One dbt unlocks the potential of the analytics development lifecycle
PHILADELPHIA, October 8, 2024 /PRNewswire/ — dbt laboratoriesthe pioneer of analytical engineering, today announced new features and enhancements for debt cloudconsolidating the platform as a data control plane for business analytics. Announced during the keynote speech at the dbt Labs annual conference, Merge 2024New innovations support users at different stages of the analytics development lifecycle by providing unprecedented cross-platform flexibility, enabling more people to contribute to the analytics workflow while accelerating speed and productivity, and improving organizational trust in data. This includes the launch of dbt Copilot, an AI engine integrated into dbt Cloud to boost productivity while improving data quality.
Unify analysis workflows with One dbt
A central theme of Coalesce 2024 is One dbt, a commitment to creating a single unified dbt experience, regardless of the infrastructure, data platform or cloud an organization uses.
Through One dbt, capabilities will be unified for data transformation, observability, orchestration, cataloging and semantics, regardless of which data platform or cloud an organization chooses to use – providing universal view of what is happening within the data estate, accelerating data delivery, data quality tuning and optimization of computing costs.
According to dbt Labs 2024 State of Analytical Engineering report, decade-long challenges such as data transformation and improving access to computing have largely been resolved, but major hurdles related to data quality, data literacy and data ownership continue to plague the sector. By kissing the Analytics Development Lifecycle (ADLC), a mature, integrated analytics workflow, organizations can now benefit from a standardized, scalable way to overcome these challenges and move faster with trusted data.
“The data industry has made real progress toward maturity over the past decade,” said Tristan Handyfounder and CEO of dbt Labs. “But real problems persist. Siled data. A lack of trust. Too much ‘duct tape’ in our operational systems. Our announcements this week go a long way to filling these gaps: a cross-platform, multi-person dbt experience. reliable and AI-infused, all facilitating a single, mature workflow: the analytics development lifecycle.
Deliver enhanced flexibility, collaboration and trust with the dbt Cloud data control plane
dbt Cloud is a data control plane that supports users at every stage of ADLC, regardless of their title, technical aptitude, chosen data platform, or where they create and consume data. data, and further accelerates workflow with AI. dbt Cloud centralizes metadata and makes it actionable in the ADLC workflow.
A suite of new features and capabilities in the dbt Cloud data control plane, introduced during Coalesce, have been designed to expand adoption to a more diverse set of data practitioners, make data development more accessible, streamlined and governed, and create and automate high levels. quality data pipelines. These include:
- co-pilot dbt, dbt Cloud’s AI engine that helps users accelerate their analytics workflows. dbt Copilot is designed to automate tasks that previously required repetitive manual work, significantly improving productivity, data quality and stakeholder trust. Today, this includes the ability to automatically generate tests, documentation and semantic models (all in beta), an AI chatbot that allows business stakeholders to ask natural language questions about their data (in beta as part of the native dbt application in Snowflake), and the ability to bring your own OpenAI API (GA) key. In the coming months, dbt Copilot will be expanded to help automate model code generation.
- Dbt Mesh cross-platform will rely on dbt Mesh existing support forproject references and allow cross-referencesplatform references using the Iceberg table format as the underlying transport layer. This allows users to eliminate silos while maintaining data governance, even in increasingly complex cross-platform environments. With cross-platform benchmarks in dbt Mesh, data teams will be able to centrally define and maintain data governance standards, visualize end-to-end tracing across various data platforms, and find, reference, and reuse Easily existing data assets instead of rebuilding.
- Apache Iceberg™ support allows users to create tables in Iceberg format and benefit from Iceberg’s best-in-class performance and portability. Support for Apache Iceberg makes cross-platform dbt Mesh possible. Snowflake support is currently in beta and Athena, Spark, Databricks, Starburst/Trino and Dremio are GA.
- A new visual editing experiencecurrently in beta, is a low-code, drag-and-drop environment for creating and exploring dbt models, designed to democratize ADLC to more user types. Like everything else in dbt, these visual models compile to SQL and new code must be version controlled before being deployed to production. This new development interface gives downstream users (who already have the most important business context) the ability to create analysis code in an accessible and secure manner. Users more familiar with SQL may choose to use the visual editing experience to check their work and explore a visual representation of their models.
- Advanced ICnow GA, allows users to compare code changes as part of the CI process to detect unexpected behavior before new code is merged into production. This improves code quality and helps organizations optimize their compute spend by materializing only the correct models. Users can see a summary of their changes in their Git pull request and dive into the modified, added, and deleted rows and columns in dbt Cloud.
- Data Health Tilesnow GA, can be integrated with any downstream application so data consumers can have real-time context on critical trust signals like data freshness and quality directly in the tools they work with .
- Automatic exposures with Tableaunow in preview, automatically integrates Tableau dashboards into the dbt line. This enables data practitioners to optimize, accelerate, and automate end-to-end pipeline orchestration, from source to dashboard. Business users can be sure they always have the most up-to-date data to make their decisions.
- An upcoming Power BI integration for the dbt semantic layer will enable business users who have standardized on the Microsoft ecosystem to query and analyze consistent metrics.
- Teradata and Athena are supported adapters. dbt Cloud now integrates with Teradata (in preview) and AWS Athena (GA), enabling more organizations and teams to collaborate on data workflows.
These and other new features enable dbt customers like Rock to unify their data, standardize and accelerate their workflows and maximize their debt investment.
“dbt Core has jumpstarted the growth of our data platform, and dbt Cloud has allowed us to deliver it across the world,” said Yannick MisteliHead of Engineering, Global Product Strategy at Roche. “Today, we are able to power our platform in 70 countries and run more than 15,000 models and 40,000 tests every day. We can support our core and country teams with the workflows that work best for them and push code into production in two-week cycles instead of the previous quarter or semester cycles.
For more information about dbt Cloud and how it helps users at every stage of ADLC, visit https://www.getdbt.com/product/dbt-cloud.
About dbt laboratories
Since 2016, dbt Labs’ mission is to help analysts create and disseminate organizational knowledge. dbt Labs pioneered the practice of analytical engineering, created the leading tool in the analytical engineering toolbox, and was fortunate to see a fantastic community come together to help push the boundaries of flow analytical engineering work. Today, 50,000 teams use dbt every week.
To learn more about dbt Labs, visit getdbt.com and follow us LinkedIn.
SOURCE dbt Laboratories
YOU WANT NEWS ABOUT YOUR COMPANY FEATURED ON PRNEWSWIRE.COM?
440,000+
Newsrooms and
Influencers
9k+
Digital media
Points of sale
270,000+
Journalists
Registration
