Performing multi-table incremental loads or delta loads from SQL to Blob Storage in Azure Data Factory involves a series of steps. Here's a high-level overview of how you can achieve this:
1. Set up Linked Services:
SQL Database Linked Service: Create a linked service in Azure Data Factory to connect to your SQL Database. This connection will allow ADF to access the data.
Azure Blob Storage Linked Service: Set up a linked service to your Azure Blob Storage where you want to store the incremental data.
2. Create Datasets:
SQL Dataset: Define datasets in ADF for each SQL table you want to extract data from. Configure these datasets to connect to the respective tables in your SQL Database.
Blob Storage Dataset: Create datasets for Blob Storage, specifying the destination location for storing incremental data.
3. Incremental Load Strategy:
Identify Incremental Columns: Determine the columns in your SQL tables that signify changes or updates (e.g., modified date, timestamp).
Tracking Changes: Use these columns to track incremental changes. For example, you might use a timestamp column to identify rows that have been updated since the last load.
4. Pipeline Activities:
Copy Activity: Create a pipeline in Azure Data Factory that uses the Copy Activity to extract data from SQL tables and copy it to Blob Storage.
Source: Use the SQL Dataset as the source in the Copy Activity.
Sink: Configure the Blob Storage Dataset as the sink for storing the data.
Incremental Load Logic: Implement logic (via SQL query or dynamic filtering) in the source dataset to retrieve only rows with changes since the last load.
Mapping and Transformations: Perform any necessary mapping or transformations during the data movement process.
5. Scheduled Trigger or Incremental Detection:
Scheduled Triggers: Set up a scheduled trigger in Azure Data Factory to execute the pipeline at specified intervals (e.g., daily, hourly) to perform incremental loads.
Incremental Detection Logic: Implement logic within your pipeline to detect the latest modified or created records since the last load. This can involve using parameters, variables, or a lookup activity to identify the incremental data range.
6. Error Handling and Monitoring:
Error Handling: Implement error handling mechanisms within the pipeline to handle any issues that might occur during data extraction or movement.
Monitoring: Monitor the pipeline runs and data movement activities within Azure Data Factory to ensure successful execution and track any failures.
Implementing a multi-table incremental load or delta load from SQL to Blob Storage in Azure Data Factory involves configuring linked services, defining datasets, setting up the pipeline with the Copy Activity, implementing incremental load logic, scheduling triggers, and ensuring error handling and monitoring for successful data movement. The key is to identify and track incremental changes effectively while leveraging ADF's capabilities for data movement and transformation.
Azure Data Factory (ADF) is a cloud-based data integration service provided by Microsoft on the Azure platform. It allows you to create, schedule, and manage data pipelines for transforming and processing data from various sources. Here's an overview:
Key Components of Azure Data Factory:
Pipelines: ADF uses pipelines to define workflows that orchestrate the movement and transformation of data. Pipelines consist of activities that perform specific tasks like data copying, transformation, or calling external services.
Activities: These are the building blocks within pipelines that represent individual tasks such as data movement, data transformation, data processing, or control activities.
Datasets: Datasets represent the data structures within data stores. They define the schema and location of the data to be used as inputs or outputs for activities within pipelines.
Linked Services: Linked services define connections to various data sources and destinations. They contain connection information and credentials required to connect to external resources like databases, storage accounts, or APIs.
Triggers: Triggers enable the scheduling and execution of pipelines based on time schedules, data events, or external events.
Common Use Cases:
Data Movement: Copying data between different data stores such as moving data from an on-premises database to Azure Blob Storage.
Data Transformation: Transforming data from one format to another, like converting CSV files to Parquet format or performing transformations using mapping data flows.
Data Orchestration: Orchestrating complex workflows that involve multiple tasks, dependencies, and conditional execution logic.
Data Processing: Running compute-intensive operations on data, such as running analytics, machine learning models, or aggregations.
Hybrid Data Integration: Working with data from on-premises and cloud sources to create unified data solutions.
Azure Data Factory UI and Components: