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Metabase is bringing data tools with the elegance and simplicity of consumer products to the crufty world of enterprise business intelligence. Our Open Source analytics and business intelligence application let installs in minutes, and can connect to most commonly used databases. It lets anyone in your company quickly ask questions, and create dashboards or nightly emails without knowing SQL. We provide an opinionated open source starting point for how companies should measure, analyze and share their data as well as a suite of tools to deal with the complexity that arises as they grow.

Metabase

metabase

SAN FRANCISCO--(BUSINESS WIRE)--Metabase Q, a leading all-in-one cybersecurity solution, today announced it has joined the World Economic Forum’s (WEF) Global Innovators Community. The Global ...

Metabase Q Joins World Economic Forum Global Innovators Community

Metabase & Substrate: A Simple and Flexible, Yet Unscalable, Approach to Data Analysis

“Data is the new oil”. We have all heard this adage but does it fit in the startup ecosystem? The statement might be true for large enterprises which have multiple verticals and many departments looking at metadata or transactional level information to make sense of it, but what about a growing startup? Is Metabase the answer? It’s wise to start early and bloom according to the customer or market requirements when they come. As mentioned earlier, large enterprises might have data engineers setting up pipelines, and dedicated personnel preparing dashboards and presentations using the data collated from the multiple divisions of the organization, but a startup has limited resources in terms of man-hours and funds. This is where our trusty friend Metabase comes in. Metabase is a business intelligence tool that lets you access your data in a read-only manner. The only setup required is getting the docker up and running, then following the steps to add your database. No engineers or extra licensing costs are involved. Let’s dig deeper (for the “oil”) We at Decentro have a central RDBMS database containing transactional data. This makes querying the data easier and extracting information a piece of cake. Metabase sits on a separate instance and has read-only access to this database. Components of Metabase Metabase has 4 important components – – Written and saved queries that can be altered using the query editor or the custom question (used to visually provide filters and grouping options) Questions – A set of questions makes a collection. This binding of questions makes them easily manageable. Collections – The users of Metabase. People – Collections, but for people Groups Metabase allows to be self-onboarded using Google SSO which works very well for us at Decentro. Once a person is onboarded we assign them a , and the group in turn defines the accesses to the tables and that they will have. The permissions can be to simply view in a collection or curate them as well. Groups have policies to manage the access to collections and questions which are managed by the admins very stringently. people group collections questions Based on this framework, we have internally divided people into 3 groups Operations and Sales Product Developers team requires access to dashboards and questions that deal with customer success. Issues like Success rate dipping below a certain threshold, transaction-related issues, and customer billing. Similarly, the Sales team can check how their clients are doing using the data from Metabase. The Operations team needs to have a look at turnaround times, success metrics, and data anomalies. All of these fit in very well with the Metabase way of doing business. It helps the team to preemptively check for issues or inefficiencies in the APIs that our customers use. The Product are provided access to Metabase when they are working with the operations team to debug issues on production servers or to do configuration checks or root cause and corrective analyses (RCCA). Since developers work with SQL day in and day out, they can write queries to solve these problems and have a window to the production database. Developers As you can see the efficiency of the whole Decentro team is benefitted from Metabase. Why is Metabase awesome? Now that we have seen how to tap into efficiency, let’s deep dive into the why. Visualizations. Boom. Just, effortless visualizations. I just fetched some information from the database but I want to make a graph with it. Scenario: : A few clicks in the visualization tab and it’s done. Solution No pesky configuration options. Just a clean interface. Let’s take a closer look at a real-time scenario for better visualization. Below I have a query to fetch Transactions per second for one of the servers. This data represents the traffic on that server and is originally received in tabular format. But tables are not very intuitive. However, charts are. So I click on the visualization tab and switch my mode from data to visualization, and voila! Bar graph Now, this looks cool and intuitive. I have chosen a bar graph to analyze the TPS data more efficiently. But what if I wanted to further customize it to my liking? There are options such as setting a goal line if you want to set targets, changing colors (not shown in the screenshot below), showing a trend line, and many more, for each chart type. All the customization is focused on incorporating efficiency, and a level of intuitiveness with each chart. Stacking option Axes configuration Final Visualisation As you can see, my chart now looks good enough for our team to consume, and infer information from. Conclusion Metabase is a very important tool in the arsenal of a growing startup. Its impact might be low in the beginning, but as time passes on it becomes more and more valuable. Data-driven product and engineering decisions are what empower Decentro to understand customer pain points and work on them without the customers conveying them verbally, and Metabase is the silent aid for such decisions. In case it spikes your interest, our engineering team at Decentro has penned more tech blogs to satisfy the developer within you. Be it why we moved to , or a pocket guide on . Kong API Gateway from NGINX API Request Validation We’ll see you next time with yet another tech story! Also published . here

