A typical ETL (Extract, Transform, Load) architecture for a data warehouse consists of three core stages, supported by tools and processes to ensure reliable data flow. At a high level, data is pulled from source systems, processed into a usable format, and loaded into the warehouse. The design often includes a staging area for raw data, transformation logic to standardize and clean data, and orchestration to manage workflows. Scalability, error handling, and integration with source/target systems are key considerations.
In the extract phase, data is collected from diverse sources such as transactional databases (e.g., MySQL, PostgreSQL), cloud applications (e.g., Salesforce), flat files (CSV, JSON), or APIs. A staging area—often a cloud storage bucket (e.g., Amazon S3) or a relational database—temporarily holds raw, unprocessed data. This decouples extraction from downstream steps, allowing retries without reprocessing source systems. For example, an e-commerce company might extract order data from an OLTP database, customer logs from JSON files, and ad metrics from Google Analytics APIs. Incremental extraction (fetching only new/changed data) is often used to reduce load on source systems.
During the transform phase, raw data is cleaned, validated, and restructured. This includes deduplication (e.g., merging customer records), type conversion (e.g., strings to dates), and business logic (e.g., calculating revenue). Tools like Apache Spark or dbt handle complex transformations, while lightweight scripts may process smaller datasets. Data quality checks (e.g., ensuring non-null keys) and logging are critical here. For instance, a healthcare provider might standardize patient IDs across systems, mask sensitive data, and flag incomplete records for review. The transformed data is then mapped to the warehouse schema, which might use star/snowflake schemas for analytical efficiency.
The load phase writes the processed data into the warehouse (e.g., Snowflake, BigQuery). This can be a full load (replacing entire tables) or incremental load (appending updates). Orchestration tools like Apache Airflow or AWS Step Functions manage dependencies—for example, ensuring customer data loads before sales transactions. Post-load, indexes or materialized views optimize query performance. Monitoring (e.g., failed job alerts) and security practices (encryption, access controls) wrap the process. A retail company might schedule nightly loads to update inventory metrics, with validation checks to reconcile warehouse totals against source systems.