum_length information_schema.columns table_schema = database(); select from where We handled a few types manually, for example instead of moving binary data over we would detect a binary type and instead return a boolean as for whether the column was null or non-null, to avoid having to copy a large amount of binary data over the wire that would be unusable for analytics. We wrapped the functionality into some python scripts that generates translation configurations. Example MySQL configuration We then created dag_mysql_processor.py to take in these database configurations and generate the associated dags. It iterates through each entry and generates the corresponding step using a series of SQL templates that we wrote using the as a guide. For example: AWS Docs The result is a batch of Airflow DAGs, one for each table in a MySQL Database. Currently we have each of these DAGs running once daily, which provides a good-enough latency for our current use-cases, by completely re-building the table once a day. Our linear DAG pulling data from MySQL to S3 to Redshift Bumps in the Road The first bit of trouble came about from trying to do a hot-swap. We wanted to ensure that the table looked correct before replacing the old data, so we added a step to validate the row counts. In the first version of our DAG, we executed each statement as a separate airflow task, but tables could occasionally disappear. Combining the swap step into a single, transactional task prevents any table downtime for our style of full-table replacement. V1 of the project had a race condition in dropping and recreating the table. We anticipated that full-copies would be troublesome, as some of our tables are many billions of rows and a global “success or fail” — a consequence of only doing full table copies — would be hard to recover from in real-time. We ran a few experiments on some of our largest tables to understand where errors may be occurring, and quickly became our go-to debugging . stl_load_errors tool * stl_load_errors starttime 1; select from order by desc limit Some of these issues we’re in translating columns from MySQL types to Redshift types. We were able to resolve a good handful of these issues by adding some logic to account for specific types during the conversion and to account for patterns in our schema, which we were able to match based on column name and type. potential_binary_column = column_name column_name 'md5' in or 'uuid' in Another culprit of errors are newlines present in the data, which causes a line to get split into two (or more) rows which then fail to parse. The solution here was to use a consistent and purposeful delimiter on either side of the equation. From mysql: {{ info.columns }} {{ info.table_name }} OUTFILE s3 '{{ info.s3_path }}'FIELDS TERMINATED enclosed escaped **by '\\'**LINES TERMINATED **BY '\n'**MANIFEST **ON**OVERWRITE **ON**; select from INTO BY ',' by '"' And pairing it with the Redshift query "{{ info.schema_name }}"."{{ info.table_name }}"; "{{ info.schema_name }}"."{{ info.table_name }}" '{{ info.s3_path }}.manifest'iam_role '{{ PLAID_REDSHIFT_ROLE }}'**DELIMITER** {{ info.max_error }}**ESCAPE**; TRUNCATE COPY from ','TRUNCATECOLUMNSEMPTYASNULLACCEPTINVCHARSIGNOREBLANKLINESNULL AS '\\N'MANIFESTREMOVEQUOTESSTATUPDATE ONMAXERROR Dawn of a New Day — The Intern Arrives Instead of hard-coded schema files, we want to move the configuration files into a persistent storage layer which we planned on calling , or database-database — a moniker we are inheriting from members of our engineering team who worked on a similar system at Square. dbdb We received the feedback to not call it around the same time that Michael Troute joined us for the Summer as a software engineering intern. He identified a need for Plaid to support ad-hoc uploading of CSVs to our Redshift cluster, and we agreed to combine CSV uploads, DBDB, and other planned ETL improvements under the broader umbrella of Data Warehouse Management. dbdb This paid immediate dividends for our development team. When we first started working with Airflow there were a handful of DAGs governed by a pair of configuration files, but the addition of several MySQL databases and an increasing suite of use cases led to the team having to open several pull requests just for configuration changes. Storing the state in the data warehouse manager instead lets us more easily modify the system— automatically adding and removing tables and columns as they get added or dropped from the upstream database and adding custom functionality like setting Redshift sort and distribution keys, and deploying better methodology for our database ingestion. Some of our tables are billions of rows, and many of these tables are either “append-only” or “sliding-window” updates. For append-only tables the proverbial “low-hanging-fruit” is to only query new rows from the database. For other tables, the rows that are updated are usually created within the past few days, so a mixture of full, partial, and incremental updates will make sense here. Iterative loading, easily controlled maintaining state in the Data Warehouse Manager Alternative Solutions We took a foray into using existing solutions that would save us the engineering effort. Our favorite tool out of this investigation was , and if you’re looking for a drag-n-drop style data pipeline tool, I would definitely check them out. They offer an Amazon Machine Image (AMI) that you can deploy inside of your AWS infrastructure and have great support for these kinds of problems. Ultimately, we identified that our use-case required too much external processing for Matillion to be the right solution for us. Matillion If you’re looking for a cloud provider, we’ve had success using for data processing pipelines, recommends additional vendors, and recommends a similar method using mysqldump. Stitch Data this post from Amazon Panoply Conclusion Airflow has been a reliable tool for us and is an important part of our in-house ETL efforts. Plaid works with many different data sources, and for non-sensitive datasets + 3rd-party data and have been instrumental in building up data workflows. For our data under our on-premise security umbrella, Airflow has shown itself to be reliable, informative, and accessible to new members of the team getting ramped up on data ingestion at Plaid. Stitch Segment Plaid has a blossoming data ecosystem spanning many billions of rows. If you’re an airflow veteran or live and breathe ETL pipelines we’d love to chat! We’d especially love to open-source our efforts: if you like working on these kinds of problems or think this solution could help your own use case, we’d love to get in touch in the comments below.

