What is ETL?


Having the right data on hand is essential in order to beat your competitors, better serve your customers, and make smarter data-driven business decisions. But how can you efficiently feed all of this big data to your business intelligence (BI) and data analytics workloads?

For many organizations, the answer is ETL (extract, transform, load). Since the 1970s, ETL has been a dominant method of data integration to enable better business decision-making. In this article, we'll go over everything you need to know: the definition of ETL, how ETL works, the benefits of ETL, and more.

What is ETL (Extract, Transform, Load)?

ETL is a data integration process that consists of three stages: extract, transform and load. A closely related process, ELT (extract, load, transform), reverses the order of the last two stages. Below, we'll discuss each of the three steps of the ETL process in more detail.


First, data is extracted from one or more data sources. During this data extraction phase, you may pull from sources that include:

  • SQL relational databases and NoSQL (non-relational) databases
  • Flat files (e.g. CSV, XML, JSON, Excel spreadsheets, etc.)
  • SaaS platforms such as CRM (customer relationship managemen) and ERP (enterprise resource planning) software
  • APIs (application programming interfaces)
  • System logs and metadata

The data sets and source databases you use during the extraction phase may come from anywhere: on-premises, in the cloud, or from a third party. Any robust ETL implementation must be able to efficiently pull information from many different source locations.


Once you've extracted your raw data from your choice of source systems, this extracted data needs to go through the data transformation process. This is because your sources may provide information in a wide variety of data formats and data types. The objectives during the ETL transformation phase include:

  • Converting unstructured data into structured data.
  • Altering data so that it fits the schema of the target data store.
  • Performing data cleansing by removing inaccurate, out-of-date, and duplicate information.

Finally, when you have your transformed data in hand, you need to load data into your target database, data warehouse, or data lake.

Some common target systems for the data loading process include data warehousing solutions like Google BigQuery, Snowflake, and Amazon Redshift. You can also skip the transformation stage and directly move data into a data lake, which is a type of data repository for long-term data storage of "unrefined data" that hasn't yet gone through data cleansing and transformation.

How Does ETL Work?

Thus far, we've given a broad overview of the ETL process. In this section, we'll dive a little deeper into the specifics of how ETL works.


ETL can be separated into two categories based on when extraction occurs: real-time ETL (also called streaming ETL) and batch ETL.

  • In batch ETL, data extraction only occurs at regularly scheduled time intervals. For example, batch ETL might run overnight so that key decision-makers can have fresh reports on their desk the next morning.
  • In real-time ETL, data extraction occurs as soon as new data is available from one or more data sources. Real-time ETL is best at working with streaming data sources such as social media or IoT (Internet of Things) sensors.

There are also multiple ways of signaling when the extraction phase needs to occur: update notifications, incremental extraction, and full extraction.

  • Update notifications: The ETL system is notified as soon as fresh data is available.
  • Incremental extraction: The ETL system examines the data sources to determine which data records have changed, and then extracts only the changed records. (Note that it can be a challenge to identify deleted records that are no longer present in the source.)
  • Full extraction: The ETL system performs a full extraction of all data during every execution of ETL. This is resource-intensive and likely only feasible for smaller datasets.

There are too many potential data transformations to discuss in this ETL overview, but some of the more common data transformations are:

  • Data cleansing: Improving data quality by removing inaccurate, incomplete, or irrelevant data.
  • Data aggregation: Gathering data in aggregate and creating new data or presenting it in a new format. For example, you can calculate your total annual sales revenue by adding up the revenue from each deal closed by each of your sales representatives.
  • Data filtering: Selecting only certain fields, rows, or columns from a dataset that are relevant for future data analysis.
  • Data joining: Combining information from multiple data sources.
  • Data validation: Ensuring that your source data does not have empty, null, or corrupted elements.

The transformed data ends up in a staging area or staging layer, which is an intermediate location that holds the data before it loads into the target system. The destination tables of the target system are referred to as the schema layer.

Data loading can occur either in batches or in real time, depending on your choice of ETL method.

  • Large batches are usually loaded into the target system during off-peak hours to avoid performance slowdowns. However, small batches can load on an as-needed basis, which offers an intermediate option between large batching and real-time ETL.
  • With streaming loads, new data enters the target system as soon as it has passed through the transformation stage. This is usually for use cases that require up-to-the-minute decision-making.
What Are the Benefits of ETL?

ETL has many advantages and applications that can benefit organizations of all sizes and industries. The benefits of ETL include:

  • Automation: For any non-trivial ETL use case, the amount of data being handled is far too much for human workers to handle. ETL automatically extracts, transforms, and loads your data on a scheduled basis (or in real time), with little to no work required from your employees.
  • Less time, money, and effort: As a corollary to the previous point, automating your data integration processes with ETL is significantly more time- and cost-effective than manual data processing, even for the smallest use cases. Your employees can focus on high-level revenue-generating activities, instead of the technical details of ETL.
  • Smarter business decisions: Organizations depend on access to accurate, up-to-date, high-quality information to enable better decision-making, predicting, and forecasting. By efficiently obtaining this data, ETL enables you to uncover hidden trends and insights that help you remain competitive in a constantly shifting business landscape.
  • Better data management: Establishing a formal ETL workflow is part of a good data management and data governance strategy for your organization. ETL requires you to define which data sources you pull from, which transformations you enact, and which target systems receive the data. As a result, ETL formalizes the data integration process, making it more predictable, consistent, and reliable. This is especially important for data privacy and data security regulations such as GDPR and CCPA that require you to document how you use personally identifiable information (PII).
What Are ETL Tools?

The benefits of ETL are obvious—but if you're not a data science pro, how can you bring these ETL benefits into your organization? The answer lies in ETL and data integration tools that do the heavy lifting for you, helping you extract, transform, and load large volumes of data by setting up data pipelines.

Modern ETL tools are designed from the ground up to be powerful and robust, while also simple to use for the end user. Many ETL tools have a straightforward drag-and-drop, point-and-click user interface, and include a variety of pre-built connections to data sources such as databases, APIs, and SaaS platforms. By selecting the connections you want to use and the data transformations you want to perform, you can easily set up data pipelines and create data flows between your source and target systems.

When choosing the right ETL tool, there are several factors to consider, including:

  • The types and number of data sources you have
  • The types of data transformations you need to perform
  • Support for batch or real-time ETL
  • Your IT budget
  • Your business needs and objectives

The best ETL tools handle all the complicated, technically challenging parts of the data integration process for you. They automate and streamline the process of creating data pipelines, reducing the need to write code and link your source and target systems together.

ETL is a beneficial process for any organization that handles data from many sources. ETL tools help accomplish this so entities of all sizes can make full use of their data.