Mapping data flow video tutorials

APPLIES TO: Azure Data Factory Azure Synapse Analytics

Below is a list of mapping data flow tutorial videos created by the Azure Data Factory team.

As updates are constantly made to the product, some features have added or different functionality in the current Azure Data Factory user experience.

Getting Started

Getting started with mapping data flows in Azure Data Factory

Debugging and developing mapping data flows

Debugging and testing mapping data flows.

Data exploration

Data preview quick actions

Monitor and manage mapping data flow performance

Benchmark timings

Debugging workflows for data flows

Updated monitoring view

Transformation overviews

Aggregate transformation

Alter row transformation

Derived Column transformation

Join transformation

Self-join pattern

Lookup transformation

Lookup Transformation Updates & Tips

Pivot transformation

Pivot transformation: mapping drifted columns

Select transformation

Select transformation: Rule-based mapping

Select transformation: Large Datasets

Surrogate key transformation

Union transformation

Unpivot transformation

Window Transformation

Filter Transformation

Conditional Split Transformation

Exists Transformation

Dynamic Joins and Dynamic Lookups

Flatten transformation


Stringify transformation

External Call transformation

Transform hierarchical data

Rank transformation

Cached lookup

Row context via Window transformation

Parse transformation

Transform complex data types

Output to next activity

Stringify transformation

External Call transformation

Assert transformation

Log assert error rows

Fuzzy join

Source and sink

Reading and writing JSONs

Parquet and delimited text files

CosmosDB connector

Infer data types in delimited text files

Reading and writing partitioned files

Transform and create multiple SQL tables

Partition your files in the data lake

Data warehouse loading pattern

Data lake file output options

Optimizing mapping data flows

Data lineage

Iterate files with parameters

Decrease start-up times

SQL DB performance

Logging and auditing

Dynamically optimize data flow cluster size at runtime

Optimize data flow start-up times

Azure Integration Runtimes for Data Flows

Quick cluster start-up time with Azure IR

Mapping data flow scenarios

Fuzzy lookups

Staging data pattern

Clean addresses pattern


Merge files

Slowly changing dimensions type 1: overwrite

Slowly changing dimensions type 2: history

Fact table loading

Transform SQL Server on-prem with delta data loading pattern


Distinct row & row counts

Handling truncation errors

Intelligent data routing

Data masking for sensitive data

Logical Models vs. Physical Models

Detect source data changes

Generic type 2 slowly changing dimension

Delete rows in target when not present in source

Incremental data loading with Azure Data Factory and Azure SQL DB

Transform Avro data from Event Hubs using Parse and Flatten

Data flow expressions

Date/Time expressions

Splitting Arrays and Case Statement

Fun with string interpolation and parameters

Data Flow Script Intro: Copy, Paste, Snippets

Data Quality Expressions

Collect aggregate function

Dynamic expressions as parameters

User-defined functions


Metadata validation rules