[GTM glossary](/glossary.txt) Data pipeline Data & Enrichment # Data pipeline In short A data pipeline is a set of automated steps that collects data from one or more sources, processes it, and delivers it to a destination such as a warehouse, a CRM, or an analytics tool. It replaces manual exports and spreadsheets with repeatable flows. In a GTM setting, a pipeline might pull product usage, form fills, and enrichment results, clean them, and sync scores back into the CRM so reps see them. Not to be confused with a sales pipeline, which tracks deals through stages. ## Core stages of a data pipeline ### Ingest Collect data from sources through APIs, webhooks, file drops, or database connectors. ### Transform Clean, normalize, deduplicate, join, and calculate fields such as an account score. ### Load Write the results to a destination such as a warehouse or data lake, or sync them back to operational tools. ### Monitor Alert on failures, late runs, and unexpected changes in volume or values. --- ## ETL and ELT * **ETL:** Extract, transform, then load. Data is shaped before it reaches the destination. * **ELT:** Extract, load, then transform inside the warehouse. Common with modern cloud warehouses. --- ## GTM examples * **Enrichment flow:** New leads pass through verification and enrichment, then land in the CRM with routing fields set. * **Scoring sync:** Warehouse models compute account scores and push them to CRM fields. * **Reporting feed:** Marketing and sales data combine in a warehouse for attribution dashboards. * **Signal routing:** Intent or job-change events trigger tasks or alerts for reps. --- ## How to build a reliable pipeline Start with one use case and one destination. Document each field's source and meaning. Add validation, such as required fields and sane value ranges, between stages. Make runs repeatable so a failed job can be re-run without creating duplicates. Assign an owner who gets alerted when it breaks. --- ## Common mistakes * **No monitoring:** Silent failures leave teams working with stale data without knowing it. * **Hard-coded logic:** Rules buried in scripts that nobody documents become impossible to change. * **Ignoring privacy:** Moving personal data between systems needs access controls and retention rules. * **Syncing everything:** Push only the fields teams use, to avoid cluttering the CRM. --- ## Data pipeline FAQs **What is a data pipeline?** A data pipeline is an automated series of steps that moves data from source systems to a destination, usually cleaning and transforming it on the way. It replaces manual exports with repeatable, monitored data flows. **What is the difference between a data pipeline and a sales pipeline?** A data pipeline is technical infrastructure that moves and processes data between systems. A sales pipeline is the set of stages deals pass through from first contact to close. They share a word but describe different things. **What is the difference between ETL and ELT in a data pipeline?** ETL extracts data, transforms it, and then loads it into the destination. ELT loads raw data first and transforms it inside the destination, usually a cloud warehouse. ELT is common when the warehouse can handle heavy processing. **How do GTM teams use data pipelines?** They use pipelines to enrich and clean leads, sync account scores from a warehouse to the CRM, combine marketing and sales data for reporting, and route intent or job-change signals to reps as tasks or alerts. Share term --- ## More related phrases * [ETL](/glossary/etl.txt) * [Native integration](/glossary/native-integration.txt) * [Custom API integration](/glossary/custom-api-integration.txt) ## Similar phrases * [CRM integration](/glossary/crm-integration.txt) CRM integration connects your customer relationship management system with other tools, such as marketing automation, data providers, and sales engagement, so records and activity sync automatically between them. * [Data warehouse](/glossary/data-warehouse.txt) A data warehouse is a central system that stores structured data from many sources, such as CRM and product usage, organized for reporting and analysis. * [Data lake](/glossary/data-lake.txt) A data lake is a central store that holds large amounts of raw data in its original format, structured or not, so teams can analyze it later for many purposes. * [Data quality](/glossary/data-quality.txt) Data quality describes how well a dataset is fit for its intended use, measured by traits such as accuracy, completeness, consistency, timeliness, and uniqueness. * [Enrichment API](/glossary/enrichment-api.txt) An enrichment API lets your own systems send a record, such as an email or domain, and receive additional data back programmatically, instead of using a manual upload or a point-and-click tool.