Mastering Slowly Changing Dimensions (SCD) Type 1 with Azure Data Factory: A Step-by-Step Guide
Arpit TyagiTable of contents
- Step 1: Setting Up Your Azure SQL Database for SCD Type 1. Create the emp_scdtype1 table in Azure SQL Database.
- Step 2: Populating Your Table: Adding Initial Data Entries.
- Step 3: Visualizing Data: Confirming Table Entries
- Step 4: Data Lake Insights: Reviewing New File Entries.
- Step 5: Dataset Creation: Building the Foundation for Data Flow
- Step 6: Configuring Data Movement: Source and Sink Setup.
- Step 7: Executing the Pipeline: Ensuring a Successful Run.
- Step 8: Finally, verifying the result in SQL DB to check whether entries have been modified as per SCD Type 1.
(SCD Type 1 implementation via ADF)
Step 1: Setting Up Your Azure SQL Database for SCD Type 1. Create the emp_scdtype1 table in Azure SQL Database.
Step 2: Populating Your Table: Adding Initial Data Entries.
Step 3: Visualizing Data: Confirming Table Entries
Step 4: Data Lake Insights: Reviewing New File Entries.
Step 5: Dataset Creation: Building the Foundation for Data Flow
Step 6: Configuring Data Movement: Source and Sink Setup.
Step 7: Executing the Pipeline: Ensuring a Successful Run.
Step 8: Finally, verifying the result in SQL DB to check whether entries have been modified as per SCD Type 1.
In conclusion, mastering Slowly Changing Dimensions (SCD) Type 1 using Azure Data Factory involves a systematic approach to managing and updating data. By following the step-by-step guide, you can effectively create and manage datasets, define source and sink in copy activities, and verify the results in your SQL database. This process ensures that your data remains accurate and up-to-date, reflecting the most current information without retaining historical data. Azure Data Factory provides a robust platform for implementing SCD Type 1, making it an essential tool for data management and transformation in modern data solutions.
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Written by
Arpit Tyagi
Arpit Tyagi
Experienced Data Engineer passionate about building and optimizing data infrastructure to fuel powerful insights and decision-making. With a deep understanding of data pipelines, ETL processes, and cloud platforms, I specialize in transforming raw data into clean, structured datasets that empower analytics and machine learning applications. My expertise includes designing scalable architectures, managing large datasets, and ensuring data quality across the entire lifecycle. I thrive on solving complex data challenges using modern tools and technologies like Azure, Tableau, Alteryx, Spark. Through this blog, I aim to share best practices, tutorials, and industry insights to help fellow data engineers and enthusiasts master the art of building data-driven solutions.