Azure Data Factory CDC: A Comprehensive Guide

Azure Data Factory CDC is a powerful tool for integrating and transforming data from various sources. It allows you to replicate data from on-premises sources to the cloud, enabling real-time analytics and business intelligence.

Azure Data Factory CDC uses Change Data Capture (CDC) technology to track changes to data in real-time, reducing latency and ensuring data consistency. This is particularly useful for applications that require up-to-the-minute data, such as financial trading platforms.

Azure Data Factory CDC supports a wide range of data sources, including relational databases, big data stores, and cloud-based services. This flexibility makes it an attractive choice for organizations with diverse data ecosystems.

Azure Data Factory CDC is a feature that identifies and captures changes made to data in source data sources.

This feature was recently announced by Microsoft, and it's now available in Azure Data Factory (ADF) and Azure Synapse Pipelines designer mode as a factory resource.

CDC stands for Change Data Capture, which refers to the process of delivering changes in real time to a downstream process or system.

The goal of CDC is to provide real-time data integration and processing, allowing organizations to respond quickly to changing data.

By using CDC, organizations can reduce the latency associated with traditional data integration methods and improve the accuracy of their data.

Azure Data Factory's Change Data Capture (CDC) capabilities have been updated to offer real-time change tracking of data in Azure SQL databases. This feature captures changes made to a table in the source database and writes them to a separate change table in the same database.

The updated CDC capabilities in ADF don't require a pipeline to run, making it a top-level resource that can be easily configured. No trigger setting is required, and CDC runs continuously, maintaining checkpoints and watermarks automatically.

You can use Native CDC in Mapping Data Flow to capture and process changes in your data sources in real-time. This improves the efficiency and speed of your data integration process, reducing the amount of data that needs to be processed.

Native CDC supports several connectors for change data capture, including Azure SQL Database, Azure Synapse Analytics, Azure Cosmos DB, and Azure Blob Storage. These connectors allow you to capture changes made to your data sources in real-time, making it easier to integrate them with other data sources.

To implement Change Data Capture, you can use either the incremental column approach or the database-maintained change log-based approach. The incremental column approach detects changed records using a specific column, while the database-maintained change log-based approach uses the internal change log maintained by the database.

Here are the sources supported for each approach:

Database-maintained change log-based approach:

Implementing Change Data Capture (CDC) in Azure Data Factory (ADF) offers numerous benefits. It's an efficient way to move data across components and networks in near real-time, keeping systems in sync.

One of the key benefits of CDC is that it enables incremental load, eliminating the need for bulk load. This reduces the amount of data that needs to be transferred, resulting in less cloud cost and less time spent on data processing.

Here are some of the benefits of implementing CDC:

To implement CDC in ADF, you need to choose the right data source, such as Azure SQL Database, and select an incremental column to track changes. You also need to store CDC metadata, implement error handling and retry logic, and monitor performance to ensure that changes are captured and processed in a timely manner.

Implementation in ADF is a breeze, thanks to the intuitive interface and streamlined process. You can configure a CDC process without designing graphs or learning triggers.

To start, open a new CDC mapping flow in ADF and define your source. For this example, we'll use the Azure SQL Database as a source, but you can choose from other sources like DelimitedText File, Parquet, or Avro.

The first step is to select an incremental column, which requires identification of the changes you're making. In our case, we choose a column that will be used for identifying changes.

You can also use a database-maintained change log-based approach, which doesn't require any column to identify changes. This option uses the internal change log maintained by the database.

For database change log-based CDC, Azure Data Factory currently supports the following sources: Azure SQL Database, SQL Server, Azure SQL Managed Instance, Azure Cosmos DB (SQL API), and SAP CDC.

To implement CDC, first create a new mapping data flow and select the source. Once you choose the source dataset, go to Source Options and check the Change Data Capture checkbox.

There are two options within this: incremental column and database-maintained change log-based approach. For incremental column, you must select a column that will be used for identifying changes.

Here are some supported sources for incremental column: Azure Blob Storage, ADLS Gen2, ADLS Gen1, Azure SQL Database, SQL Server, Azure SQL Managed Instance, Azure Database for MySQL, Azure Database for PostgreSQL, and Common data model.

For database change log-based CDC, you only need to enable the SQL Server CDC and select the target table in sink transformation. Then, automatically changes from the source table will be transmitted to the target table.

Here are some best practices for implementing Change Data Capture:

1. Choose the right data source: Choose a data source that supports CDC.

2. Use a unique identifier: Make sure to include a unique identifier in your data source that can be used to track changes.

3. Store CDC metadata: Store the metadata that is generated by CDC in a persistent store.

4. Use appropriate technology: Use appropriate technology for your CDC implementation.

5. Implement error handling: Implement error handling and retry logic in your CDC pipeline.

6. Monitor performance: Monitor the performance of your CDC pipeline.

7. Consider data privacy: Consider data privacy and security implications when implementing CDC.

To implement CDC in ADF, you can follow these steps:

1. Open a new CDC mapping flow in ADF.

2. Define your source, such as Azure SQL Database.

3. Select an incremental column or use a database-maintained change log-based approach.

4. Map the source to a target table.

5. Set the latency, which can be set to 15 minutes or 2 hours.

6. Publish and start the CDC process.

Note: As of today, the latency options available are 15 minutes to 2 hours, but soon you'll be able to track CDC in real-time with less than one minute latency.

The benefits of using a CDC approach in cloud data architecture are numerous. One of the key advantages is that it enables incremental load, eliminating the need for bulk loading.

This approach also requires less networking bandwidth, which can lead to significant cost savings. In fact, CDC can help reduce cloud costs.

With CDC, data can be extracted, loaded, and transformed in near real-time, resulting in better performance and less time spent on data processing. This is especially beneficial for large datasets.

Here are some of the key benefits of using CDC in cloud data architecture: