What is ETL?
ETL stands for Extract, Transform, Load. It’s the three-step process for moving data from where it lives (CRMs, databases, SaaS tools) to where it needs to go (data warehouses, analytics platforms, operational systems):
- Extract — pull data from source systems via APIs, database queries, or file exports.
- Transform — reshape, clean, join, and enrich the data to match the destination’s schema.
- Load — write the transformed data into the target system.
ELT (Extract, Load, Transform) is a variation where raw data lands in the warehouse first and transformation happens in-place using SQL — common with modern cloud warehouses like Snowflake, BigQuery, and Databricks that have the compute to handle it.
Why it matters
Businesses run on data spread across dozens of systems. Sales data is in Salesforce. Financial data is in NetSuite. Product data is in your database. None of those systems talk to each other natively, and none of them are built for analytics. ETL is the plumbing that gets data into one place so teams can actually use it.
The failure mode is stale data. Batch ETL jobs that run nightly mean analytics teams work with yesterday’s numbers. Change data capture is the modern answer to that — streaming changes in real time rather than re-pulling everything on a schedule.
The other failure mode is brittle pipelines. When a vendor changes their API, hand-built ETL breaks. Managed connector libraries handle that maintenance so engineering teams don’t have to.
ETL at Tray.ai
Data Integration in Tray.ai handles the full data movement story — real-time syncs, CDC, batch pipelines, and in-flight transformation via the SQL Transformer. Pull from Salesforce, Zendesk, Stripe, and more into Snowflake, Redshift, BigQuery, or Databricks without building custom pipelines.
IBM used it to cut a 9M-record data pull from 8 hours to 5 minutes. See the IBM story.