How to Build a Data-Driven Product Using Metabase

In the " " story, I describe the approach enhancing your debugging possibilities. On the one hand, I decided that adding description would add misleading details to the narrative. On the other hand, real-life applying might be an excellent opportunity to demonstrate method flexibility. 3 Sharpest Instruments for Debugging a meticulous coding This contradiction led to emerging of this auxiliary story. What Is Going on? On GitHub, there is a project called . I discovered it by searching through the trending repositories. I narrowed down my search by filtering to only those repositories that use the Clojure language I like. My specialization is mostly front-end, so I've chosen that might not be of considerable interest to other developers. I acted so as not to discover that someone passed over me with fixing it. After some investigation described in the primary article about fixing, I found out that the bug exists in the component. Metabase UI issue #14782 AccordionList Diving Into the Details Here's how the initial checklist would look: 1. Understand the actions required to implement task #14782
2. Write the commit messages for task #14782
3. Create a branch for task #14782
4.
5. Verify the correspondence of the implemented solution to task #14782
6. Create task #14782 PR
7. Pass the review of task #14782
8. Merge task #14782 into `master` In , there were 11 steps in the checklist. We do not need three of the administrative tasks, so I removed them. Also, I rewrote the last one to reflect the aspects of the situation. the original article about meticulous coding Let's act on the first task here and understand what do we need to do: 2. ...
3. Create a branch for task #14782
4. Prevent `AccordionList` from jumping on inner search value change task #14782
5. Verify the correspondence of the implemented solution to task #14782
6. ... The second task asks to write the commit messages. I didn't find any recommendations about the formatting of commit messages, so here is mine: Prevent Saved Questions search input from jumping The third task is about creating the branch. I also didn't find any recommendations on branch naming, so here we go: fix/14782-prevent-saved-questions-search-input-from-jumping Now it's time to solve the problem. The full description of discovery and solution is in the dedicated "Three Sharpest Instruments on Your Debugging Service" story. While trying to prepare the necessary state of the component, I discovered that the problem might no longer exist. I need to verify this. AccordionList 3. ...
4. Ask if task #14782 is still relevant after the "Add saved questions picker" update
5. Wait for someone's reply to my question about the relevance of task #14782
6. Prevent `AccordionList` from jumping on inner search value change task #14782
7. Verify the correspondence of the implemented solution to task #14782
8. ... I've shifted all the undone tasks by adding two on the fourth position. The fifth might sound unusual. I learned such a type of task by reading " " by David Allen. You set something reminding you about liabilities others have taken. By doing so, you clearly state the next step of the process and know the action required to unblock it. Or at least to try doing so. Getting Things Done It turned out that, indeed removed the problem I was tackling. As I mentioned before, the meticulous coding method is flexible. We delete all the tasks from the sixth, add another verificatory one and end up with the following completed checklist: the PR #16808 ☑ Understand the actions required to implement task #14782
☑ Write the commit messages for task #14782
☑ Create a branch for task #14782
☑ Ask if task #14782 is still relevant after the "Add saved questions picker" update
☑ Wait for someone's reply to my question about the relevance of task #14782
☑ Ensure the closing of task #14782 Thank you for reading! Please reach out if you want to discuss the ideas I describe. on Twitter

