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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Generative AI
Building generative AI systems on Databricks.
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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Generative AI
Building generative AI systems on Databricks.
TECHNOLOGIES BEHIND THE EXAMS
Further reading
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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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