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dbt Labs is on a mission to empower data practitioners to create and disseminate organizational knowledge. Since pioneering the practice of analytics engineering through the creation of dbt—the open source data transformation tool made for anyone that knows SQL—we've been fortunate to watch more than 5,000 companies use dbt to build faster and more reliable analytics workflows. dbt Labs also supports more than 1,000 customers using dbt Cloud, the centralized development experience for analysts and engineers alike to safely deploy, monitor, and investigate that code—all in one web-based UI.

dbt Labs

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Tristan Handy is a lot of things: co-creator of dbt, founder and CEO of dbt Labs, and self-described “startup person.” But besides leading dbt Labs to a

Tristan Handy’s Audacious Vision of the Future of Data Engineering

"We at Yuga are committed to building a Web3 world that's interoperable and fair for all creators, and we're pleased to be collaborating on the OMA and the standards we believe will drive its success,” Yuga Labs CTO Mike Seavers said in a statement.

NFT Creator Royalties Are Broken—Yuga Labs and Magic Eden Join Group Trying to Fix Them

A manufacturer of lab-grown diamonds owned by a Detroit-based private equity firm has moved to liquidate with $48 million in liabilities amid several pending legal disputes, according to a ...

Lab-grown diamond maker with Detroit backing files for bankruptcy with $48M in liabilities

Big data platform startup dbt Labs Inc. said today it’s reinvigorating its flagship dbt Cloud offering with new enhancements and capabilities that it says will enable every employee to ...

Big-data startup dbt Labs rolls out data mesh architecture and enhances semantic layer

SAN DIEGO, Oct. 17, 2023 /PRNewswire/ -- dbt Labs, the pioneer in analytics engineering, has announced the next generation of the dbt Semantic Layer following its acquisition of Transform in ...

dbt Labs Announces the Next Generation of the dbt Semantic Layer, Introduced Alongside New Integration with Tableau

SAN DIEGO, Oct. 17, 2023 /PRNewswire/ -- dbt Labs, the pioneer in analytics engineering, today announced several new product capabilities to its dbt Cloud platform at its annual customer conference, Coalesce 2023. New dbt Cloud capabilities enable ...

dbt Labs Announces Major Enhancements to dbt Cloud to Enable Collaboration at Scale

SAN DIEGO, Oct. 17, 2023 /PRNewswire/ -- dbt Labs, the pioneer in analytics engineering, has announced the next generation of the dbt Semantic Layer following its acquisition of Transform in February 2023. Unveiled during the keynote at dbt Labs' Coalesce ...

SAN DIEGO, Oct. 17, 2023 /PRNewswire/ -- dbt Labs, the pioneer in analytics engineering, today announced several new product capabilities to its dbt Cloud platform at its annual customer ...

The dbt semantic layer is now powered by MetricFlow, the SQL query generation engine obtained from the recent acquisition of Transform Data.

Dbt launches next generation semantic layer to solve trust in data

Adoption of dbt has skyrocketed since it was launched just seven years ago, and today, more than 30,000 organizations use the open source software in

dbt Labs Tackles Data Project Complexity with Mesh at Coalesce

The dbt Semantic Layer is now powered by MetricFlow, following dbt Labs' acquisition of Transform in early 2023SAN DIEGO, Oct. 17, 2023

SAN DIEGO, Oct. 17, 2023 -- dbt Labs, a pioneer in analytics engineering, today announced several new product capabilities to its dbt Cloud platform at

The dbt Explorer is a brand new documentation and lineage visualization tool that supports the dbt Mesh paradigm and makes it easy for anyone within an organization to discover, understand and reuse dbt assets from across different teams. In this way, dbt Explorer helps to reduce friction within data development workflows, the startup said.

Scientists doing “gain-of-function” research said that heightened fears of lab leaks are stalling studies that could thwart the next pandemic virus. By Benjamin Mueller and Sheryl Gay Stolberg ...

Lab Leak Fight Casts Chill Over Virology Research

Trophy has announced a collaboration with Polygon Labs. The goals are twofold: to accelerate the onboarding of new players into Web3 games and fuel mass adoption of mobile Blockchain gaming. Polygon will offer support via infrastructure and tools ...

Trophy collaborates with Polygon Labs

Use Next and Previous buttons to navigate Coordinator of mechatronics programming Freddy Santiago gives a tour of Dutchess Community College’s Mechatronics Lab in Fishkill on Oct. 12 ...

New mechatronics lab in Fishkill aims to train local high-tech workforce

The company will launch these two new Revefi offerings at the dbt Labs' Coalesce 2023 event, which takes place next week. "The Revefi Data Operations Cloud – which disrupts the data ...

