ELTPipelines

ELTpipelines, or ELT pipelines, are data integration workflows that extract data from source systems, load it into a target data store such as a data warehouse or data lake, and perform transformations within the target environment. This approach contrasts with traditional ETL, where transformations occur before loading. ELT leverages the compute capabilities of modern cloud warehouses to transform large volumes of data after ingestion, enabling quicker ingestion and greater scalability.

Typical architecture includes sources, an extract layer, a landing or raw storage zone, a load step into the target warehouse or data lake, and a transformation layer built within the target environment. Transformation is often implemented with SQL-based models or scripts, with dbt (data build tool) being a common framework. Data quality checks, metadata management, and lineage tracking are integrated via orchestration and catalog tools.

Orchestrators such as Apache Airflow, Prefect, or Dagster schedule and monitor pipelines; the choice of warehouse (Snowflake, Google BigQuery, Amazon Redshift, Azure Synapse) determines performance characteristics and cost model. A typical pipeline includes incremental loads, handling schema evolution, and maintaining separate layers such as raw, cleaned, and curated data to support governance and reproducibility.

Benefits include scalability, faster data ingestion, reuse of raw data, and simplified transformation tracking with versioned models. Challenges include reliance on target warehouse performance, potential cost considerations, and the need for disciplined data governance and robust testing to prevent data quality issues.