What is ETL? The Ultimate Guide

This is Part 1 of our 3-part Data Integration Guide series. Also read: What is ELT? & ETL vs. ELT.

ETL stands for "Extract, Transform, and Load." If you're reading this, you've probably heard the term "ETL" thrown around in relation to data, data warehousing, and analytics, but you're probably wondering exactly what ETL is and what it means.

Put simply, if you have data from multiple sources that you want to bring into a centralized database, you need to extract, transform, and load that data:

• Extract the data from its original source, whether that is another database or an application
• Transform data by cleaning it up, deduplicating it, combining it, and otherwise getting ready to…
• Load the data into the target database

This infographic explains the ETL (Extract, Transform, Load) process, a method for moving data from multiple sources into a centralized data warehouse or platform. It highlights the three steps:

• Extract – Collecting raw data from CRMs, ERPs, APIs, flat files, and IoT sensors.
• Transform – Cleaning, formatting, and enriching data by filtering duplicates, standardizing formats, joining datasets, and adding calculated fields.
• Load – Moving the transformed data into a target system (warehouse or lake) for analytics, BI, and AI.

The goal of ETL is to deliver clean, reliable, analytics-ready data.

Typically, one ETL tool does all three of these steps, and is a critical part of ensuring that data required for reporting, analytics, and, now, machine learning and artificial intelligence is complete and usable. But the nature of ETL, the data it handles, and where the process takes place has evolved tremendously over the last decade, and the right ETL tool and software is more critical than ever.

A Brief History of ETL

A brief history

ETL has roots in the 1970s and the rise of centralized data repositories. But it wasn’t until the late 1980s and early 1990s, when data warehouses took center stage, that we saw the creation of purpose-built tools to help load data into these new warehouses. Early adopters needed a way to “extract” data from siloed systems, “transform” it into the destination format, and “load” it. The first ETL tools were primitive, but they got the job done. Granted, the amount of data they handled was modest by today’s standards.

ETL has roots in the 1970s and the rise of centralized data repositories. But it wasn't until the late 1980s and early 1990s, when data warehouses took center stage, that we saw the creation of purpose-built tools to help load data into these new warehouses. Early adopters needed a way to "extract" data from siloed systems, "transform" it into the destination format, and "load" it. The first ETL tools were primitive, but they got the job done. Granted, the amount of data they handled was modest by today's standards.

As the amount of data grew, data warehouses grew, and ETL tools and software proliferated and became more sophisticated. But through the end of the 20th century, data storage and transformation was done primarily in on-premises data warehouses.

The 1970s: Birth of ETL

With the advent of relational databases, businesses began to use batch processing for extracting, transforming, and loading data into central data warehouses. Data integration and transformation became a crucial part of data management for structured datasets in on-premises systems.

Key Technologies: IBM DB2, Oracle, and early batch ETL tools like Informatica.

Why is ETL Important?

ETL has several business benefits that go beyond simply extracting, cleaning, conforming and delivering data from Point A (source) to Point B (destination):

Context: ETL helps businesses gain deep historical context with data.

Consolidation: It provides a consolidated view of data, for easier analysis and reporting.

Productivity: It improves productivity with repeatable processes that don't require heavy hand-coding.

Accuracy: It improves data accuracy and audit capabilities that most businesses require for compliance with regulations and standards.

Your data still needs to be brought to a central repository–now from more sources than ever, in structured and semi-structured form. Those huge stores of data need to be transformed into formats best suited for analysis. ETL tools prepare data for fast access and thus fast insight.

ETL: The Three Steps Explained

1. Extract

Data is gathered from multiple sources, such as databases, APIs, CRM systems, ERP systems, or flat files. The extraction process must handle different formats, latency issues, and connectivity challenges.

The 'Extract' part can be relatively simple if it's a single data source such as an ERP database. But if it's an ERP database plus a number of lines of business systems or external third-party data sources, it gets more complicated quickly. Whether you have one source or many, you need the right connectors to extract the data from your source.

2. Transform

Before loading, the data undergoes various transformations to ensure consistency and readiness for analytics. These transformations include:

• Data cleansing (handling missing values, deduplication)
• Data enrichment (merging external data, standardizing formats)
• Aggregations (calculating totals, averages, or other metrics)
• Schema modifications (renaming columns, restructuring tables)
• Data type conversions and field mapping

The "Transform" step can vary in complexity. It can be a straightforward denormalization of the normalized data contained in an ERP or line of business database. Or, it can be a more involved task, such as converting units of measure to a common basis or harmonizing customer names.

3. Load

Once transformed, the data is loaded into the data warehouse. Because transformations are already completed, the data is structured and immediately available for reporting.

Whether ETL is straightforward or more complicated, it's characterized by a lot of I/O activity, a lot of string processing and variable transformation, and a lot of data parsing. In other words, it's very compute-intensive.

Common ETL Use Cases

Data Warehousing

Enterprises have traditionally used ETL to collect data from various sources, transform it into a consistent, analytics-ready format and load it into a data warehouse, where business intelligence teams can analyze it for business purposes.

Database Replication

ETL is critical to database replication, whether you are moving data from source databases as a one-time operation, or as an ongoing process. Often this involves moving data from an on-premises data warehouse to a cloud data warehouse.

Business Intelligence

Enterprises need to analyze data to provide business intelligence that enables managers and stakeholders to make informed decisions. In order for those decisions to be truly informed, they need to be based on all of an organization's data, not just as much as a legacy data architecture can handle.

Marketing Data Integration

Customers interact with businesses today on multiple channels, logging multiple interactions and transactions per day, or even per hour. ETL can be critical to collecting and integrating customer data from eCommerce, social networking, websites, mobile applications, and other platforms.

