Microsoft DP-750: DevOps for Databricks Data Products
A Databricks notebook that works in one engineer’s development workspace is not automatically a production data product. It may depend on a…
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Databricks certifications
Lakehouse data engineering, governance and generative AI.
Databricks qualifications cover data engineering and AI on the lakehouse platform. A reliable data pipeline depends on governance, incremental processing and recoverable operations, while generative AI work introduces questions of evaluation, grounding and deployment.
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RELEVANT EXAM DESTINATIONS
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Data engineering
Reliable data systems and the Lakehouse platform.
Generative AI
Building generative AI systems on Databricks.
TECHNOLOGIES BEHIND THE EXAMS
Further reading
Selected recent articles touching this certification ecosystem.
Microsoft DP-750: Reliable Lakeflow Pipelines
A reliable data pipeline does more than finish on schedule. It ingests expected data, handles duplicates and late events, enforces quality rules,…
Read articleMicrosoft DP-750: Designing Data Models in Unity Catalog
In Azure Databricks, data modeling is inseparable from governance. A table design that makes a dashboard fast may create a privacy problem…
Read articleLakeflow Pipeline Design: Reliable Data Products in Databricks
A data pipeline is not production-ready because a notebook completes successfully when run by its creator. It must handle changing source volume,…
Read articleDelta Lake File Compaction: When OPTIMIZE Actually Helps
A large Delta table can contain millions of rows and still perform badly for a reason unrelated to total size: the data…
Read articleDatabricks Liquid Clustering: A Practical Delta Lake Design
Teams maintaining large Delta tables often inherit a complicated rulebook: pick static partitions, accept the imbalance when data grows, and schedule ZORDER…
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