Azure Data Factory, Synapse, pipelines
The complete hands-on CI/CD guide for ADF and Synapse. ARM template deep dive showing actual JSON structure, environment parameter files (Dev/UAT/Prod), Service Principal creation, pre/post deployment trigger scripts, complete GitHub Actions and Azure DevOps YAML files, multi-subscription enterprise setup, rollback strategies, and how our blog pipelines map to Git JSON files.
Master Databricks CI/CD from Git integration to production deployment. Repos setup with GitHub, branching and pull requests, folder structure, environment promotion (Dev to UAT to Prod), GitHub Actions and Azure DevOps pipelines, Databricks CLI and REST API deployment, writing testable notebooks with pytest, parameterized environment configs, Databricks Asset Bundles, and ADF vs Databricks CI/CD comparison.
Master every file storage option in Databricks. /tmp/ (temporary), DBFS (legacy), Unity Catalog Volumes (modern), External Locations (ADLS Gen2), and FileStore. Path prefix cheat sheet, managed vs external volumes, the append mode Illegal Seek bug with workaround, Python open() vs dbutils.fs vs spark.read comparison, and which storage for which use case.
Build a production data quality framework in PySpark. Eight validation checks (nulls, duplicates, range, regex, referential integrity, freshness, row count, type), the quarantine pattern, a reusable DQ class, DQ reports, integration with Medallion Architecture, and real-world DQ issues table.
Master Databricks Workflows for production orchestration. Job creation, Job vs All-Purpose clusters, multi-task DAG pipelines, task dependencies, parameter passing with Task Values, cron scheduling, retry and timeout config, email alerts, the complete Medallion workflow, triggering from ADF, and cost optimization.
Master the Medallion Architecture with the water purification analogy. Bronze (raw), Silver (cleaned), Gold (business-ready) layers explained with format selection, transformation patterns, ownership model, cost optimization, and exact mapping to every pipeline we built on the blog.
Master every PySpark window function with real business scenarios. ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, NTILE, running totals, moving averages, percent of group. Plus 5 real-world patterns: deduplication, gap detection, sessionization, YoY comparison, and top N per group.
Implement both SCD Type 1 and Type 2 using PySpark and Delta Lake MERGE in Databricks. Type 1: one MERGE with whenMatchedUpdate + whenNotMatchedInsertAll. Type 2: MERGE to expire + APPEND for new versions. Hash-based change detection, version numbering, idempotent tests, reusable functions, and complete Synapse Data Flow to PySpark MERGE mapping.
SCD Type 1 and Type 2 Using PySpark and Delta Lake MERGE in Azure Databricks Read More »
Master lazy evaluation — the most important PySpark concept nobody explains properly. Why Spark waits, how the Catalyst Optimizer rewrites your code, transformations vs actions with complete lists, predicate pushdown, column pruning, the DAG, narrow vs wide transformations, the cache trap, proving laziness with a hands-on experiment, and how it powers your SCD Type 2 pipeline.
The complete optimization playbook for Databricks. Small file problem and OPTIMIZE compaction, Z-ORDER for file skipping, conditional OPTIMIZE, VACUUM with retention and time travel interaction, partitioning strategy, AQE, broadcast joins, join best practices, caching, coalesce vs repartition, and the production checklist that turns 45-minute pipelines into 5-minute pipelines.
