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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
Lookup and Get Metadata Activities in ADF
Lookup and Get Metadata are how an Azure Data Factory pipeline finds things out before it acts. This guide explains what each returns, the shape of their outputs, the documented limits of 5000 rows and 4 MB, and the patterns they enable: a loop driven by a control table, a watermark, a file check and a folder listing.
ADF Logging and Auditing for Compliance
An audit asks four things of a data pipeline: what ran, who changed it, what data moved and who had access. This guide maps each question to the record Azure Data Factory can produce, its retention, and the settings that must be on before the evidence exists, with a session log analysed as an example.
Building a Metadata Framework for ADF Pipelines
A control table and a ForEach copy many tables. A framework is what makes that safe to operate: configuration you can validate, a log of every load, stages that run in order, and reruns that resume instead of repeating. This guide designs each part for Azure Data Factory, with the restart logic shown working.
Loading Data into Synapse with ADF
Loading a Synapse SQL pool row by row is slow by design. This guide explains the three load methods the Azure Data Factory Copy activity offers, the COPY statement, PolyBase and bulk insert, when a load is direct and when it needs staging, the settings for each, upserts, and the documented limits.
Connecting ADF to REST APIs and Web Sources
Azure Data Factory has four ways to talk to a web endpoint, and choosing the wrong one is the usual first mistake. This guide separates the REST connector, the HTTP connector, the Web activity and the Webhook activity, explains pagination rules with a working simulation, and lists the documented limits.
Data Flow Debug and Performance in ADF
Debug mode lets you watch a data flow work on live data, and it bills like a running cluster. This guide covers what debug does and does not check, the four places a data flow loses time, cluster size and time to live, partitioning and skew measured on real data, and the pipeline designs that waste clusters.