The Meticulous Coding Strategy Applied to Metabase Issue #14782

Metabase is a business intelligence tool for your organisation that plugs in various data-sources so you can explore data and build dashboards. I'll aim to provide a series of articles on provisioning and building this out for your organisation.  This article is about getting up and running quickly. There are multiple ways to get Metabase up and running on Google Cloud. https://www.metabase.com/docs/latest/operations-guide/installing-metabase.html You can install it locally on your Mac as well as popular cloud platforms. It's available as a JAR file and also as a Docker container. I went via the Docker container route and Google Cloud Platform. Step 1 - GCloud Developer Tools Setup Make sure you have GCloud developer tools setup. You can do this via the instructions . here After that, you'll need to login to your Google account and also configure Docker to work with your credentials.  In my case I generated a new service account in GCP to access Google BigQuery (my chosen reporting database).  I then downloaded the corresponding service account JSON file. gcloud auth login gcloud auth configure-docker Step 2 - Deploying Metabase to GCP Metabase docker images are on Dockerhub here. https://hub.docker.com/r/metabase/metabase/tags However, I wanted these in Google Cloud Registry for performance and reliability reasons. You can pull the Docker image using the following: docker pull metabase/metabase:latest Tag the docker image with the following: hostname — eu.gcr.io projectname — krustysfunhouse tag — metabase docker tag metabase/metabase:latest eu.gcr.io/krustysfunhouse/metabase Deploy the Docker image to Google Cloud Registry with the following docker push eu.gcr.io/krustysfunhouse/metabase:latest Step 3 - Compute Engine Instance Create a Compute Engine instance: - Specify the GCR image you've just uploaded: eu.gcr.io/krustysfunhouse/metabase:latest Specify vCPU and Memory (I used 1 vCPU and 3.75GB) Specify the region, I've used London (europe-west2-c) here. n.b. You'll have a by default. container optimized OS Step 4 - Getting Access Once that instance is provisioned you can login from gcloud via the console with the following: gcloud beta compute ssh --zone "europe-west2-c" "metabasereporting" --project "krustysfunhouse" Metabase runs as a Java app and starts-up on port 3000. If you want to get into installing Nginx or GCP Load Balancing that’s a different story. For this, I opened up access to port 3000. Add a new Firewall Rule to allow traffic to access tcp:3000 for now — example below. Then add this “name” to the machine VM instance as a network tag. You should lock this down to your office IP ideally, or setup a VPN so that you access this securely.  Once running, you will have a publically accessible instance that is subject to brute-force attack in terms of username/password. By default, Metabase will make you specify a default email and password, but still this isn't production ready. I'll look at the basics of building a dashboard in the next article, but hopefully that should be good enough to get you started. I'm a Consulting CTO working as an architect and engineer with startups and scaleups.  A large part of my career was spent working in analytics companies and data-heavy companies often with the aim of building/transforming data-platforms.  You can find out more about me (and hire me for advice, fractional or interim work) here: https://www.hwintegral.com/ That's all for now...

How To Deploy Metabase on Google Cloud Platform (GCP)?

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How to Chat With Your Data Using OpenAI, Pinecone, Airbyte and Langchain: A Guide

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53 Stories To Learn About Enterprise Software

