ETL, pipelines, architecture concepts
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 »
Learn what Azure Data Factory is, how its 6 core components work together, when to use it, and how to get started. A complete beginner’s guide for aspiring data engineers.
What is Azure Data Factory? A Beginner’s Guide for Data Engineers Read More »
Prepare for your Azure Data Factory interview with 15 real questions and detailed answers. Covers pipelines, activities, metadata-driven patterns, incremental loading, error handling, triggers, and performance optimization.
Top 15 Azure Data Factory Interview Questions Every Data Engineer Should Know Read More »
