CloudEQS

dbt Labs annual user conference wrapped up yesterday and we're excited about the company vision, asks of the market, & product releases! Here are our take-aways: 1 - DBT's vision is to have a common unifying framework to solving analytics problems. What their CEO calls One DBT. 2 - Their ask of the market is to adopt the?Analytics Development Lifecycle?(ADLC) framework in order to mature the analytics programs within organizations. The goal is to apply best-in-class Software Engineering principles to data in order to improve the trust between business and data teams. 3 - Their product strategy is to become the Data Control Plane. Meaning, they've extended the DBT Cloud platform to include orchestration, observability, catalog, & semantic layer. 4 - Specific product features / releases include: Cross-Platform DBT Mesh, Iceberg Table Support, Visual Editing Experience, DBT Copilot, Auto-exposures w/ Tableau, Data Health Tiles, Amazon Web Services (AWS) Athena integration, & Teradata integration You can read more about the updates here - https://lnkd.in/enqyFzWV #Coalesce2024

In order to enable #GenAI use-cases, organizations need their data centralized, standardized KPIs & rigor in your data quality operations. We recently helped a leading #cloud security company optimize their data platform foundations. Here were some key results: - Optimize their Enterprise Data Platform (Snowflake + Matillion + dbt Labs) in under 7-months & under budget - 70% improvement in production bugs - Zero downtime during re-architecting - Built 650 DBT Models & 1,350 data quality checks - End-to-end data lineage visibility ? - Standardize KPIs in DBT's Semantic Layer If your organization is struggling with data quality, let's chat.

At our #Coalesce2024 keynote this morning, Tristan Handy shared his vision for the future of the analytics workflow: One dbt. One dbt is an ethos that influences all aspects of how we operate at dbt Labs. It represents our commitment to an integrated, governed, and scalable approach to data analytics. We are building towards a future where everyone—from data engineers to analysts to business decision-makers—has a common unifying framework for solving analytics problems. This framework should allow all of these humans to collaborate on any data platform, on any cloud. Their work should be accelerated by AI. And they should be able to work in the tools that make the most sense for them. Here are the latest platform features, which exemplify the One dbt ethos: ? Cross-platform dbt Mesh: Practitioners will be able to reference and re-use data assets from not only different projects, but also different data platforms, breaking down silos and fostering collaboration across an organization’s full data estate. ? Iceberg table support: This allows users to leverage the benefits of Iceberg's table format, including improved query performance and schema evolution. ? Visual editing experience: We are adding an intuitive, visual drag-and-drop interface (currently in beta) to our suite of data development environments. ? dbt Copilot: dbt Cloud’s embedded AI engine with various user experiences across the dbt workflow designed to help users accelerate and automate analytics (currently in beta). ? Advanced CI: Each CI job will include a breakdown of the columns and rows that are being added, modified, or removed in your underlying data platform as a result of executing your dbt job.? ? Auto-exposures with Tableau: Now in Preview, the dbt DAG can be automatically populated with downstream exposures in Tableau (and Power BI to follow). ? Data health tiles: With dbt Cloud, you can now embed health signals like data quality and freshness within any dashboard, giving your downstream stakeholders at-a-glance confirmation of whether they can trust the data they’re about to use. ? New integrations: dbt Cloud now integrates with Amazon Web Services (AWS) Athena (GA) and Teradata (Preview), enabling more organizations and teams to collaborate on data workflow. ? dbt Core v1.9: dbt remains committed to advancing our open source offering for data practitioners, and starting in Core 1.9, we have two big improvements, available now in beta. Read our blog (link in comments) to learn more about the new features and how we empower data teams to work seamlessly across data ecosystems. Which new features are you most excited about?

Want to know more about Matillion <> dbt? In this three-part series we explore an intro to Matillion and dbt, testing data quality, and getting into AI. Take a look: Pt 1 https://bit.ly/4dQ2D11 Pt 2 https://bit.ly/4cTW9x2 Pt 3 https://bit.ly/4dykubO #dbt #elt #DataEngineering