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Top 8 Best Qlik Sense Extensions Qlik Sense is powerful data visualization and BI software. But sometimes its functions are not enough. Meet the best Qlik Sense extensions to do more with data! 38. Deep Dive Into Open Source BI Tool Helical Insight When Helical Insight first announced a couple of years ago that they were releasing an Open Source Business Intelligence (BI) tool, it really caught my interest and I reached out to founder Nikhilesh Tiwari to find out more about what he was doing. I spent a little time with the product and really liked where it was going and was determined to do more of a deep dive in the future, and with their release of version 3.0, that time is now. 39. Opportunities And Challenges of Integrating Power Business Intelligence Powerful, affordable, effective—these are just three of the terms used to describe Microsoft’s Power BI data visualization platform. Its status as a world-leading business intelligence tool supports all the positive hype to be found across the IT media world. 40. Hacking The Psychology of Business Names What's in a name? It's important to figure out how to pick the best name for your business. 41. Embedded data analytics and reporting tools that empowers Business analysts Embedded data analytics and reporting tools that empowers Business analysts 42. An Internal Email to Tim Cook and the State of Business Intelligence We get a glimpse into the inner workings of a valuable company and it turns out it's not all sunshine and rainbows. 43. British Cybersecurity Firm Darktrace Goes Public Darktrace affirmed on Monday that it intended to float on the London Stock Exchange to raise new funds and for the acceleration of product development. 44. 4 of the Best Ways to Use AI in Business Artificial Intelligence (AI) has created a lot of disruption in the business world. 45. From Raw Data to Actionable Insights: The Power of Data Aggregation This article examines data aggregation processes: collecting data to present it in summary form. 46. Optimize Power BI Reporting and Designing As a data analysis tool, Power BI comes loaded with plenty of report generation and design features. However, do not rely on the default settings of the tool. 47. 5 Steps to Master Customer Intelligence in 2021 Customer intelligence is the process of gathering and analyzing customer data. 48. Translating Data into Actionable Insights: Development of Data Studio Connector for FreshSales Deals Startups lack resources. They start small, think big and wish to scale fast. At the same time, they expect data and analytics to lie at the heart of their decision-making process. 49. 5 DBT Repositories You Need to Star on GitHub The 5 hottest dbt Repositories you should star on Github 2022 - Those are mine! 50. Grafana Vs. Kibana Vs. Knowi: Battle Royale 2020 Intro: Grafana vs Kibana vs Knowi 51. How To Choose The Right Business Process Automation Technology This article will give you some insight into what to look for when selecting business process automation software to enhance your productivity. 52. Uber to Netflix: The Basics of Billion Dollar Business Models Do you know what distinguishes Billion-dollar companies like Uber, Amazon, Netflix, Airbnb, etc. from other companies? Their effectively created & implemented business models. Business models that are flexible. Revenue generation models that react to the varying needs of customers. 53. The Role of Machine Learning in Data Cleaning There are stages in the data cleansing process where machine learning can not only automate workflows, but influence decisions, and achieve better results. 54. 5 Things to Watch Out for When Implementing Tableau BI Has your organization decided to adopt and implement the Tableau BI platform, namely its Tableau Server and Tableau Online versions? 55. 20 Herramientas de Inteligencia Empresarial (BI) más Populares en 2020 Business Intelligence (BI) es un negocio basado en datos, un proceso de toma de decisiones basado en datos recopilados. A menudo es utilizado por gerentes y ejecutivos para generar ideas procesables. Como resultado, BI siempre se conoce indistintamente como "Business Analytics" o "Data Analytics". 56. IoT Trends in 2021: Driving Innovation in Businesses Here are the latest IoT trends and key predictions of how the Internet of Things will drive digital transformation of business in 2021. 57. Breaking into Deep Learning: Transforming the World Without Expert Input Deep learning is a subdivision of machine learning in which Artificial Neural Networks (ANNs) learn from a huge influx of data to produce high-quality output. 58. 8 Tools To Improve Business Reporting Based on the best reporting tools criteria, I selected eight top reporting software, arranging from free and open-source to commercial, specialized in different 59. The United Kingdom's New National Data Strategy (Summary and Analysis) This Article is a Summary for the official National Data Strategy release here 60. Top Tableau Consulting Companies on the Market in 2020 Business intelligence has become an indispensable part of successful businesses, and the sooner executives recognize data as a crucial component of decision-making, the sooner they will be able to improve their operational processes. 61. Top 30 Business Intelligence Consulting Companies In the information age, data is a vital source for everything: from housekeeping to business management, people need to stay tuned in order to survive. But the tricky thing is that the amount of data available both offline and online is so huge it cannot be traced manually anymore, and it continues to grow. According to Raconteur, each day we publish 500 million tweets, send 294 billion emails, and make five billion search requests. The amount of data generated daily is expected to reach 463 exabytes by 2025. The numbers are mind-blowing, and the first question to arise is: how do we process all this data? 62. BI Analyst Interview Questions And Answers: 2020 Edition Why you should prepare for BI analyst interview questions? 