Overview
ETL stands for extract, transform, and load in data integration and data warehousing. This process involves moving data from source systems to a data warehouse or another destination system for analysis and reporting.
ETL is crucial for organizations that need to consolidate data from multiple sources and make it available for reporting, business intelligence, and analytics. It helps ensure that data is accurate, consistent, and in a suitable format for analysis. Read on to get a deep dive into ETL, how it works, the benefits and challenges you can expect, and how to build the best data management strategy.
What does ETL refer to in data warehousing?
ETL, or Extract, Transform, and Load, refers to the process in data warehousing that concurrently reads or extracts unstructured data from the source system—such as a data lake—converts (or transforms) the data into the proper format for querying and analysis, and loads it into an on-site data warehouse, cloud data warehouse, operational data store, or data mart. ETL systems commonly integrate data from multiple applications or systems that may be hosted on separate hardware and managed by different groups or users. ETL is commonly used to assemble a temporary subset of data for ad-hoc reporting, migrate data to new databases, or convert databases into new formats or types.
ETL is important to data warehousing because it allows raw data collection from multiple data sources and centralization for analytics needs. This lets you make faster queries because you’re asking questions from a single data source.
How does the ETL process work?
ETL tools automatically consolidate data from one or many sources into one central container. The process involves three steps:
- Extract. The process of pulling data from multiple sources, ranging from key enterprise applications and databases to devices connected to the internet of things (IoT).
- Transform. To process the raw data into a uniform format—something that can be stored in a data warehouse as transformed data for eventual analysis. Data transformed into certain formats—like structured query language (SQL)—is useful for making certain queries.
- Load. The storing of the data in a new container, typically a database, application, or data warehouse.
There are several types of ETL tools and ETL software:
- On-premises tools can allow for better security, as all the data is stored onsite.
- Cloud software is specifically designed for ETL processes that serve cloud-based data warehouses and applications.
- Batch software conducts the ETL process in batches, which is ideal for regular analytics and reporting of structured data—like payroll information.
- Real-time ETL tools minimize the amount of time it takes to gather and analyze information in the data pipeline
Benefits and challenges of ETL
ETL has several important benefits.
Ideal for classic business intelligence (BI) processes
Business intelligence is a broad term that encompasses data mining, process analysis, performance benchmarking, and descriptive analytics. Without ETL, businesses would have great difficulty compiling and analyzing data for BI. ETL allows companies to make complex queries and get prompt responses that help them make better decisions.
Helps prepare data for easier migration to the cloud
The ETL process allows you to pull data from many disparate sources and transfer them to a centralized data warehouse or analytics platform. Without ETL tools, this can be exceptionally difficult and time-consuming, especially if you’re working with many diverse data sources and types.
Makes data easier to trace through a pipeline
Through the proper use of ETL, data exists in a uniform format that can be easier to track through an enterprise's data pipelines and overall architecture.
Can save time and resources if deployed in conjunction with automation
Automation tools make it possible to perform ETL without constant monitoring. This is especially true for enterprise-scale businesses that process large amounts of data each day. Automated ETL tools also protect data teams from the risks associated with human error.
Useful in industries like healthcare, manufacturing, financial services, and government
While these industries are some of the most prominent, nearly any industry that utilizes big data to function can benefit from ETL because it helps you make better decisions through faster queries.
A few challenges of ETL are:
- Lack of scalability. ETL relies on predictable data sources that don’t change much to function. If you change your IT environment, you’ll need to tweak your ETL testing processes so they can keep up.
- Transformation leading to flawed or inaccurate data if not tested for quality and explored before the process begins. ETL tools are complex and require a great deal of expertise to function properly. Without proper testing, cleansing, and exploration, errors may find their way into the data.
- Conflicting ideas about ETL. Both data analytics and data engineering are vital for all data teams, but they serve separate purposes. Data scientists perform data analysis using tools such as machine learning (ML) in the realm of data science. Data engineers work with raw data to turn it into useful information for decision-making.
ELT vs. ETL
Extract, load, and transform (ELT) is a variation of the ETL pipeline, often used in cloud-based environments. Instead of transforming data before loading, ELT ingests and stores raw data in a data warehouse or data lake, where it can be transformed later as needed for analysis.
Because transformation happens after loading, ELT can improve speed and flexibility, particularly in cloud-native architectures that support large-scale data processing and on-demand compute.
While ETL and ELT follow similar steps, they differ in how and when data is transformed. The table below summarizes the key differences between ETL and ELT across processing, performance, and common use cases.
| Factor | ETL (Extract, Transform, Load) | ELT (Extract, Load, Transform) |
|---|---|---|
| Processing order | Data is transformed before loading into the target system | Data is loaded first, then transformed within the target system |
| Speed | Slower for large-scale data due to upfront transformation | Faster ingestion; transformation happens after data is stored |
| Data volume | Best suited for smaller, structured datasets | Designed for large-scale, high-volume data |
| Use cases | Traditional data warehousing and structured reporting | Cloud analytics, data lakes, and modern data platforms |
| Best for | Environments requiring strict data quality and predefined schemas | Flexible analytics, exploration, and advanced data processing |
In general, ETL is preferred for structured, controlled environments, while ELT is better suited for scalable, cloud-based analytics.
Building the best data management strategy
When choosing ETL tools, there are a few factors to consider. Your ETL tool should have:
- Comprehensive monitoring features. A detailed illustration of progress when performing ETL tasks is vital for maximum transparency
- Effective error handling. If something goes wrong, the ETL tool should be able to explain why. In addition, it should have preventative measures against data loss.
- Scalability. If you expect your business to grow, your tools should be able to grow with you. An ETL tool that can’t handle increasing amounts of data isn’t going to be useful for long.
- An easy-to-use interface. The most advanced ETL tool on the market is of little help if its UI makes no sense. Your data integration tool should be bug-free, reliable, and easy to set up.
- Compatibility with various data sources. If you need to gather data from a wide range of containers, whether a data warehouse or database, your tool should be able to work with all of them without a hitch. It should also be able to work seamlessly with a variety of cloud services.
ETL has specific uses, but it’s generally not a suitable approach to big data on its own. Instead, it should be part of a larger strategy that accounts for current data trends and constantly shifting processes.
An ETL solution can be used most effectively with on-premises databases that need to stay on premises for security reasons, or in the form of streaming ETL for real-time processing via open-source tools like Spark Streaming. On the other hand, ELT needs to be used for optimal, cloud-native data integration. ELT is faster than ETL and best suited for working with large volumes of data from different sources.
Teradata VantageCloud is ideal for integrating all enterprise data—whether it was processed using batch ETL, streaming ETL, or cloud-native ELT—and creating a single source of enterprise truth. Contact us today for more information.