Getting Ramped-Up on Airflow with MySQL → S3 → Redshift

CAYMAN ISLANDS, George Town, December 12th, 2024/Chainwire/--KAST a financial technology platform built on stablecoins to deliver a neobank-style experience, has raised US$10 million in seed funding. The round is led by HSG (HongShan Capital Group) and Peak XV Partners. A number of high-profile angels, partners of DST Global, and Goodwater Capital also joined the round. “For most countries and over half of global GDP, banking does not match the openness and speed of the Internet, it’s fundamentally broken. Stablecoins are the clear solution, but the user experience wasn’t great. We are building KAST to change this”, adds Raagulan Pathy, KAST’s Co-Founder. Pathy is a stablecoin expert, formerly the VP for Asia Pacific & CEO for Singapore at Circle, best known for USDC. KAST offers payment cards and USD-denominated accounts to people in over 150 countries, the ability to receive and send cash, and working with partners such as Bridge (recently acquired by Stripe). KAST will soon launch savings and other remittance products — all without the need to touch fiat directly. The maturing of stablecoin regulation and acceptance has led to rapid integration with traditional finance networks, enabling users to send money faster, cheaper, and more securely. KAST highlighted use cases such as holding USD stablecoins as an alternative to volatile currencies, enabling people to buy imports and travel with a card, and allowing remote workers globally to get paid. “The next generation of neobanks will be built on stablecoins and inherently global by DNA. They will differ fundamentally from the current generation of neobanks, which are limited in countries due to licensing, and struggle in integrating blockchain; their core systems and capabilities are just too different,” comments Daniel Bertoli, co-founder of KAST and former Partner at Quona Capital, a global FinTech fund that focuses on investments in neobanks. KAST has completed a $10 million seed funding round at an undisclosed valuation. Within its first four months, the company has demonstrated strong early traction in terms of user adoption and revenue. The team views this as a reflection of significant global interest in its platform. Stablecoins have experienced remarkable growth with over US$170 billion in supply, and US$8.5 trillion transaction volume in Q2 2024 by over 125 million users. Analysts predict by 2030; user numbers will exceed 1 billion and supply will surpass US$1 trillion. “KAST delivers real benefits to global users. Their USD-denominated accounts provide a safe haven for hard-earned income when local currencies decline, especially for those without access to traditional US accounts. As more digital nomads receive salaries in stablecoins, they can now bypass the hassle of legacy rails. International transfers that once took weeks can now be completed instantly and at virtually no cost,” said Alex Svanevik (CEO/co-founder of Nansen.ai, an early investor in KAST). Users can visit the KAST website to learn more about KAST and discover how their innovative offerings can transform financial experience. Testimonials From Card Users “I spent two weeks at the bank trying to open a simple savings account. They asked for more than 10 documents (~100 papers) and had to sign and stamp each of them. And I opened a crypto card @KAST_official in literally 2min. Why would anyone go after traditional cards anymore? If i don't need my bank statements for embassies, I would always use crypto cards like @KAST_official” - Mardeni (Twitter)“One tap to solve all my issues. That's all it took when I got my KAST card. Here's a quick recap, I've been traveling a lot lately, and exchanging money has always been a hassle. You usually end up losing 5-8% in the process by the time the cash is in your hand. A friend of mine sent me a link and said, "Give this a try!" Fast forward two months, and I've been tapping away quite a lot. Thanks to @KAST_official … I've used it across five countries over the last couple of months without any issues. It's made international spending and crypto usage incredibly simple” - Alpha Bets (Twitter)“I just used my @KAST_official card to pay for my project's domain name and it worked seamlessly all I had to do was fund it with Solana USDC, this is actually a game changer for me and any other thing I'll want to do with it” - Alex (Twitter) About KAST KAST is transforming the financial-technology landscape, by offering a seamless blend of stablecoin technology with traditional payment means. The KAST platform enables USD accounts globally to 150+ countries, reaching over 5 billion people - offering a solution where banking falls short. The silky-smooth user experience is taking mainstream the openness, speed and security of stablecoins; KAST enables users to send, receive, save, and spend funds across borders efficiently. For more information, users can visit www.kast.xyz. Contact VP Global Partnerships Bas KAST Bas@kast.