Incremental ETL, the most Efficient in terms of Time, Resources, and Bandwidth Consumption

Data Extraction, Transformation, and Loading (ETL) is a critical process for businesses looking to maximize their information resources. However, as data volumes grow exponentially, traditional full ETL approaches become inefficient and costly. This is where Incremental ETL comes into play—a strategy most companies need to adopt.

What is Incremental ETL?

Incremental ETL differs from other types of ETL, such as Full ETL and Rebuild ETL:

• Full Load ETL:

  • This is the traditional ETL approach where all data from the source systems is extracted in every run of the process.
  • It involves loading all the data, whether or not it has changed.
  • It’s useful when the source data has undergone massive changes or when a full refresh is required.
  • However, for large datasets with only incremental changes, it can be inefficient and costly.

• Incremental ETL:

  • In this approach, instead of processing all data from scratch each time, only the new or modified data since the last process run is extracted, transformed, and loaded.
  • This significantly reduces the time and resources needed while keeping the data updated and synchronized.
  • It’s essential for environments with large, constantly changing datasets.

• Rebuild ETL:

  • This is a hybrid approach that combines elements of both Full ETL and Incremental ETL.
  • It first performs a full ETL to create a data baseline.
  • Subsequent runs perform incremental ETL to capture changes.
  • This is useful when an initial full load is required, followed by incremental updates.

The key difference lies in how data is handled: Full ETL loads everything, Incremental ETL loads only new/modified data, and Rebuild ETL combines both approaches. The choice depends on the specific use case, data volume, and frequency of changes in the source data.

Key Benefits of Incremental ETL

• Reduced Processing Time: By handling only new data, ETL processes run much faster.
• Lower Resource Usage: Requires less computing power, storage, and network bandwidth.
• Data Consistency: Data stays up to date without replacing entire datasets.
• Scalability: As data volumes grow, Incremental ETL becomes essential to maintaining manageable processes.

Microsoft Fabric: Enabling Incremental ETL

Microsoft Fabric is a comprehensive data analytics platform that integrates data lakes, data storage, and analytics in one solution. Fabric offers key features that make Incremental ETL easier to implement:

• Data Flows Gen 2: Enables efficient data loading into a data lake, with features like incremental loading and query folding to optimize performance.
• Data Warehouse: Stores structured data and allows SQL procedures to incrementally update fact tables with new data.
• Data Pipelines: Orchestrates and automates incremental ETL workflows, running Data Flows and SQL procedures in the right order.
• Integration with Power BI: Combined data models in the Data