[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-etl::en":3,"gloss-cluster-etl::en":20,"gloss-next-etl::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"etl","data-infra","ETL","ETL — Extract, Transform, Load — is the traditional pattern for moving data from operational source systems into an analytical destination like a data warehouse. As the name states, it's three ordered stages: Extract pulls raw data from source systems (production databases, third-party APIs, SaaS tools like Stripe or HubSpot, log files); Transform reshapes that raw data into a clean, consistent, analysis-ready format (standardizing date formats, joining reference tables, computing derived fields, filtering out test\u002Finternal records) while it's still in transit, before it ever touches the destination; and Load writes the finished, transformed data into the destination system. Why it matters for AI\u002FSaaS builders: ETL (and its increasingly common inverse, ELT) is the plumbing behind every internal analytics dashboard, every \"your usage this month\" customer-facing report, and every dataset used to train or fine-tune a custom model on a company's own data. Getting it wrong — silently dropping rows, double-counting on retry, letting schema drift break downstream reports — is one of the most common causes of \"the numbers don't match\" fire drills in a growing SaaS company. How it works: modern data teams increasingly favor ELT (Extract, Load, Transform) over classic ETL — load raw data into the warehouse first, then transform it there using the warehouse's own compute (via SQL and tools like dbt), rather than transforming in a separate processing layer before load. This shift happened because modern cloud warehouses (Snowflake, BigQuery, Redshift) got cheap and powerful enough to make in-warehouse transformation more flexible than pre-load transformation, and it keeps a copy of the untransformed raw data available if a transformation bug is discovered later — you can just re-run the transform, not the whole extract. Tools like Fivetran and Airbyte standardize the extract-and-load legs for hundreds of common SaaS data sources (Stripe, Salesforce, Postgres, Google Ads) so engineering teams don't hand-write API integration code for each one. Worked example: an AI analytics SaaS needs nightly usage data from Stripe (subscriptions, invoices) and its own product database (feature usage events) combined into one warehouse table for a customer-facing \"ROI dashboard.\" An Airbyte connector extracts raw Stripe data into BigQuery every night; a dbt model then transforms it — joining subscription records to usage events by customer ID, computing \"cost per feature used,\" and materializing the result as a `customer_roi_summary` table — which the dashboard queries directly. When Stripe adds a new invoice status value, the raw extract keeps working (ELT tolerates schema drift at the extract stage) and only the dbt transform needs a one-line update to handle it.","ETL (Extract, Transform, Load) is the classic three-stage pattern for moving data from source systems into a warehouse, transforming it in-flight.",null,[11,14,17],{"slug":12,"name":13},"batch-processing","Batch Processing",{"slug":15,"name":16},"data-pipeline","Data Pipeline",{"slug":18,"name":19},"data-warehouse","Data Warehouse",[21,25,28,31,32,36,39,42,45,48,52,55],{"slug":22,"category":5,"name":23,"updated_at":24},"acid","ACID","2026-08-24T02:46:37+00:00",{"slug":26,"category":5,"name":27,"updated_at":24},"ann-search","ANN Search",{"slug":29,"category":5,"name":30,"updated_at":24},"backpressure","Backpressure",{"slug":12,"category":5,"name":13,"updated_at":24},{"slug":33,"category":5,"name":34,"updated_at":35},"bm25","BM25","2026-08-24T02:46:38+00:00",{"slug":37,"category":5,"name":38,"updated_at":24},"cache","Cache",{"slug":40,"category":5,"name":41,"updated_at":24},"cap-theorem","CAP Theorem",{"slug":43,"category":5,"name":44,"updated_at":24},"change-data-capture","Change Data Capture (CDC)",{"slug":46,"category":5,"name":47,"updated_at":24},"chroma","Chroma",{"slug":49,"category":5,"name":50,"updated_at":51},"chunk-overlap","Chunk Overlap","2026-08-24T03:30:02+00:00",{"slug":53,"category":5,"name":54,"updated_at":24},"columnar-storage","Columnar Storage",{"slug":56,"category":5,"name":57,"updated_at":24},"connection-pooling","Connection Pooling"]