What is ETL? Definition, use cases, & benefits

ETL stands for Extract, Transform, Load. It’s a process that combines data from multiple sources into a single data repository, usually for analysis and reporting purposes.

ETL is a process where data is extracted from one or more sources, transformed (cleaned, sanitized, scrubbed), and loaded into an output data container.

The ETL process

Extraction

The first step is to extract data from various sources such as databases, applications, files, or other systems. The data can be structured (e.g., relational databases) or unstructured (e.g., log files, social media data).

Transform

The extracted data is cleaned, transformed, and formatted in this step according to predefined rules or business logic. This may involve filtering, sorting, joining, deduplicating, validating, and applying calculations or conversions to the data.

Load

The final step is to load the transformed and cleaned data into a target data warehouse, data mart, or other data repository for further analysis, reporting, and business intelligence purposes.

ETL benefits

• Integrates data from disparate sources into a centralized repository
• Ensures data quality and consistency through transformation rules
• Enables data analysis and reporting for better business insights
• Supports data warehousing, business intelligence, and analytics initiatives

ETL use cases

Data warehousing and business intelligence

One of the primary use cases of ETL is to consolidate data from disparate sources into a centralized data warehouse or data mart for analysis and reporting. ETL pipelines extract data from operational systems, transform it into a structured format, and load it into the data warehouse, enabling business intelligence and analytics initiatives.

Data Integration and Data Migration

ETL is widely used for integrating data from multiple heterogeneous sources, such as databases, applications, and files, into a unified view. It is also employed for data migration projects, where data needs to be moved from legacy systems to modern platforms or cloud environments.

Machine Learning and AI

ETLs are crucial in preparing data for machine learning and artificial intelligence applications. It helps clean, transform, and format data to create high-quality datasets for training ML models and enabling advanced analytics.

Internet of Things (IoT)

With the proliferation of IoT devices generating large volumes of data, ETL processes extract, transform, and load sensor data, location data, and other IoT data into data lakes or warehouses for analysis and insights.

Customer Relationship Management (CRM)

ETL pipelines are used to integrate customer data from various sources, such as sales, marketing, and support systems, into a centralized CRM system, enabling a 360-degree view of customers and supporting targeted marketing campaigns.

Financial Services

In the financial sector, ETL consolidates transaction records, customer data, and market information for risk management, fraud detection, and regulatory compliance.

How can Prequel support your ETL initiative?

Prequel helps software companies share data with their customers without building an ETL pipeline. Companies use Prequel’s Data Sharing Platform to send data to every major data warehouse, database, and object-based storage service, including Snowflake, BigQuery, Redshift, and Postgres. 

• Set up Prequel in less than one day.

Connect Prequel to your source and outline the data your company would like to share.

• Customers sign up in a couple of clicks.

Send customers a magic link to set up their destination.

• Transfer up to 100M records per destination every 15 minutes.

Fresh, analysis-read data is always available whenever customers need it.