Data aggregation in ETL (Extract, Transform, Load) processes involves summarizing raw data into meaningful metrics during the Transform phase. This step reduces data volume by combining records based on common attributes (e.g., dates, categories) and applying calculations like sums, averages, or counts. Aggregation ensures that downstream systems, such as data warehouses or reporting tools, receive concise, actionable insights instead of raw transactional data. For example, daily sales transactions might be aggregated into monthly revenue totals per region.
During aggregation, the ETL pipeline groups data using keys or dimensions. Suppose a retail company extracts sales records from multiple databases. The Transform phase could group sales by region and month, then calculate total revenue and average order_size for each group. Tools like SQL’s GROUP BY or Apache Spark’s aggregation functions are commonly used here. Aggregation logic must account for data quality—handling missing values or duplicates—to ensure accuracy. Performance is also critical: processing large datasets often requires optimizations like partitioning data or using in-memory processing to avoid bottlenecks.
A practical example is aggregating website clickstream data. Raw logs containing user IDs, timestamps, and page visits might be transformed into daily session counts or average time spent per page. This reduces terabytes of raw logs into gigabytes of summarized data, making it easier to load into a dashboard. However, over-aggregation can lead to loss of granularity, so balancing summarization with retaining necessary detail is key. Properly implemented, aggregation in ETL enables efficient analysis while minimizing storage and compute costs.