Optimizing "Data Transfer" and "Data Transformation" in ADF: Filtering Even Customer IDs from CSV to SQL
Arpit TyagiTable of contents
- Step 1: Inspecting the CSV File in Data Lake: Your First Step to Data Optimization
- Step 2: Configuring the Data Flow Source: Pointing to the Customer.CSV File
- Step 3: Filtering Even Customer IDs: Streamlining Data with ADF's Filter Data Flow
- Step 4: Integrating Data Flow into a Pipeline: Directing Data to SQL's EvenCustomer Table
- Step 5: Pipeline Execution Success: Ensuring Smooth Data Transfer
- Step 6: Data Flow Success: Confirming Effective Data Transformation
- Step 7: Verifying SQL Database Entries: Ensuring Accurate Even Customer IDs
Step 1: Inspecting the CSV File in Data Lake: Your First Step to Data Optimization
Step 2: Configuring the Data Flow Source: Pointing to the Customer.CSV File
Step 3: Filtering Even Customer IDs: Streamlining Data with ADF's Filter Data Flow
Step 4: Integrating Data Flow into a Pipeline: Directing Data to SQL's EvenCustomer Table
Step 5: Pipeline Execution Success: Ensuring Smooth Data Transfer
Step 6: Data Flow Success: Confirming Effective Data Transformation
Step 7: Verifying SQL Database Entries: Ensuring Accurate Even Customer IDs
In conclusion, optimizing data transfer and transformation in Azure Data Factory (ADF) can significantly enhance the efficiency of data workflows. By following the outlined steps, we successfully filtered even customer IDs from a CSV file and transferred them to a SQL database. This process not only ensures data accuracy but also streamlines data management tasks. The successful execution of both the pipeline and data flow confirms the effectiveness of this approach, providing a reliable method for handling similar data transformation tasks in the future.
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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.