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here CAYMAN ISLANDS, George Town, December 12th, 2024/Chainwire/--KAST a financial technology platform built on stablecoins to deliver a neobank-style experience, has raised US$10 million in seed funding. The round is led by HSG (HongShan Capital Group) and Peak XV Partners. A number of high-profile angels, partners of DST Global, and Goodwater Capital also joined the round. “For most countries and over half of global GDP, banking does not match the openness and speed of the Internet, it’s fundamentally broken. Stablecoins are the clear solution, but the user experience wasn’t great. We are building KAST to change this”, adds Raagulan Pathy, KAST’s Co-Founder. “For most countries and over half of global GDP, banking does not match the openness and speed of the Internet, it’s fundamentally broken. Stablecoins are the clear solution, but the user experience wasn’t great. We are building KAST to change this”, adds Raagulan Pathy, KAST’s Co-Founder. Pathy is a stablecoin expert, formerly the VP for Asia Pacific & CEO for Singapore at Circle, best known for USDC. KAST offers payment cards and USD-denominated accounts to people in over 150 countries, the ability to receive and send cash, and working with partners such as Bridge (recently acquired by Stripe). KAST will soon launch savings and other remittance products — all without the need to touch fiat directly. The maturing of stablecoin regulation and acceptance has led to rapid integration with traditional finance networks, enabling users to send money faster, cheaper, and more securely. KAST highlighted use cases such as holding USD stablecoins as an alternative to volatile currencies, enabling people to buy imports and travel with a card, and allowing remote workers globally to get paid. “The next generation of neobanks will be built on stablecoins and inherently global by DNA. They will differ fundamentally from the current generation of neobanks, which are limited in countries due to licensing, and struggle in integrating blockchain; their core systems and capabilities are just too different,” comments Daniel Bertoli, co-founder of KAST and former Partner at Quona Capital, a global FinTech fund that focuses on investments in neobanks. “The next generation of neobanks will be built on stablecoins and inherently global by DNA. They will differ fundamentally from the current generation of neobanks, which are limited in countries due to licensing, and struggle in integrating blockchain; their core systems and capabilities are just too different,” comments Daniel Bertoli, co-founder of KAST and former Partner at Quona Capital, a global FinTech fund that focuses on investments in neobanks. KAST has completed a $10 million seed funding round at an undisclosed valuation. Within its first four months, the company has demonstrated strong early traction in terms of user adoption and revenue. The team views this as a reflection of significant global interest in its platform. Stablecoins have experienced remarkable growth with over US$170 billion in supply, and US$8.5 trillion transaction volume in Q2 2024 by over 125 million users. Analysts predict by 2030; user numbers will exceed 1 billion and supply will surpass US$1 trillion. growth growth Analysts predict Analysts predict “KAST delivers real benefits to global users. Their USD-denominated accounts provide a safe haven for hard-earned income when local currencies decline, especially for those without access to traditional US accounts. As more digital nomads receive salaries in stablecoins, they can now bypass the hassle of legacy rails. International transfers that once took weeks can now be completed instantly and at virtually no cost,” said Alex Svanevik (CEO/co-founder of Nansen.ai, an early investor in KAST). “KAST delivers real benefits to global users. Their USD-denominated accounts provide a safe haven for hard-earned income when local currencies decline, especially for those without access to traditional US accounts. As more digital nomads receive salaries in stablecoins, they can now bypass the hassle of legacy rails. International transfers that once took weeks can now be completed instantly and at virtually no cost,” said Alex Svanevik (CEO/co-founder of Nansen.ai, an early investor in KAST). Users can visit the KAST website to learn more about KAST and discover how their innovative offerings can transform financial experience. website website Testimonials From Card Users “I spent two weeks at the bank trying to open a simple savings account. They asked for more than 10 documents (~100 papers) and had to sign and stamp each of them. And I opened a crypto card @KAST_official in literally 2min. Why would anyone go after traditional cards anymore? If i don't need my bank statements for embassies, I would always use crypto cards like @KAST_official” - Mardeni ( Twitter )“One tap to solve all my issues. That's all it took when I got my KAST card. Here's a quick recap, I've been traveling a lot lately, and exchanging money has always been a hassle. You usually end up losing 5-8% in the process by the time the cash is in your hand. Twitter Twitter A friend of mine sent me a link and said, "Give this a try!" Fast forward two months, and I've been tapping away quite a lot. Thanks to @KAST_official … I've used it across five countries over the last couple of months without any issues. It's made international spending and crypto usage incredibly simple” - Alpha Bets ( Twitter )“I just used my @KAST_official card to pay for my project's domain name and it worked seamlessly all I had to do was fund it with Solana USDC, this is actually a game changer for me and any other thing I'll want to do with it” - Alex ( Twitter ) Twitter Twitter Twitter Twitter About KAST KAST is transforming the financial-technology landscape, by offering a seamless blend of stablecoin technology with traditional payment means. The KAST platform enables USD accounts globally to 150+ countries, reaching over 5 billion people - offering a solution where banking falls short. KAST KAST The silky-smooth user experience is taking mainstream the openness, speed and security of stablecoins; KAST enables users to send, receive, save, and spend funds across borders efficiently. For more information, users can visit www.kast.xyz . www.kast.xyz www.kast.xyz Contact VP Global Partnerships Bas KAST Bas@kast.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