63. Can Machine Learning Predict Loan Defaults? Visualize Insights and Discover Driving Features in Lending Credit Risk Model for Loan Defaults Thank you for checking out the 63 most read stories about Business Intelligence on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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If you have been in the tech industry for a while you may have heard about open source or open source software. When we talk about open source, you should think of open collaboration, open exchange, participation transparency and community-oriented development. In this article, you will learn more about open source software including the difference between open source and other forms of software, key features of OSS, pros and cons of open source, how to start contributing to open source, and some examples of OSS. What is Open Source? Open source is any program whose source code can be modified and shared by anyone because it’s openly accessible. What is Source Code? A source code is a set of instructions written in the form of functions, descriptions, methods, calls or any other operational statement by a programmer using a computer programming language. A source code can be open that is available to anyone to edit and redistribute or closed. Source code is the part that most software or computer users cannot see. What Does Open Source Software(OSS) Mean? The term Open source software is a non-proprietary software in which code is publicly available to anyone to inspect, comment on, modify, improve and redistribute it. Open source software is created and maintained by a group of developers so if you are new to computer programming, you can begin your journey as an open source contributor. I have known open source since 2017 and started with my first contribution in 2020. I made my first contributions to the Gihhub and . Apache Software Foundation Mozilla mobile Github What is the Difference between Open source Software and other Types of Software? Do not get confused with open source, free, closed software and freeware. This section highlights the differences between these types of Software. Open Source vs Free Software Open source software is software available for anyone to edit, improve and redistribute. The source code is accessible to anyone. Open source promotes collaboration and sharing of source code. It’s an effective way for organizations, developers and individuals to collaborate. Freedom to use the software is not absolute or imposed but it’s allowed. Every open source software is not free. However, some open source licenses are restrictive. is an example of open source software WordPress Free Software describes software in which source code is free to use, edit and redistribute without any restrictions. With free software, Freedom is a value that is more important than any other thing. All free software is open source software. Ares is an example Open source and free software have distinguished features. Some open source software is restrictive and all free software are open source software but not all open source software is free software. Open Source Software vs Freeware is any software that is free to use and available to the general public. If you are a, it can be frustrating to use freeware because its source code is not open or rather, it’s closed so you can edit the software to fit your needs. You can only use the features it offers even if it it is limited. Freeware Google Chrome is an example of freeware. While with open source software, you have the possibility to edit, improve, and add different features to fit your needs. An example is Chromium. Freeware source code is closed, only the developer or organization who created the software can view its source code while open source one can edit and redistribute. Freeware is copyrighted while Open source is not. Open Source vs Proprietary Software You do not need an authenticated license to use open source software. They function with GNU Public License. They can be installed freely on any computer system. Open source software is developed and maintained through open collaboration. Proprietary Software is software with protected source code(closed). They cannot be installed on a computer without a valid license. So users need a valid and authenticated license to use it. They are also copyrighted like freeware but the difference is that you do not need to pay to use freeware. An example is Windows Key features of Open Source Software In this section, I will elaborate on the various key features and values that cover Open source Software. Open source allows you to track and understand any changes made to software without relying on the software manufacturer. Transparency: : The main goal of the open source initiative is to allow anyone to freely participate and improve the software. By working together, we can easily identify and solve problems Collaboration : Including diverse perspectives from different brains can help identify the best ideas by decision-makers and could equally determine the success of a project. Meritocracy : When many people gather to help and share ideas, it creates a community where each other can get support. Community : Since source code is freely available to the public, different experimented community members can check, propose ideas and improve the software. Review : Many collaborators can update and test the code to check if there is any bug and fix it. Open sources do not depend on one, two or three people to maintain but rather have a large community that supports them. Reliability : Flexibility refers to the fact that you can use open source code to address problems that are unique to your business. Plus you can rely on community help to improve an existing solution or implement a new one. Flexibility Most of the time, open source is free to use and download. Sometimes you may need to pay for only reinforcing security and interoperability. Lower cost: What are the Benefits