Databricks Data Quality as Code | Spark Declarative Pipelines Expectations (Episode 4) скачать в хорошем качестве

Databricks Data Quality as Code | Spark Declarative Pipelines Expectations (Episode 4)

🚀 Full Databricks Lakeflow Masterclass (32+ Episodes)    • Databricks Lakeflow Masterclass   📚 Start the course here: 1️⃣ Lakeflow Architecture    • Databricks Lakeflow Explained (2026) | Arc...   2️⃣ Lakeflow Connect    • 1️⃣ Lakeflow Connect Explained (2026) | Da...   This video is Episode 4 of the Databricks Lakeflow Declarative Pipelines Masterclass 2026. In this episode we implement Data Quality as Code using @dp.expect decorators. You will learn how to integrate validation rules directly into your data pipelines and enforce quality at different levels: @dp.expect → log violations @dp.expect_or_drop → remove invalid rows @dp.expect_or_fail → stop the pipeline We also explore business rule validations, reference data checks, and temporal validation strategies to ensure reliable data pipelines. ▶ Previous Episode Databricks Materialized Views Explained | Spark Declarative Pipelines Silver & Gold Layer Episode 3    • Databricks Materialized Views Explained | ...   ▶ Next Episode Databricks Medallion Architecture Pipeline | S Declarative Pipelines Bronze Silver Gold (Episode 5)    • Databricks Medallion Architecture Pipeline...