KAST Secures US$10 Million Seed Round Led By HSG (HongShan Capital Group) And Peak XV Partners

KAST ukax 10 millones de dólares ukjam jatha muyuntatw HSG (HongShan Capital Group) ukat Peak XV Partners ukanakan p’iqinchata

KAST obtiene una ronda de financiación inicial de 10 millones de dólares liderada por HSG (HongShan Capital Group) y Peak XV Partners

KAST、HSG（HongShan Capital Group）とPeak XV Partnersが主導する1,000万ドルのシードラウンドを確保

KAST нь HSG (HongShan Capital Group) болон Peak XV түншүүдээр удирдуулсан 10 сая долларын үрийг баталгаажуулав.

KAST Mendapat Pusingan Benih AS$10 Juta Diketuai Oleh HSG (HongShan Capital Group) Dan Rakan Kongsi Peak XV

KAST получает начальный раунд финансирования в размере 10 миллионов долларов США под руководством HSG (HongShan Capital Group) и Peak XV Partners

KAST zabezpečuje počiatočné kolo v hodnote 10 miliónov USD vedené HSG (HongShan Capital Group) a partnermi Peak XV

КАСТ обезбеђује 10 милиона долара за почетак рунде предвођени ХСГ (ХонгСхан Цапитал Гроуп) и Пеак КСВ партнерима

HSG (ஹாங்ஷான் கேபிடல் குரூப்) மற்றும் பீக் XV பார்ட்னர்கள் தலைமையிலான KAST US$10 மில்லியன் விதை சுற்று

KAST, HSG (HongShan Capital Group) ve Peak XV Partners Liderliğinde 10 Milyon ABD Doları Değerinde Tohum Turu Elde Etti

KAST забезпечує стартовий раунд у розмірі 10 мільйонів доларів США під керівництвом HSG (HongShan Capital Group) і партнерів Peak XV

KAST đảm bảo vòng hạt giống trị giá 10 triệu đô la Mỹ do HSG (HongShan Capital Group) và Peak XV Partners dẫn đầu

