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The most useful guides in this topic, not just the most recent.
Azure Data Factory Guides & Tutorials
Azure Data Factory end to end — pipelines, linked services, triggers, copy activity, mapping data flows and Synapse.
Azure Data Factory articles
ADF Data Flow Transformations: A Complete Reference
A reference to the transformations available in Azure Data Factory mapping data flows, grouped by task: shaping columns, filtering and marking rows, combining streams and handling nested data. Each has its SQL equivalent and the documented detail worth knowing, with row counts from a real example.
Microsoft Fabric vs Azure Data Factory: What's New
Microsoft calls Fabric Data Factory the next generation of Azure Data Factory. This guide maps each Data Factory concept to its Fabric counterpart, lists what Fabric adds and what its documentation says is still missing, explains the move from pay-as-you-go to capacity billing, and sets out the three upgrade paths.
Azure Data Engineering Projects for Your Portfolio
Five Azure data engineering projects for a portfolio, each on real public data and each built to demonstrate one skill an interviewer will ask about: metadata-driven ingestion, incremental loading, Spark cleaning with quality rules, a dimensional model with history, and event-driven processing with alerts.
Handling Errors and Retries in ADF Pipelines
Error handling in Azure Data Factory has a trap: a pipeline can catch a failure, send an alert and still be reported as succeeded. This guide covers retry and timeout settings, the four conditional paths, the rule that decides a pipeline's status, modelled across four patterns, and the Fail activity.
Parameterizing Pipelines in Azure Data Factory
Parameters turn a pipeline that copies one table into a pipeline that copies any table. This guide explains the four places parameters live in Azure Data Factory, how a value travels from a trigger down to a connection, a worked generic copy in JSON, and where parameterising goes too far.
Pipeline Dependencies and Scheduling in ADF
Scheduling in Azure Data Factory works at three levels: dependencies between activities, pipelines calling pipelines, and triggers. This guide covers the four dependency conditions, Execute Pipeline, the three trigger types and how they differ, schedule recurrence and time zones, and patterns for ordering work.