ETL is the plumbing that moves data between systems. Extract pulls data from sources (CRM, MAP, ad platforms, databases). Transform cleans, restructures, and enriches the data (renaming fields, joining tables, calculating metrics). Load writes the processed data into the destination (typically a data warehouse).
In modern data stacks, the traditional ETL pattern has shifted to ELT (Extract, Load, Transform). Instead of transforming data before loading it, ELT loads raw data into the warehouse first and then transforms it in place using tools like dbt. This approach is more flexible because the raw data is always available for reprocessing if requirements change.
The major ETL/ELT tools include Fivetran (the market leader for pre-built connectors), Airbyte (open-source alternative), Stitch (Talend), and custom-built pipelines using Python or Airflow. For MOps teams, Fivetran and Airbyte are the most relevant because they offer native connectors for marketing tools like Salesforce, HubSpot, Marketo, Google Ads, and LinkedIn Ads.
For MOps professionals, understanding ETL matters because it determines what data is available for analysis and how fresh it is. If your Salesforce data syncs to the warehouse every 24 hours, your pipeline reports are always a day behind. If your ad platform data syncs weekly, your spend analysis is even more delayed. Understanding these constraints helps you set appropriate expectations for reporting timeliness.
The most common ETL problems in marketing contexts are connector failures (APIs change, tokens expire), data volume limits (some tools charge by row or API call), schema changes (adding a field in Salesforce can break downstream transformations), and sync frequency limitations. Build monitoring and alerting so you know when a pipeline breaks before your stakeholders discover stale data.