LONDON, united kingdom, December 10th, 2024/Chainwire/--Pepeto is carving its niche in the cryptocurrency landscape by blending an engaging narrative with practical utility. Inspired by the concept of six iconic documents—P, E, P, E, T, and O—the project has built a strong following, with active engagement across its social media platforms. Beyond its story, Pepeto is positioned as a central hub for frog-themed and memecoin projects. With features enabling seamless trading, bridging, and token listing, the platform aims to support the next wave of innovative projects anticipated in the upcoming market cycle. Dynamic Staking Rewards System: Pepeto APY Now at 709% Pepeto’s staking program offers competitive rewards, with the current annual percentage yield (APY) at 709%. This rate reflects the dynamic nature of the system, which adjusts rewards based on the number of participants staking $PEPETO tokens. While the rate has decreased with increased participation, it remains competitive, demonstrating the platform’s commitment to incentivizing early adopters. Engineered for Security, Transparency, and Industry Leadership Pepeto’s zero-fee exchange is built to set a new benchmark in how memecoins are traded and adopted. By offering a streamlined platform, the exchange allows token holders to list their projects with ease via a straightforward submission process on the official website. This initiative supports the growth of emerging tokens, offering them a platform to thrive. With an emphasis on accessibility, innovation, and value, Pepeto’s exchange plays a pivotal role in enabling visibility and liquidity for memecoin projects, positioning itself as a vital component of the anticipated 2025 memecoin market surge. What sets Pepeto apart is not just its narrative or staking rewards but its foundation of robust industry practices. Built on the Ethereum blockchain, the project features carefully designed tokenomics, competitive staking incentives, and a long-term growth strategy. This meticulous approach highlights Pepeto’s commitment to fostering sustainability and supporting a thriving ecosystem. https://www.youtube.com/embed/qXZWPZDkScw https://www.youtube.com/watch?v=qXZWPZDkScw Pepeto’s commitment to security and transparency is evident, having successfully passed rigorous audits by SolidProof and Coinsult. These checks ensure the project offers top-tier security and includes safeguards against potential risks like rug-pulls. Additionally, Pepeto’s tokenomics are thoughtfully structured to balance supply, enhance market liquidity, and allocate 30% of $PEPETO tokens for staking rewards after its official launch, solidifying its position as a sustainable and investor-focused project. PepetoSwap: Advancing Cross-Chain Token Interoperability Pepeto is preparing to introduce PepetoSwap, a utility designed to facilitate token interoperability across blockchains. This platform includes a bridge for cross-chain token swaps and an exchange that supports a broad range of tokens. By enabling seamless transactions and fostering accessibility, PepetoSwap aims to provide practical tools for users and token projects in the growing crypto ecosystem. The bridge utilizes advanced technology to lock tokens on one blockchain and mint equivalent wrapped tokens on another, enabling interoperability while maintaining security. Meanwhile, the Pepeto exchange empowers token owners to list their projects directly via the bridge section of the official website, fostering a collaborative and expansive ecosystem for memecoins and other tokens alike. About Pepeto Pepeto is a memecoin project designed to integrate cross-chain utility with community-driven development. Offering zero-fee trading, blockchain bridge functionality, and a staking rewards program, Pepeto seeks to combine accessibility with practical features. The project emphasizes interoperability and long-term value, fostering a dedicated user base through its ecosystem innovations and community-focused approach. Disclaimer The official website for Pepeto is https://pepeto.io/ Be cautious of fraudulent websites attempting to exploit the project’s growing buzz. Always use the official platform. To learn more about Pepeto’s progress and upcoming features, users can visit https://pepeto.io/ Official Website: https://pepeto.io/ Social Media: X (Twitter): https://x.com/Pepetocoin
YouTube Channel: https://www.youtube.com/@Pepetocoin
Telegram Channel: https://t.me/pepeto_channel
Instagram: https://www.instagram.com/pepetocoin/
