Azure - DriveDataScience

Microsoft Fabric for Data Engineers: What It Is, What It Replaces, How It Competes, and Why It Matters

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The complete guide to Microsoft Fabric for data engineers. What it is, all 7 workloads explained, OneLake as the universal storage layer, what Azure services it replaces (13-row mapping table), how our blog pipelines translate to Fabric, head-to-head comparisons with Databricks and Snowflake and AWS, Direct Lake mode for Power BI, the DP-700 certification, capacity-based pricing, migration path, and when to use Fabric vs Databricks vs both.

Microsoft Fabric for Data Engineers: What It Is, What It Replaces, How It Competes, and Why It Matters Read More »

How Real Companies Receive Data: SFTP, APIs, CDC, Event Streaming, and Every Ingestion Pattern Explained

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How data actually arrives in production — not from tutorials, from real companies. Six ingestion patterns: SFTP file drops, REST API pulls, CDC database replication, event streaming, direct cloud drops, and third-party tools. Complete architectures for banking, e-commerce, telecom, healthcare, retail, and insurance with exact data flow diagrams.

How Real Companies Receive Data: SFTP, APIs, CDC, Event Streaming, and Every Ingestion Pattern Explained Read More »

CI/CD for Azure Data Factory and Synapse: ARM Templates, Environment Promotion, and the Complete Hands-On Guide

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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.

CI/CD for Azure Data Factory and Synapse: ARM Templates, Environment Promotion, and the Complete Hands-On Guide Read More »

Databricks Git Integration and CI/CD: Repos, Branching, Notebook Versioning, and Deploying Across Environments

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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.

Databricks Git Integration and CI/CD: Repos, Branching, Notebook Versioning, and Deploying Across Environments Read More »

File Storage in Azure Databricks: Volumes, DBFS, /tmp/, External Locations, and Where Your Files Actually Live

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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.

File Storage in Azure Databricks: Volumes, DBFS, /tmp/, External Locations, and Where Your Files Actually Live Read More »

Data Quality in Azure Databricks: Validation Rules, Quarantine Patterns, and Building a DQ Framework from Scratch

Leave a Comment / Azure, Data Engineering, Python

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.

Data Quality in Azure Databricks: Validation Rules, Quarantine Patterns, and Building a DQ Framework from Scratch Read More »

Databricks Workflows and Jobs: Scheduling, Multi-Task Pipelines, Alerts, and Production Orchestration

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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.

Databricks Workflows and Jobs: Scheduling, Multi-Task Pipelines, Alerts, and Production Orchestration Read More »

The Medallion Architecture in Azure: Bronze, Silver, and Gold Layers Explained with Real Pipelines

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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.

The Medallion Architecture in Azure: Bronze, Silver, and Gold Layers Explained with Real Pipelines Read More »

PySpark Window Functions Deep Dive: ROW_NUMBER, RANK, LAG, LEAD, Running Totals, and Real-World Patterns

Leave a Comment / Azure, Data Engineering, Python

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.

PySpark Window Functions Deep Dive: ROW_NUMBER, RANK, LAG, LEAD, Running Totals, and Real-World Patterns Read More »

SCD Type 1 and Type 2 Using PySpark and Delta Lake MERGE in Azure Databricks

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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 »