What is ELT
ELT (Extract, Load, Transform) is a data pipeline approach where raw data is pulled from source systems (Extracted), loaded into a target system (Loaded), and transformed inside that system rather than before loading.
How It Works
Unlike traditional ETL, where data is transformed before being loaded into its final destination, ELT loads raw data first—often into a data lake or warehouse. Once there, you use the computing power of the target system to transform the data as needed. This approach can make the pipeline simpler in some cases, because you ingest everything upfront, then transform subsets or re-transform as new requirements arise.
Technical Details
ELT pipelines often rely on cloud-based data lakes or warehouses (e.g., Snowflake, BigQuery) that can handle large volumes of raw data. The transformations are done with SQL, Python, or specialized frameworks once the data is already inside the warehouse. This can reduce duplication of effort if the transformations need to change, since the raw data is still present. However, it also means you need enough computing power and storage in your warehouse to handle unfiltered, unprocessed data.
How to Write It
Basic Syntax
-- Simplified ELT flow:
-- 1. Extract raw data from a source (e.g., an external API or raw file).
-- 2. Load raw data into a data lake or warehouse in its native form.
-- 3. Transform data in the warehouse, often via SQL or Python,
-- creating cleaned or aggregated tables.Learn More
Best Practices
- Load data in a well-organized structure (separate raw and transformed schemas).
- Use versioning or immutability for raw data to revisit earlier versions if transformation logic changes.
- Automate transformations with scheduled tasks or orchestrators (e.g., Airflow, dbt).
- Leverage the analytics warehouse as a central place for data transformations, queries, and governance.
Common Pitfalls
- Loading large amounts of unfiltered data can drive up storage and compute costs.
- Relying solely on the warehouse for transformations can slow query performance if not optimized.
- Keeping raw data forever may become unwieldy if lifecycle policies are not defined.
- Not clearly labeling or separating raw data from transformed data might confuse analysts.
Advanced Tips
- Adopt a "data lakehouse" strategy, using a single platform for both raw storage and structured transformations.
- Implement cluster or partition pruning to optimize queries on large tables.
- Use orchestration tools (e.g., Airflow, Dagster) to chain transformations into reproducible workflows.
- If real-time analytics is required, consider micro-batching or streaming solutions in parallel with ELT.