TikTok: https://www.tiktok.com/@pepetocoin Contact Pepeto team contact@pepeto.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here LONDON, united kingdom, December 10th, 2024/Chainwire/--Pepeto is carving its niche in the cryptocurrency landscape by blending an engaging narrative with practical utility. Inspired by the concept of six iconic documents—P, E, P, E, T, and O—the project has built a strong following, with active engagement across its social media platforms. Beyond its story, Pepeto is positioned as a central hub for frog-themed and memecoin projects. With features enabling seamless trading, bridging, and token listing, the platform aims to support the next wave of innovative projects anticipated in the upcoming market cycle. Dynamic Staking Rewards System: Pepeto APY Now at 709% Pepeto’s staking program offers competitive rewards, with the current annual percentage yield (APY) at 709%. This rate reflects the dynamic nature of the system, which adjusts rewards based on the number of participants staking $PEPETO tokens. While the rate has decreased with increased participation, it remains competitive, demonstrating the platform’s commitment to incentivizing early adopters. Engineered for Security, Transparency, and Industry Leadership Pepeto’s zero-fee exchange is built to set a new benchmark in how memecoins are traded and adopted. By offering a streamlined platform, the exchange allows token holders to list their projects with ease via a straightforward submission process on the official website. This initiative supports the growth of emerging tokens, offering them a platform to thrive. With an emphasis on accessibility, innovation, and value, Pepeto’s exchange plays a pivotal role in enabling visibility and liquidity for memecoin projects, positioning itself as a vital component of the anticipated 2025 memecoin market surge. What sets Pepeto apart is not just its narrative or staking rewards but its foundation of robust industry practices. Built on the Ethereum blockchain, the project features carefully designed tokenomics, competitive staking incentives, and a long-term growth strategy. This meticulous approach highlights Pepeto’s commitment to fostering sustainability and supporting a thriving ecosystem. https://www.youtube.com/embed/qXZWPZDkScw https://www.youtube.com/embed/qXZWPZDkScw https://www.youtube.com/embed/qXZWPZDkScw https://www.youtube.com/watch?v=qXZWPZDkScw https://www.youtube.com/watch?v=qXZWPZDkScw https://www.youtube.com/watch?v=qXZWPZDkScw Pepeto’s commitment to security and transparency is evident, having successfully passed rigorous audits by SolidProof and Coinsult . These checks ensure the project offers top-tier security and includes safeguards against potential risks like rug-pulls. SolidProof SolidProof Coinsult Coinsult Additionally, Pepeto’s tokenomics are thoughtfully structured to balance supply, enhance market liquidity, and allocate 30% of $PEPETO tokens for staking rewards after its official launch, solidifying its position as a sustainable and investor-focused project. PepetoSwap: Advancing Cross-Chain Token Interoperability Pepeto is preparing to introduce PepetoSwap, a utility designed to facilitate token interoperability across blockchains. This platform includes a bridge for cross-chain token swaps and an exchange that supports a broad range of tokens. By enabling seamless transactions and fostering accessibility, PepetoSwap aims to provide practical tools for users and token projects in the growing crypto ecosystem. The bridge utilizes advanced technology to lock tokens on one blockchain and mint equivalent wrapped tokens on another, enabling interoperability while maintaining security. Meanwhile, the Pepeto exchange empowers token owners to list their projects directly via the bridge section of the official website, fostering a collaborative and expansive ecosystem for memecoins and other tokens alike. About Pepeto Pepeto is a memecoin project designed to integrate cross-chain utility with community-driven development. Offering zero-fee trading, blockchain bridge functionality, and a staking rewards program, Pepeto seeks to combine accessibility with practical features. Pepeto Pepeto Pepeto Pepeto The project emphasizes interoperability and long-term value, fostering a dedicated user base through its ecosystem innovations and community-focused approach. Disclaimer The official website for Pepeto is https://pepeto.io/ Be cautious of fraudulent websites attempting to exploit the project’s growing buzz. Always use the official platform. https://pepeto.io/ https://pepeto.io/ To learn more about Pepeto’s progress and upcoming features, users can visit https://pepeto.io/ https://pepeto.io/ https://pepeto.io/ Official Website: https://pepeto.io/ https://pepeto.io/ https://pepeto.io/ Social Media: X (Twitter): https://x.com/Pepetocoin YouTube Channel: https://www.youtube.com/@Pepetocoin Telegram Channel: https://t.me/pepeto_channel Instagram: https://www.instagram.com/pepetocoin/ TikTok: https://www.tiktok.com/@pepetocoin X (Twitter): https://x.com/Pepetocoin https://x.com/Pepetocoin https://x.com/Pepetocoin YouTube Channel: https://www.youtube.com/@Pepetocoin https://www.youtube.com/@Pepetocoin https://www.youtube.com/@Pepetocoin Telegram Channel: https://t.me/pepeto_channel https://t.me/pepeto_channel https://t.me/pepeto_channel Instagram: https://www.instagram.com/pepetocoin/ https://www.instagram.com/pepetocoin/ https://www.instagram.com/pepetocoin/ TikTok: https://www.tiktok.com/@pepetocoin https://www.tiktok.com/@pepetocoin https://www.tiktok.com/@pepetocoin Contact Pepeto team contact@pepeto.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Pepeto ICO Hits $2 Million Milestone, Showcasing Strong Community Support

