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CDC ETL CODE

Many organizations have invested significantly in developing SSIS ETL packages for specific data tasks. Traditionally, SSIS has been the ETL tool of choice for many SQL Server data professionals for data transformation and loading.

CDC ETL CODE

The approach in this article uses the Data Factory SQL Server integration runtime to enable a lift and shift migration of existing databases into the cloud, while incorporating existing code and SSIS packages into the new cloud data workflow. A hybrid approach uses Data Factory as the primary orchestration engine, but continues to use existing SSIS packages to clean data and work with on-premises resources. To facilitate a lift and shift migration of an existing SQL database, a hybrid ETL approach provides a suitable option. Commonly used SSIS capabilities include Fuzzy Lookup and Fuzzy Grouping transformations, Change Data Capture (CDC), Slowly Changing Dimensions (SCD), and Data Quality Services (DQS). In other cases, the data load process requires complex logic or specific data tool components that aren't yet supported by Data Factory v2.

However, reworking existing ETL processes that are built with SSIS can be a migration roadblock. When you migrate your SQL Server databases to the cloud, you can realize tremendous cost savings, performance gains, added flexibility, and greater scalability. Installing paid or licensed custom components for the Azure-SSIS integration runtime might be a viable alternative to the hybrid approach. You can easily access the data by using standard ANSI SQL queries.ĭata Factory can invoke data cleansing procedures implemented by using other technologies, such as a Databricks notebook, Python script, or SSIS instance running in a virtual machine (VM).

Azure Synapse Analytics centralizes data in the cloud.

Data Factory is the cloud orchestration engine that takes data from multiple sources and combines, orchestrates, and loads the data into a data warehouse.

SQL Server Integration Services contains the on-premises ETL packages that are used to run task-specific workloads.

Blob Storage is used to store files and as a source for Data Factory to retrieve data.

The clean data is then loaded into tables in Azure Synapse Analytics.

After the data cleansing task finishes successfully, a copy task is run to load the clean data into Azure.

The data cleansing jobs are run to prepare the data for downstream consumption.

The Data Factory pipeline invokes a stored procedure to run an SSIS job that's hosted on-premises via the integration runtime.

Data is sourced from Azure Blob Storage into Data Factory.

Architectureĭownload a Visio file of this architecture. To incorporate existing SQL Server Integration Services (SSIS) packages into the new cloud data workflow, the solution uses the Data Factory integration runtime. The solution uses Azure Data Factory as the primary cloud-based extract, transform, and load (ETL) engine. This example scenario presents a hybrid solution for moving SQL Server databases to the cloud.