Revefi supercharges Data Operations Cloud with deep dbt integration, announces new dbt-only plan

Low-code software development, only recently trumpeted as the next big megatrend, is here and it’s already become mainstream. By 2025, 70 percent of new apps developed by organizations will be created using low and no code platform, up from less than 25 percent in 2020 according to . If you’re not already using low-code platforms, you will be very soon. Gartner Low and no-code development makes it possible for people without a technical background to create digital products, which is how the term “citizen developer” came about. However, the idea of the citizen developer is a little misleading. While it is true that you really don’t have to be techy by training to use low-code tools…..some transferable skills definitely help. As data scientist and low-code evangelist, Rosaria Silipo, put it, “ .” Codeless does not mean math-less Furthermore, while organizations and so-called citizen developers are of course benefiting from easier access to digital development, so are technologists. Industry studies show that a significant proportion of professional developers – easily a third and potentially nearly two thirds – are using low-code solutions to speed up their work or create workarounds. Low-Code AI: A Story of Supply and Demand? The only skill that enterprises need even more than IT is data science. The same market forces that are driving up low-code adoption in general are especially acutely felt when it comes to low-code AI solutions. Lack of people is not the only challenge for organizations wanting to get their AI projects up and running. AI development often takes a long time and is resource-intensive when large or complex data sets are involved. In other words, it’s costly. Added to that, competition for data scientists is fierce and remuneration generous. Big data and AI projects involve a serious commitment in terms of money and time. Until now, that is. Low-code is helping to significantly speed up timelines, while bringing down costs not only on a per-project basis but in terms of headcount – there’s mounting evidence that low-code solutions are by reducing the overall number of hires businesses need to make. In the recently updated Emerging Architectures for Modern Data Infrastructure, Andreesen Horowitz noted the ML industry is consolidating around a data-centric approach with low code ML solutions (like Continual and MindsDB) automating the ML modelling process. helping to bridge the skills gap Low-code Tools To Suit Every Job While this might sound like bad news for data scientists, it is worth remembering that data scientists themselves are benefiting from the increased speed and ease of using low-code AI tools. The low-code movement is often described as a “democratization” of all things digital, breaking down the barriers to development and to transformational outcomes. This democratization has been gradually taking place at every level of data science from data input to data transformation to machine learning. There’s a low-code, simpler way of doing many of the jobs that were previously highly complex or took a long time. Using low-code solutions, project timelines speed up and data scientists no longer find themselves locked into tools and processes based on the particular set of programming skills they happen to have. Take SQL as an example, a classic skill and the core language of databases. However, to then work with the data, a data scientist used to need to be proficient in a number of other languages, BI tools and ML solutions. No more: rather than extracting the data from the database, uploading it to a BI tool, later an ML tool, running the models, then loading the findings back into the BI tool, there’s a rapidly growing movement seeking to make the magic happen within the data layer, using the established and universal language of data, SQL. This example, from the field of in-database machine learning, shows how data scientists using standard SQL commands can now query predictions and build ML models from inside the database. Data pipelines are another example of a process that’s being radically simplified and speeded up thanks to companies like with its data transformation product based on SQL commands. dbt Labs Data scientists, who have long debated the relative merits of different programming languages and tools, are increasingly finding humble SQL can get them to their destination quickly and easily. Accelerating Transformation with In-Database Tools It’s an uncomfortable truth for data scientists, but analytics can easily become the bottleneck that slows down digital transformation. There are good reasons behind this – shortage of skills, cost, complexity. Low-code data science tools radically speed up the process of extracting actionable intelligence from data. In the hands of an experienced data pro, the speed, simplicity and cost effectiveness of in-database low-code solutions bring extraordinary possibilities. By making vast amounts of data more accessible, data scientists need only to be constrained by their imagination, rather than the usual reasons things don’t get done (cost, integration issues, time concerns, skills gap). Low-code solutions are helping innovative data scientists shine as they deliver ROI faster and showcase the power of ML for a growing number of scenarios. They also help to increase an organization’s overall digital output. After all, the pandemic caused the world to pivot to digital solutions and there has been no going back. reported an increased demand for new digital products and services, which has continued to grow since. For data and IT teams that were already overworked, overstretch and super stressed, the availability of low-code solutions couldn’t have come at a better time. Three quarters of CIOs The Challenges of a Low-code World Although the adoption of low-code data science tools is rising rapidly, it’s still early days for this type of development. Organizations are understandably worried about the security and compliance implications of non-experts handling data (and other) projects. As development and data science become increasingly decentralized, one of the biggest challenges will be for organizations to foster a culture where innovation can occur freely, but remains strategic, relevant, and secure. Most experts agree that company policies will need to be updated to reflect low-code practices. Low-code AI solutions, when used by data scientists, are a way of working more efficiently, but they could, indirectly, end up adding to their workload. While “citizen development” sounds like it operates independently of the IT team, the reality for data scientists will mean increased calls to help integrate, rescue or otherwise support digital tools that have been created elsewhere. Life as a Low-code Data Scientist Low-code tools represent a major disruption to the way digital has been done so far. Like every disruptive force, it creates opportunities while threatening the status quo. For data scientists, low-code is now becoming a fact of life, a hybrid approach to AI development that should be embraced because of its undeniable speed and simplicity. Furthermore, democratization and decentralization of IT are unstoppable. For data scientists, the question now is not whether to adopt low-code, it is how best to do so. I have seen low code fast-track many data science careers. Understanding their field as they do, they already have an advantage over their non-data colleagues. In the hands of data scientists, low-code solutions help data scientists work faster and smarter while at the same time being more agile and creative.