Pepeto ICO, Güclü İcma Dəstəyini nümayiş etdirərək 2 Milyon dollarlıq mərhələni vurdu

Pepeto ICO dostigao prekretnicu od 2 miliona dolara, pokazujući snažnu podršku zajednice

La ICO de Pepeto alcanza el hito de los 2 millones de dólares, lo que demuestra un fuerte apoyo de la comunidad

Pepeto ICO به نقطه عطف 2 میلیون دلاری رسید و پشتیبانی قوی از جامعه را به نمایش گذاشت

Pepeto ICO が 200 万ドルのマイルストーンを達成、強力なコミュニティのサポートを実証

Pepeto ICO ឈានដល់ចំណុចសំខាន់ 2 លានដុល្លារដែលបង្ហាញពីការគាំទ្រសហគមន៍ដ៏រឹងមាំ

Pepeto ICO, 200만 달러 이정표 달성, 강력한 커뮤니티 지원 선보여

Пепето ICO нь 2 сая долларын амжилтыг үзүүлж, олон нийтийн хүчтэй дэмжлэгийг харуулж байна

Pepeto ICO د 2 ملیون ډالرو هدف وټاکه، د ټولنې قوي ملاتړ څرګندوي

Пепето ИЦО достигао прекретницу од 2 милиона долара, показујући снажну подршку заједнице

Pepeto ICO güýçli jemgyýetçilik goldawyny görkezip, 2 million dollarlyk üstünlik gazandy

