ETL, pipelines, architecture concepts
Build an automated cleanup pipeline in ADF that scans ADLS Gen2 folders, checks file ages using Get Metadata, and deletes files older than your retention period using If Condition and Delete activities.
Master every ADF and Synapse expression. Understand @dataset(), @item(), @activity(), @pipeline() with real examples, scope rules, value flow diagrams, and a complete cheat sheet.
Set up CI/CD for ADF and Synapse with GitHub. Complete guide covering Git integration, feature branches, ARM templates, GitHub Actions workflows, environment parameterization, and production deployment with manual approval gates.
CI/CD for Azure Data Factory and Synapse Pipelines with GitHub: The Complete Guide Read More »
Master ADLS Gen2 – hierarchical namespace, RBAC vs ACLs, Bronze/Silver/Gold structure, and integration with ADF, Synapse, and Databricks.
Azure Data Lake Storage Gen2: The Complete Guide for Data Engineers Read More »
Everything about Azure Blob Storage – hierarchy, blob types, access tiers, authentication, lifecycle management, and ADLS Gen2 comparison.
Azure Blob Storage Explained: A Complete Guide for Data Engineers Read More »
Why audit logging is essential for production pipelines. Design audit tables, stored procedures, and success/failure logging in ADF and Synapse.
Audit Logging in Data Pipelines: Why It Matters and How to Implement It Read More »
Master parameterized datasets in ADF. Understand @dataset() vs @item(), avoid BadRequest errors, and build reusable datasets.
Understanding Parameterized Datasets in Azure Data Factory: A Deep Dive Read More »
A practical comparison of ADF and Synapse Pipelines. Same engine, different context. UI differences, features, pricing, and when to use each.
Azure Data Factory vs Synapse Pipelines: A Practical Comparison for Data Engineers Read More »
Understand the difference between schema-on-write (databases) and schema-on-read (data lakes). Learn when to use each, how the Lakehouse architecture combines both, and how this relates to real ADF/Synapse pipelines.
Schema-on-Write vs Schema-on-Read: When to Use a Database vs a Data Lake Read More »
Compare Parquet, CSV, and JSON file formats for data pipelines. Includes real file size benchmarks, query performance comparison, and clear recommendations for when to use each format.
Parquet vs CSV vs JSON: Choosing the Right File Format for Your Data Pipeline Read More »
