News from the Databricks ecosystem.
Posts from databricks.com, MLflow, and dbt Labs — three trusted sources covering the platform, the open-source projects around it, and the data tooling layer most teams pair with it. Summarized for scanning.
This week
5 articlesAI-ready data in practice: What dbt Semantic Layer and dbt's MCP server and agent skills do for your team
dbt's Semantic Layer, MCP server, and agent skills now provide AI with essential business context. This enables your team to move beyond just clean data to truly AI-ready data in practice.
What's shipped in dbt — May 2026
May 2026 brings a roundup of dbt shipments since January, covering agents, Fusion, security, developer experience, dbt Core, and more. This post details all the product changes relevant to your Databricks workflows.
AI-assisted analytics engineering: Docusign’s framework for scaling dbt unit testing
Docusign reduced dbt unit test authoring from 5 hours to 30 minutes. Learn their AI-assisted framework for scaling dbt unit testing.
How NASDAQ built a governed intelligence layer with dbt and Databricks
NASDAQ built a governed intelligence layer using dbt and Databricks to process up to a trillion messages daily across 26 business lines. Learn why they chose this combination for their data architecture.
Week of May 4
1 articleWeek of Apr 20
4 articlesHow Obie cut compute costs by 30%, reclaimed engineering hours, and built stronger governance
Databricks shipped dbt Fusion, a new engine and state-aware orchestration for dbt. Learn how Obie used it to cut compute costs by 30%, reclaim engineering hours, and build stronger governance.
Using dbt with Databricks: Architecture decisions that determine success
Databricks users who skip dbt incur compounding costs. A solution architect explains key architecture decisions and when to act to ensure success.
Week of Apr 13
5 articlesMeet Antigravity: Google’s agentic IDE enters the dbt orbit
Antigravity, Google's new agentic IDE, now integrates with dbt. This pairing promises to significantly improve developer productivity, potentially giving you your weekends back.
Exploring dbt and Google with AI agents
Learn how to build your first ddbt agent by plugging AI into a dbt project. This practical guide explores what happens when AI agents interact with dbt and Google.
Tableau and dbt MCPs together
Tableau and dbt MCPs can now be configured together in a single file. Learn how this pairing unlocks impact analysis, metric reconciliation, and more.
New dbt Labs Report Finds AI-driven Acceleration is Outpacing Trust and Governance
A new dbt Labs report finds AI is accelerating data workflows, but governance and trust aren't keeping pace. This press release details the findings on how AI-driven acceleration is outpacing trust and governance.
Week of Apr 6
1 articleWeek of Mar 30
3 articlesOperationalize analytics agents: dbt AI updates + Mammoth’s AE agent in action
Databricks now supports operationalizing analytics agents with dbt AI updates and Mammoth’s AE agent. Learn how to build context for LLM models using dbt and MCP servers.
Week of Mar 23
1 articleWeek of Mar 16
4 articlesTypes of data transformations for machine learning
Databricks practitioners can explore key data transformation types for machine learning, including cleaning, scaling, feature engineering, and validation. This Pulse post details these transformations to help optimize ML workflows.
What are the most common data pipeline architecture patterns?
Databricks practitioners can explore common data pipeline architecture patterns, including ETL, ELT, batch, streaming, and semantic layers. This post details the most prevalent patterns to help you understand their applications and differences.
Week of Mar 9
7 articlesThe Iceberg ecosystem today
Iceberg is production-ready, and this post details what Databricks practitioners can realistically expect when running on top of it today. Anders Swanson explains the current state of the Iceberg ecosystem.
Why metadata management is critical for modern data teams
Metadata management improves discovery, governance, performance, and trust in modern data systems.
Why ETL is still essential for modern data pipelines
ETL remains essential for modern data pipelines, consolidating fragmented data, enforcing quality, and satisfying compliance requirements. These core benefits are why ETL is still a critical component for modern organizations.
How a global investment firm reduced runtimes by 30–40% with the dbt Fusion engine
The dbt Fusion engine and State-Aware Orchestration helped a global investment firm reduce runtimes by 30-40% in 3 months. Learn how NBIM achieved these gains without heavy optimization efforts.
Effective strategies to enhance data quality management
Improve data quality with testing, metrics, automation, and a scalable governance framework.