Pepeto ICO 达到 200 万美元里程碑，展现出强大的社区支持

DUBAI, United Arab Emirates, December 10th, 2024/Chainwire/--DWF Labs, the new generation crypto market maker and investor, announced the launch of a $20 million fund dedicated to supporting the development of autonomous AI agents. This initiative underscores DWF Labs’s commitment to unlock the next generation of artificial intelligence and crypto technology. The new fund aims to support Web3 projects building next-generation AI agent solutions that have the potential to transform industries and redefine the digital economy. Alongside financial backing, recipients will receive comprehensive support to accelerate their growth and adoption. Eligible projects can also receive up to $100,000 of cloud server credits to help optimise performance and scale their infrastructure seamlessly. Additionally, these crypto projects can access strategic advisory services and collaboration opportunities with leading blockchain ecosystems.These partnerships are designed to facilitate the integration of AI-driven applications within decentralised networks. “Autonomous AI agents will transform how businesses and individuals interact with technology, from automating complex decision-making processes to unlocking entirely new economic opportunities,” said Andrei Grachev, Managing Partner at DWF Labs. “Through this fund, we aim to empower builders and accelerate the innovation of AI and decentralised technologies.” Eligible projects will be assessed on their potential to drive innovation and create meaningful impact across industries such as finance, logistics, entertainment, and governance. This initiative reflects DWF Labs’s commitment in supporting cutting-edge technologies to drive adoption and innovation within the Web3 ecosystem. By enabling projects that leverage the intersection of artificial intelligence and decentralised systems, this fund aims to unlock the next generation of AI innovation. The $20 million AI Agent Fund is now open for applications. Apply now: https://ww.dwf-labs.com/ai-agent-fund About DWF Labs DWF Labs is the new generation Web3 investor and market maker, one of the world's largest high-frequency cryptocurrency trading entities, which trades spot and derivatives markets on over 60 top exchanges. Contact Head of Communications Lynn Chia pr@dwf-labs.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here DUBAI, United Arab Emirates, December 10th, 2024/Chainwire/--DWF Labs, the new generation crypto market maker and investor, announced the launch of a $20 million fund dedicated to supporting the development of autonomous AI agents. This initiative underscores DWF Labs’s commitment to unlock the next generation of artificial intelligence and crypto technology. The new fund aims to support Web3 projects building next-generation AI agent solutions that have the potential to transform industries and redefine the digital economy. Alongside financial backing, recipients will receive comprehensive support to accelerate their growth and adoption. Eligible projects can also receive up to $100,000 of cloud server credits to help optimise performance and scale their infrastructure seamlessly. Additionally, these crypto projects can access strategic advisory services and collaboration opportunities with leading blockchain ecosystems.These partnerships are designed to facilitate the integration of AI-driven applications within decentralised networks. “Autonomous AI agents will transform how businesses and individuals interact with technology, from automating complex decision-making processes to unlocking entirely new economic opportunities,” said Andrei Grachev, Managing Partner at DWF Labs. “Autonomous AI agents will transform how businesses and individuals interact with technology, from automating complex decision-making processes to unlocking entirely new economic opportunities,” said Andrei Grachev, Managing Partner at DWF Labs. “Through this fund, we aim to empower builders and accelerate the innovation of AI and decentralised technologies.” “Through this fund, we aim to empower builders and accelerate the innovation of AI and decentralised technologies.” Eligible projects will be assessed on their potential to drive innovation and create meaningful impact across industries such as finance, logistics, entertainment, and governance. This initiative reflects DWF Labs’s commitment in supporting cutting-edge technologies to drive adoption and innovation within the Web3 ecosystem. By enabling projects that leverage the intersection of artificial intelligence and decentralised systems, this fund aims to unlock the next generation of AI innovation. The $20 million AI Agent Fund is now open for applications. Apply now: https://ww.dwf-labs.com/ai-agent-fund https://ww.dwf-labs.com/ai-agent-fund https://ww.dwf-labs.com/ai-agent-fund About DWF Labs DWF Labs is the new generation Web3 investor and market maker, one of the world's largest high-frequency cryptocurrency trading entities, which trades spot and derivatives markets on over 60 top exchanges. Contact Head of Communications Lynn Chia pr@dwf-labs.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

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