of Open Source? Choosing to use or contribute to open source software offers many benefits. Some of them include: Open source is free You have more control over the software. Compare to Closed software you can edit open source software to fit your needs. It’s good for anyone beginning computer programming. As a beginner, you can become a better programmer by sharing your work and contributing to other works Open source is good because it can help to enhance software security as anyone can spot and fix a security issue quickly that the original programmer may have missed Open source projects tend to be more stable than other types of software since there is a great community behind ready-to-deploy means to improve it. It promotes collaboration through the creation of communities. Open source is flexible They have Good Documentation What are the Disadvantages of Open Source Software? Not everyone embraces the open source philosophy. Some organizations still debate the fact that open source software is more vulnerable since their source code is open to anyone. I have been in the software community for 3 to 4 years now and I have noticed that they tend to be more secure since issues linked to security or not can easily be identified and fixed by community members. However, I equally notice some drawbacks of open source. Open source software can be difficult to set up, especially for beginners. Some of them may lack user-friendly interfaces imposing you to use a Terminal Liability problems. Open source rarely contains warrant or liability protections Compatibility issues. Initially, there is no cost but it can be costly in the long term especially if you need immediate support. There is also training cost in case you incorporate the software into your team. Not all open source possesses a user manual although you can seek help from the community it won’t always be available immediately like commercial software where support is available 24h/7 How can you Start Contributing to Open Source? It’s quite simple to contribute to open source software. You could do that by identifying and fixing bugs, adding features, improving documentation and in the form of blog posts, technical tutorials or creating Videos. You also create your own open source project to promote open source. writing content To contribute to existing open source software, you should head to : GitHub Find a project and Fork Clone the Project to your Local machine Create a new branch Make changes Commit your changes Push your changes to your remote repository Finally, Create a pull request If you don’t have any project ideas, then open source projects to contribute to include Medusa Tooljet Docz SAML Jackson Chatwoot Open Source License An Open source License governs how other individuals besides the initial developer can use, modify and redistribute the software. It allows the software to be transparent and changed by members of a community or third party. According to the include Open Source Initiative Apache License, Version 2.0 Eclipse Public License version 2.0 Common Development and Distribution License 1.0 GNU Library General Public License version 2 Mozilla Public License 2.0 (MPL-2.0) The 2-Clause BSD License Example of Open Source Software You might have used an open source software without realising. There are many of them out there. Some popular and most-used open source software is WordPress, an open source CMS for Content Management for web, desktop and mobile development Flutter Like Bootstrap for website layout CSS Frameworks VLC Media Player for video and sound player Libre Office for word processing Open source Linux Operating System is open source too. Python Open source reporting tools include Metabase, BIRT Open source PDF viewer include Qoppa PDF Studio, pdfFiller, and Soda PDF Some Java open source projects are Arduino, Mindustry, and Signal Android We equally have open source development tools like Eclipse IDE, and Apache Cordova FAQ Is Open Source Software Free? Most open source software is free but there are some exceptions. Some open source licenses are restrictive and do not allow the creation of a modified version. An example is Open Source Watcom Where can I find OSS? You can find and download open source projects on SourceForge, OSDN, FossHub, GitHub, and F-Droid Why Should you Contribute to OSS? The most important advantage of contributing to open source is that you can find and collaborate with other developers hence improving your developer network. Why is Open Source Important? Open source improves innovations through collaboration with other organizations and developer What are Some Open Source Competitions? are contests where you can learn to participate in open source projects, demonstrate your coding skills, and get feedback. Examples of Open source competitions include Open Source Contest, , , , , Open Source Competitions Google Summer of Code (GSoC) Outreachy Open Summer of Code Free Software Foundation (FSF) Internship Program GirlScript Summer of Code (GSSoC) What is Closed Source Software? Closed Software is software whose source code is only available to the author. So only the author can modify and copy it. What is the Open Source Initiative? The Open Source Initiative is a non-profit organisation with the main aim to educate about, form, and inform the benefit of open source Conclusion Open source is available for anyone to edit, use and redistribute. Some key features of open source include transparency, reliability, available community and flexibility. However, OSS can be costly in a long run. So if you are planning to incorporate OSS into your business you need to include additional or urgent costs in your project plan including the cost of training staff members to use the software. Some popular Open source projects include Apache, WordPress, and Mozilla Firefox. If you like this kind of content, feel free to share or subscribe to our newsletter below to get fresh content like this periodically. Initially published on Learndevtools here.

