ETL stands for Extract, Transform, Load, the three-step process that moves data out of business systems, cleans and reshapes it, then loads it into a central store for analysis. It pulls data from sources like a CRM, an ERP or ad platforms, standardizes it into one consistent format, and lands it in a data warehouse so every report reads from the same clean copy.
ETL is one of the oldest and most common ways to do data integration. It sits at the front of the modern data stack, turning scattered, messy source data into a trusted set of tables that dashboards and AI can use. Some teams build ETL with standalone tools and a separate warehouse. Others use a managed platform such as BEEM that handles extraction, transformation and the warehouse together. This guide explains how ETL works, how it differs from ELT, and where it fits.
How ETL works
ETL works in three ordered steps: extract data from source systems, transform it into a clean and consistent shape, then load it into a target warehouse. Each step does one job.
- Extract. Data is pulled from source systems such as a CRM, an ERP, databases, APIs and flat files. The extract step connects to each source and reads the raw records, on a schedule or in batches.
- Transform. The raw data is cleaned and reshaped. This is where duplicates get removed, formats get standardized, errors get fixed, and the data is modeled to match the target structure. It is the step that turns messy inputs into analysis-ready tables.
- Load. The transformed data is written into the destination, usually a cloud data warehouse or data lake. From there, dashboards, BI tools and AI assistants read one consistent source.
ETL vs ELT
The difference between ETL and ELT is the order of the last two steps. ETL transforms data before loading it into the warehouse. ELT loads the raw data first, then transforms it inside the warehouse using its own compute. ELT became popular as cloud warehouses grew powerful enough to run heavy transformations at scale.
ETL suits cases where data must be cleaned or masked before it lands, common in compliance-heavy work. ELT suits large volumes and cloud-native setups where you want raw data available fast. Many teams use both. For a full side-by-side, see our ETL vs ELT guide.
Common ETL use cases
ETL is used any time data from several systems has to come together in one place for reporting or analytics. The most common use cases share that pattern.
- Business reporting. Combine sales, finance and marketing data into a warehouse so dashboards read from one source instead of separate exports.
- Data migration. Move data from legacy systems into a new database or cloud warehouse, cleaning and reformatting it on the way.
- Customer views. Merge records that describe the same customer across a CRM, billing and support into one consistent profile.
- Feeding a data pipeline. Prepare clean, structured tables that a wider data pipeline then serves to BI and AI.
ETL tools
ETL tools automate the extract, transform and load steps so teams do not build and maintain the connections by hand. Most run in the cloud and offer pre-built connectors to common sources.
- Fivetran is a managed connector service that automates extraction and loading, with usage-based pricing. See our Fivetran alternatives guide and a breakdown of Fivetran pricing.
- Airbyte is an open-source tool with a large connector library that can be self-hosted or run in its cloud.
- Stitch is a simple cloud service focused on quick setup and lighter data volumes.
These tools handle the movement of data, but you still add a warehouse, transformation logic and BI around them. A managed platform bundles those layers together.
Where BEEM fits
BEEM runs the full ETL flow for you inside one managed platform. It brings 750+ managed connectors for extraction, SQL transformation to clean and model the data, and a built-in warehouse on Amazon Redshift to load it into. Around that, BEEM adds dashboards and AI Insights, so the pipeline and the warehouse are run for you. Plans start at $899 per month plus usage, and first dashboards land in as little as 2 weeks. For a mid-market team without a data engineer, that means no ETL tooling to stitch together. See the full picture on the product page.
