Dlt

TL;DR Lakeflow Declarative Pipelines is the evolution of Delta Live Tables, and the rename signals a real shift in mental model: from “tables and dependencies” to “data flows and transformations” The three core building blocks are streaming tables (incremental, append-only), materialized views (full recompute, best for aggregations), and AUTO CDC for slowly-changing dimensions without hand-rolled merge logic Physical optimisation is increasingly automatic in 2026 - liquid clustering is the default, predictive optimization handles maintenance, and Z-order is legacy Keep hand-rolled Spark jobs for imperative logic, external API calls, and ML workloads; Lakeflow is for SQL-shaped data movement Lakeflow and dbt are complementary rather than competitors - some teams use Lakeflow for ingestion to silver and dbt for silver-to-gold If you’ve been writing Delta Live Tables (DLT) pipelines, you’ve been building with Lakeflow without knowing the new name. In 2026, the rebranding matters because it signals how Databricks now wants you to think about declarative pipeline design. ...

Overview Databricks offers certification tracks aligned to common roles: Data Engineer, Data Analyst, Apache Spark Developer, Machine Learning Engineer, and Generative AI Engineer. All certifications: Validity: 2 years from pass date Cost: $200 per exam attempt Format: Multiple choice, proctored online Recent Updates (2026): Emphasis on Lakeflow Declarative Pipelines (the evolution of DLT), Unity Catalog, liquid clustering, predictive optimization, AUTO CDC, Lakehouse Federation, and serverless compute Choose a certification based on your: ...