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You can’t really invest in a data team too early. That’s something we learned as a small but growing team. We invested in a data team at an early stage, so we could establish product usage trends, create business insights, and identify areas to improve our product. If you’re an early-stage startup, start building your data strategy team and architecture as soon as you can. You don’t have to go big—a one-person data team can make a massive difference to your success as you grow. Hire a Generalist First When it comes to launching your data team, you might be tempted to hire an expert from the largest brand name you possibly can. Our advice: find someone with early-stage startup experience and has worked with data at scale too. There are wonderful people with brilliant minds working on large enterprise data teams, but these large teams often have their employees specialize in specific parts of their data platform. That makes sense when you have 10 data engineers on a team or when you have a large team of machine learning data scientists working on pricing algorithms. But when you’ve only got one data role for the entire organization, you need someone who can do a little bit of everything. It’s important to first hire an all-rounder who can create data insights, develop business hypotheses, and create a scalable data architecture from scratch. You need someone ready to start building the data team from the ground up, preferably someone who can hire out the rest of your team once you’re ready to do so. This first hire should be someone who has experience building (or at least working on) a data team at an early-stage startup. They need to be able to generate value quickly and have the business acumen to ask the right questions. They should be able to approach data in an 80/20 manner to deliver immediate results instead of diving into the hardest problems that you have. For the data engineering side, it’s common to hire a consultant or contractor to help set up your architecture. We decided to complement our first hire with a consultant. This way, we have the best of both worlds – an expert in data engineering part time, and a generalist who can maintain data architecture on a day to day basis. We set up strong foundations and also kept our knowledge in house. A Small Data Team Can Answer Big Questions Even if you can’t hire right away for all the roles you want on your data team long term, you can do a lot with only one or two of the right people. The key is thinking in terms of scalability and efficiency. What helps your data team do more with less? And how will this work when your team is bigger? Scalable analyses are extremely important with a small data team. By thinking ahead about how your processes and workflows will function as your team grows, you’ll save time and create valuable solutions for some of your employees’ most time-consuming issues. For example, we immediately realized that our sales team had no visibility into prospective customer product usage, and it was going to come up often. So we built our sales reps a workspace dash so they can pull the data themselves. Our data team’s job is to enable data-driven decision-making. When we saw an opportunity for a one-time data investment that could lead to hundreds of saved hours on the sales side, we acted on it. A small data team should also be focused on preventing issues in the future. If you only have the headcount for a one-person data team at the moment, keep them focused on your biggest problems but also budget time to maintain and scale your data architecture to reduce tech debt. The Tools Our Data Team Uses From the beginning, our goal was to be forward-thinking with every decision. Like all startups, we wanted to generate immediate ROI. We use a centralized data warehouse that is our single source of truth – nothing hurts data teams more than having fragmented or untrustworthy data. We’re able to consolidate our data thanks to automations and integrations. This helps us provide valuable insights to the entire company with low lift. We pipe in all of our data using ELT tools like Segment (which is also our data collection tool), Fivetran, and Snowflake external stages. We use to clean all of our raw data into a more usable format. For example, our dim_user table has ~20 tables powering it under the hood, that have a lot of critical info on our customers, such as how many notifications they send and what plan type they are on. By making this so readily available, our non-technical users can quickly draw business insights. We can quickly look at non-paying or self-serve customers that are sending a lot of notifications to identify business contract prospects. Our customer success team also built reports to look at the health of our business contract customers. We also use dbt to clean our data of sensitive information. dbt We use Census to send our warehouse data back to places such as HubSpot and Courier. This allows our sales and marketing teams to access this information on the platform that they are using on a daily basis. Metabase is our BI tool. It allows us to create dashboards, write SQL queries, and provide a self-serve format for non-technical users. We’ve found that creating these kinds of self-serve data dashboards allows users to answer a lot of their own questions. They are quick to spin up without large investments upfront like teams that use Looker or Tableau. For example, we have a KPI dash that shows the top metrics across the company. We can also track how individual workspaces are performing and identify workspaces that we can target for our sales team. We have also brought data-driven decision-making to our product development by deep-diving into our customers’ product usage. One example is that we found that a lot of our users were creating their own Courier instance instead of joining their colleague’s existing Courier instance. Using this insight, our product team went back to the drawing board to create a page that surfaces Courier workspaces that are created by the same business email domain so you can easily request access. We also use Propel to power our in-app analytics to provide visibility for our end users. One way that we use Propel is to deliver template analytics for our Business-tier customers. Propel allows our engineers to easily put GraphQL visualizations on top of our metrics from our warehouse. Our Data Team Is Evolving Again Now, we’re entering a period of growth, so naturally, we’re evolving our data approach again. Our current goal is to enable our data scientists to build subject matter expertise while still maintaining centralized data standards. Our current structure is centralized, with a senior analytics engineer reporting to the head of data. It works now since we’re still in the earlier stages of building out a data team. But when the team expands, we will be transitioning to a hybrid model. Data scientists will cover certain business areas (like the product team), but they’ll still report to the Head of Data. This will allow the data scientists to build subject matter expertise while still maintaining centralized data standards as we continue to grow. Because the data team will always be changing and growing along with the rest of the organization, you need standards that will scale with the business. If you’d like to learn more about how we approach data standardization, check out our blog post on . how Courier became HIPAA compliant