ETL and Data Quality

ETL tools can significantly improve the quality of data. The ability to standardize and automate ETL processes that move data from a source system into the data warehouse means that you're less likely to have dirty data leading to decisions based on inaccurate information and insights.

ETL tools can also integrate with dedicated data quality tools, and often will include features for data quality-related functions such as:

• Data mapping and data lineage
• Automated validation
• Duplicate removal
• Data standardization
• Error logging and monitoring

Types of ETL Tools

Batch Processing ETL Tools

Until very recently, batch processing in on-premises tools was the only practical way to do ETL for a lot of businesses. Historically, processing large volumes of data took a lot of time and resources and could easily tax a company's compute power and storage during business hours. It made more sense for enterprises to run that data processing in batches with ETL tools during off-hours.

Open Source ETL Tools

Open source ETL tools are a low-cost alternative to commercially packaged ETL solutions, and are a practical option for many businesses. Some common open source tools include Apache Airflow, Apache Kafka, and Apache NiFi.

One downside to open source ETL projects is that they are not designed to handle the data complexities that modern enterprises face, and may lack support for complex data transformation and desirable features such as change data capture (CDC).

Real-time ETL Tools

More often today, we demand real-time access to data from different sources. Real-time demand increasingly requires that we process data in real time, with a distributed model and streaming capabilities, rather than in batches. However, just because you can do ETL in real time doesn't necessarily mean you should: there are use cases where batch processing is still more efficient and simpler for handling large amounts of data.

Common ETL Challenges

Challenge #1: Scaling

Scalability is one of the most important features in a modern ETL tool. The amount of data that businesses collect is only going to continue to grow. You may move data in batches now, but will that always hold true for your business? You need to be able to scale ETL processes and capacity as much as possible.

Challenge #2: Transforming Data Accurately

Another big ETL challenge is ensuring that the data you transform is accurate and complete. Manual coding and changes or failure to plan and test before running an ETL job can sometimes introduce errors, including loading duplicates, missing data, and other issues. An ETL tool can reduce the need for hand-coding and help cut down on errors.

Challenge #3: Handling Diverse Data Sources

Data is growing in volume and complexity. One enterprise could be handling diverse data from hundreds – or even thousands – of data sources. These can include structured and semi-structured sources, real-time sources, flat files, CSVs, S3 buckets, streaming sources, and more. Handling each type of data in the most effective and practical manner can be an enormous challenge.

When to Choose ETL

ETL is best suited for organizations with well-defined data models, strict compliance requirements, or on-premises infrastructure. Use cases include:

• Regulated Industries (e.g., banking, healthcare) where data needs to be cleansed and transformed before storage to meet compliance regulations
• Legacy Systems that rely on on-premises data warehouses and have limited cloud adoption
• Operational Reporting, where data is needed in a structured format for daily business operations
• Limited Transformation Needs where data sources and formats are consistent and don't require frequent adjustments

Pros of ETL

• Well-suited for on-premises architectures
• Data is clean and structured before reaching the warehouse
• Compliance-friendly for highly regulated industries (e.g., healthcare, finance)
• Strict control over data before it enters the warehouse

Cons of ETL

• Can be slow and resource-intensive due to pre-load transformations
• Not easily scalable for large, modern datasets
• Requires significant upfront planning to define transformation rules
• Limited by the processing power of the ETL server

ETL Best Practices

When implementing ETL processes, consider these best practices:

• Predefine transformation rules to ensure consistency
• Optimize ETL workflows to minimize processing times
• Use a scalable ETL tool to handle increasing data loads
• Ensure compliance with data governance policies
• Implement proper error handling and monitoring
• Document data lineage for audit and troubleshooting purposes

The Evolution of ETL

As businesses increasingly move to the cloud and deal with larger, more diverse datasets, traditional ETL is evolving. Modern data architectures often favor different approaches that can better leverage cloud computing power and handle semi-structured and unstructured data.

For organizations evaluating their data integration strategy, understanding both traditional ETL and modern alternatives like ELT (Extract, Load, Transform) is crucial for making informed decisions about their data architecture.

To learn more about how ETL compares to modern ELT approaches, explore our comprehensive ETL vs ELT comparison guide.

For today’s enterprises, data, a cloud data warehouse, and the right ETL solution are three equally important legs of a forward-thinking business intelligence strategy. Without any one of these things, it’s going to be very difficult for an enterprise to remain relevant in the next five years.

There are five things an ETL tool needs to do:

It needs to be cloud-native. Traditional tools for traditional data warehouses don’t translate. They were built for a platform with limitations, and will not step up to take advantage of the speed and scalability of the cloud.

It needs to go deep. A cloud-based ETL tool needs to do all of the things that a traditional, full-scale, grown-up enterprise ETL tool does – and then some. It needs to be able to take all kinds of data – structured, semi-structured, cloud or on-prem– from all kinds of data sources and join them together.

It needs to be transformative. Just moving data from left to right in a pipeline isn’t the whole job. It isn’t even the most critical part of the job. Organizations need a tool that was specifically created to transform that data and get it analytics-ready.

It needs to be flexible. It’s rare that a business has data and operations in only one cloud. Or that it doesn’t need to move information from one cloud to another. Choose a tool that can move with you. Also, the job of transforming is almost never done. You continually innovate at the transformation layer, as the business changes and as new questions require new answers and insights.

Let Us Help You Build Your ETL Strategy

Matillion ETL is data transformation for cloud data warehouses. As a cloud-native solution deployed into your cloud infrastructure, Matillion leverages the speed and scale of the cloud to consolidate large data sets and quickly transform your data to make it analytics ready.

To learn more about Matillion and ELT technology, download our ebook, From ETL to ELT.