About The Meteoric Rise of the Low Code Data Scientist

Acerca del ascenso meteórico del científico de datos de código bajo

À propos de The Meteoric Rise of the Low Code Data Scientist

निम्न कोड डेटा वैज्ञानिक के उल्कापिंड उदय के बारे में

ローコード データ サイエンティストの急激な台頭について

Sobre a ascensão meteórica do cientista de dados Low Code

Giới thiệu về sự trỗi dậy số liệu của nhà khoa học dữ liệu mã thấp

关于低代码数据科学家的迅速崛起

There was a time when the data analyst on the team was the person who listened intently to a manager's business hypotheses. data is searched in Excel sheets, queried via email, or queried with SQL. The Data Analyst sets out to find answers by exploring the data for weak signals. One by one, a PowerPoint fills up with graphs and action titles to steer decisions or define a business strategy. Shortly thereafter, the data analyst started an exciting adventure: The Business Intelligence, or BI, analyst was at the forefront of digitization and was a major influence on the impact of data for decision-making. But technological advancements quickly overtook the role of the Data Analyst. A company wants to go faster, bigger and better. Data and consequently Data infrastructure is growing and so are the demands on technical skills: DevOps, DataOps, ML Engineer, Data Engineer, and Full Stack Data Scientist. PowerPoint reports have been replaced by dashboards. The BI analyst role became less defined. While in the past a BI analyst could analyze data in Excel or MySQL and then create a report, now it's just the person who visualizes data, troubleshoots the dashboard, and briefs the IT team. It is the end of the fast, iterative quest to find truth in data. Pipelines are integrated into lengthy sprints and long feedback loops. It's now the job of engineers and not of self-taught Business “McGyver” Analysts. In the worst-case scenario, the Business Analyst becomes the second-class data citizen in the enterprise. Interactions with business stakeholders become theoretical instead of pragmatic and fast. The BI analyst is left out, and the engineering department is left alone and overloaded with tasks. One symptom of this change is moldy data and dashboards. sums it up in his article: Mikkel Dengsøe A dashboard has outdated metrics definitions. A dashboard only partially loads because an underlying data model is broken. KPI targets are imported from an outdated spreadsheet. An exec has bookmarked a dashboard you made in a rush last year and still uses it to make decisions. We need a new understanding of a BI analyst to get back to old strengths. BI Analysts need to be able to build pipelines, test metrics, and visualizations quickly and iteratively despite the Modern Data Stack. The BI Analyst should spend more time on analysis than on fixing dashboard problems. We need an intermediate layer... ... that connects raw data and end metrics. .... that can be easily used by no-coders. ... that integrates with modern data stack. .... that is extensible by engineers with tools they love. You might know Matti and me. We are working a lot with companies on data-driven challenges and trying to find fast solutions and open-source them. . The BI Analysts can build workflows from data blocks (Data Sources = Airbyte under the hood), transformation blocks (Transformation= dbt), and data science blocks and connect them to a dashboard. Ultimately it means that a BI Analyst can fastly iterate metrics and professionalize them in a way (with dbt under the hood) so that an engineer can always extend the tool. with Matti for this very reason that allows BI Analysts to build advanced data pipelines directly through an abstraction layer I have just released an open-source tool This creates a clean codebase under the hood. For example, DBT projects are created and built automatically. We have the idea to build a tool for the data analytics space similar to Webflow for web designers. An experienced engineer can customize the dbt models or create new models and make them available on the Kuwala Canvas for no-coders.  Did I mention we are open-source? 😅 It would be awesome working with you together on a PR. If you are a business - Yes we are still searching for Design Customers! We don’t bite! This flexibility is necessary because data projects are individual, grow, and need to be customizable. Back to topic: Our current project state covers the following points: 💯 The setup of Kuwala is now straightforward. Just clone the repo and pull the dockers! 🗂 You can now easily connect to Snowflake, PostGres, or BigQuery. 🧱 +20 Transformations covering merging, aggregating, and filters for data. Under the hood, we are generating clean dbt Labs models! 🛠 you can now also easily add a new transformation by using dbt-core - here is a guide! You can easily start Kuwala on your local machine over Github, here! We now search for more people using it and building separate parts to grow as a community. We are set - are you? Start hacking! Send us your issues! Start contributing! And if something doesn`t work, and we will help you 🚀 join our Slack community Also Published Here

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