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Open source has been on the rise for the past few decades. From small startups to large enterprises, open source has now become a crucial part of the software development process. While open source is often thought of as simply a free alternative to proprietary software, it is actually so much more than that. In this article, we will explore the reasons why open source has been so successful, the areas where it has not been as successful, and the differences between open source and free tiers of software, with a deeper look on the data infrastructure industry. How open source has been eating software The term "open source" was first coined . Since then, open source has taken the software world. Unix was replaced by Linux, Oracle DB by Postgres and MySQL, Bigkeeper by Git, even VS code has become the IDE standard. According to a 2020 report by the , open-source software has grown so much that more than 96% of global organizations are now using it, and the number is increasing year over year. In a , 98% of respondents said they use open-source software, and 96% of respondents said they contribute to open-source projects. in 1998 by the Open Source Initiative (OSI) Linux Foundation 2020 survey by GitHub As , as software eats the world, open source has been eating software. One of the most notable areas where open source has been making significant gains is in the data infrastructure industry. Tools such as , , , , , , and more have been widely adopted by companies and organizations for big data processing and analytics. a16z said Hadoop Spark Kafka Airflow Grafana Kibana Metabase Let’s dive into explaining the reasons behind the success of open source. Why open source is so successful Open source has been extremely successful for a number of reasons. Control, flexibility and agility One of the main reasons is control, which refers to the and that open source provides. This allows developers to customize the software to meet their specific needs, which gives them a competitive advantage and can be especially important for large enterprises with their custom needs. flexibility agility Accelerated time to value Open-source software is often easier to start with and deploy than proprietary software. It is most often deployed and hosted within a company’s servers or virtual private cloud, which enables developers to avoid going through long and cumbersome security compliance processes. This is reinforced by the absence of budget validation processes to go through too, as there is no charge for open-source software. Developers can literally start testing technology the day they discover it. In comparison,  proprietary software might not be accessible to developers for several months because of those security compliance and budget validation hurdles. Better reliability and security There are several reasons why open-source software tends to be more reliable and secure. For one, it is deployed within the company’s infrastructure. This means companies are in full control over their data. Then, having direct access to the code enables teams to look for any potential issues directly without depending on external parties whose priorities might not align with those of their company. Last but not least, the development model allows for more feedback, contributions, and, most importantly, trust. It allows fast iteration cycles with multiple participants who contribute, and everything is version controlled and transparent. This improves the quality and reliability of a product, as issues are fixed faster and features added more quickly. community-driven This makes open-source software more reliable and secure than proprietary software. Accelerated innovation Open-source software tends to see higher innovation velocity, thanks to all the community contributions. Another reason for that velocity is actually an often overlooked point: open source also , as prefer working on open source and value the open-source philosophy. And stronger teams innovate faster and better. attracts technical talent most engineers No vendor lock-in The last aspect driving open source adoption is that proprietary software always comes with some lock-in aspect that makes it hard for companies to switch from one solution to another in the event their needs are not addressed. With open source, if you’re not satisfied with a paid version of an open-source solution, you can just go back to hosting the open-source version; you’re not locked into that paid solution. Where open source is not successful While there are some industries where open source has become the norm, some industries haven’t been touched by it. Here are a few criteria that are common to all of the industries that do not (yet) use it: Users of open-source technology are not technical Open-source software adoption is mainly driven by engineers attracted by the accessibility and visibility of the code. But because the rest of the organization doesn’t work with software code, the value of open source doesn’t matter much to them. This explains the lack of adoption of open source in marketing, sales, and finance tools. Lack of overlap between use cases, too much customizability needed Another issue that might occur with open source is the use case addressed needs too much customizability, meaning that every company uses the software for a different use case. While open source is great for addressing custom needs, as users have access to the code itself, a lack of common ground will make it difficult to build a valuable one-size-fits-most foundation to start. Lack of overlap between user profile and contributor profile Another challenge facing open source is the lack of overlap between the developer community and the user community. In many cases, the potential contributors of open-source software are not the same people who are using the software. This limits the contribution potential of the community. Developers are very motivated to make open-source software work if they use it. It actually becomes part of their job to make that software work for their company. One could argue that this criterion is more indicative of the community contribution potential of a project. Not a known problem Finally, open-source software will have less adoption if the problem it addresses is not well known, as it will require a lot of education of the market to make it successful. For example, open-source solutions for IoT, edge computing and many others are still relatively new, and it will take time for the industry to mature and for users to understand the benefits of open source. On the other hand, for example, generating and collecting logs is a very old problem, which might be a good reason why OpenTelemetry became the second-highest velocity project in the . , the open-source data integration leader is on a similar path ; pretty much any company has data integration challenges, so no education on the value this open-source solution provides is necessary. CNCF ecosystem Airbyte For all the industries where open source is not successful, we see a lot of adoption of free tiers. Without considering the difference in audience, let’s see how they compare. How open source is different from a free tier Open-source software is often thought of as a free alternative to proprietary software, but it is important to understand that open source is not the same as a free tier. As mentioned above, Open source offers more than free tier in several key aspects: open source is not just free to use–it's also free to modify, distribute and share. Control and customizability: An open-source community is much more engaged than a community of free users. This leads to a more robust and stable product compared to a free tier, which may not have the same level of support and maintenance. Engaged community for better reliability and innovation velocity: for software that requires some security compliance process, the accessibility of the open-source code makes that process a lot simpler. Time to value, when security compliance is involved: if you are not satisfied when using the paid version of an open-source software, you can always switch back to the open-source version. No vendor lock-in: The one time a free tier is more convenient than open source is when the main concern is . accessibility, when there is no need for security compliance So deciding whether to use a free tier or open-source solution will depend on the specific needs of the project. If a company is just starting out and wants to test out a technology without committing to a long-term contract, a free tier may be the best option. Keep in mind, however, that a free tier – by its very nature – is designed to offer a limited set of features in hopes of drawing you in to a paid version with greater scale and flexibility. Hence, if a company requires a more advanced set of features, more customizability and support from a large community of developers, open source may be a better choice. Additionally, if a company has specific security or compliance requirements, open-source solutions may be more easily compliant, as the code is transparent and can be audited. It's important to consider the criteria of each solution and weigh the pros and cons to determine which one is the best fit for the specific project or use case. What about the data infrastructure industry In the data infrastructure industry, depending on the problem, open source or free tiers might be a better fit. Data warehousing For example, when it comes to , a free tier may be the best option. Many vendors offer a limited version of their data warehouse for free, which can be a great way to get started and test out the technology. This allows end users to experiment with different data warehousing solutions without committing to a long-term contract or paying for a full-featured version. data warehousing Data visualization On the other hand, when it comes to , a free tier may not be the best option. Data visualization is often a critical part of data analysis and decision-making, and a free version may not have all the features and capabilities that a company needs to drive the right decision. In this case, open-source solutions such as may be a better option, as they offer a wide range of features, flexibility, and customizability. data visualization Apache Preset Data orchestration Orchestration users usually have a deep need for customization, as they might want to trigger custom Python jobs, or jobs on other third-party tools. A limited free tier would prevent users from getting the value of the . This explains why the three top data orchestration tools - , and - are all open source! orchestrator Airflow Dagster Prefect Data integration, specifically ETL Last but not least, what about and ? data integration ETL While most connectors are common to a lot of companies, most companies also have custom connector needs. 25% of the connectors used by users are custom. Airbyte Also, a free tier will most likely be limited in the volume of data, and you might want to test the solution for a big source of data, such as a database, in order to see how that ETL solution performs with high volume. A free tier won’t help you address these concerns. This makes data integration another industry where open source is thriving, with 40,000 companies having adopted Airbyte in some way in the last two years. In conclusion, open source is much more than a free tier. While free tiers can be a great way to get started and test out new technologies, open-source solutions offer much more control, flexibility, and community support. They are also more likely to be adopted in areas where customizability and adaptability are important such as visualization, orchestration and ELT processes. In the data infrastructure industry, only the data warehousing segment hasn’t been eaten by open source yet. Also Published Here

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