Azure Data Factory — Data Integration at Scale

“Welcome back. Today we cover Azure Data Factory — Azure's cloud-native data integration service. Every enterprise has data scattered across dozens of systems: SQL databases, Salesforce, SAP, SharePoint, flat files, REST APIs. Data Factory is the orchestration layer that moves, transforms, and schedules data flows between all these systems. It's the backbone of modern enterprise data engineering in Azure.”

“Data Factory has a clear conceptual hierarchy. Pipelines contain activities — the individual steps of your data workflow. Linked Services define connections to data sources — your Azure SQL credentials, your Salesforce OAuth connection, your S3 access keys. Datasets describe the specific data within a linked service — the table name, the file path, the container name. Triggers define when pipelines run — on a schedule, when a file arrives, or manually. The Integration Runtime is the compute that executes the activities.”

“The Copy Activity is ADF's workhorse — it moves data from any source to any destination with high throughput. It supports over 100 data stores as both source and sink, including virtually every database, file system, SaaS application, and cloud storage service. Parallel copy uses multiple threads to maximize throughput — it can move terabytes of data in minutes. Schema mapping handles column name differences between source and destination. Fault tolerance can skip malformed rows and log them for review rather than failing the entire pipeline.”

“Data Flows provide a visual drag-and-drop designer for data transformations. You connect transformation nodes — source, filter, join, aggregate, sort, sink — in a canvas without writing code. Debug mode is invaluable — click any node and see a sample of the data flowing through at that point. Under the hood, Data Flows generate Apache Spark code that runs on ADF's managed Spark clusters, so they scale to large datasets automatically. For teams without deep code skills, Data Flows make complex transformations accessible.”

“Loading data is easy; loading it efficiently is an art. Full loads copy everything every time — simple but creates unnecessary load and cost. Incremental loading copies only records that changed since the last run. The watermark pattern stores the maximum timestamp from the last run and queries only records with a later timestamp. Change Data Capture reads the database transaction log to capture inserts, updates, and deletes with no impact on the source system. For large production data pipelines, incremental loading is essential.”

“ADF offers several trigger types. Schedule triggers run pipelines on a cron schedule. Tumbling window triggers process data in non-overlapping time windows — perfect for hourly or daily batch processing with built-in backfill if a run fails. Event triggers respond to storage events — a new file appearing in a container automatically triggers processing. Within pipelines, activities can branch on success or failure — retry a failed activity, send an alert email, or run a cleanup activity on failure.”

“Let me walk through building a real data pipeline. I'll create Data Factory, configure linked services for Azure SQL and Blob Storage, build a pipeline that copies SQL table data to Parquet format in a Data Lake, add a Data Flow to filter and aggregate the data, set a daily trigger, and run it manually to watch the execution. ADF's monitoring interface shows you exactly how long each activity took and how many rows were processed.”

“Azure Data Factory is the glue that holds enterprise data architectures together. Whether you're building a simple nightly batch job or a complex real-time data mesh, ADF handles the orchestration. Next we cover Azure Databricks — the premium Apache Spark platform for data engineering and machine learning, combining the power of Databricks with the enterprise capabilities of Azure.”