Azure - DriveDataScience

Fabric Data Factory: Activities, Pipelines, Dataflow Gen2, Notebooks, and Building Production ETL in Microsoft Fabric

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The complete Fabric Data Factory guide. What changed from ADF (no datasets, connections instead of linked services, Dataflow Gen2 instead of Mapping Data Flows). All pipeline activities listed: data movement, transformation, control flow, notification (Teams, Outlook — NEW), and Fabric-specific (Semantic Model Refresh). Three complete pipeline examples including metadata-driven load and full Medallion ETL combining Copy + Dataflow Gen2 + Notebook + Power BI Refresh + Teams notification.

Fabric Data Factory: Activities, Pipelines, Dataflow Gen2, Notebooks, and Building Production ETL in Microsoft Fabric Read More »

OneLake Shortcuts in Microsoft Fabric: Every Source, Every Permission, and How to Access Data Without Copying It

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Master OneLake shortcuts in Fabric. Every supported source (ADLS Gen2, S3, S3-compatible, GCS, Dataverse, on-premises, Iceberg), read/write/delete behavior per source, the delete trap explained, two-layer security model, authentication methods per source, shortcut caching for cross-cloud cost savings, chained shortcuts, Direct Lake with shortcuts for Power BI, trusted workspace access for private ADLS, four real-world patterns, and step-by-step creation guide.

OneLake Shortcuts in Microsoft Fabric: Every Source, Every Permission, and How to Access Data Without Copying It Read More »

Microsoft Fabric Foundations: Capacity, Workspaces, Items, OneLake, and the Building Blocks Every Data Engineer Must Understand

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Master the building blocks of Microsoft Fabric. Capacity explained with the apartment building analogy, all F-SKU options with pricing, PAYG vs Reserved, pause/resume cost savings, the F64 threshold, workspaces and roles, all Fabric items listed and explained, Lakehouse vs Warehouse decision guide, OneLake storage and shortcuts, environment setup patterns, and the free 60-day trial.

Microsoft Fabric Foundations: Capacity, Workspaces, Items, OneLake, and the Building Blocks Every Data Engineer Must Understand Read More »

Azure Connections and Authentication for Data Engineers: Every Service, Every Method, and How to Remember Them All

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The Azure connections reference card for data engineers. Five authentication methods explained with building key analogies (master key, visitor badge, facial recognition, employee badge, full address). Every service covered: ADLS, SQL, Key Vault, Databricks, ADF, Fabric, Event Hubs, Power BI. Complete connection matrix, endpoint formats, connection strings, secure vs quick decision table, troubleshooting guide, and one-page cheat sheet.

Azure Connections and Authentication for Data Engineers: Every Service, Every Method, and How to Remember Them All Read More »

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

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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 »