ETL Ultimate Guide: Definition, Examples, and Tips
ETL, which stands for extract, transform, and load, is a technical process that imports information from multiple data sources, scrubs and transforms data to ensure consistency, and stores it in a single data warehouse. The ETL process sets organizations up for success with later technical tasks, including data integration and data analysis
Data management needs increase every day. In 2010, around two zettabytes of data was created globally. In 2023, the estimated total global data creation figure was 120 zettabytes, and this figure is only expected to rise.
While ETL excels in complex data transformations, for simpler integrations, Integration Platform as a Service (iPaaS) might be a suitable alternative. Integrating ELT and other data management solutions is critical to supporting success now and in the future.
This guide will focus on ETL processes vs. iPaaS, which can also play a role in your data strategy. I want to equip you with the knowledge to navigate the ETL landscape and select the best ETL tools for your data integration tasks. I'll share everything you need to know about traditional ETL (and more).
What is ETL?
ETL is a three-step process that helps support data quality, storage, and access for businesses and other organizations.
Extract
The first step of ETL is data extraction. During this step, raw data is exported out of multiple data source locations and into a temporary staging area. Typically, raw data from disparate sources is not ready to be dumped into the final data repository because it will be inconsistent.
You can extract data from a variety of sources depending on your business needs. Common sources include email, flat files provided by business partners or clients, ERP or CRM systems, online web pages and data banks, SQL servers, and NoSQL servers.
Transform
Before final data warehousing, the data must be transformed. Data transformation converts raw, messy data to consistent data that can feed data analytics processes. Some steps in the transformation process include:
- Creating viable data sets that make sense for the business needs
- Deduplicating to remove duplicate information from data sets
- Converting unstructured data to structured data by applying schema and other methods
- Validating data to ensure authenticity and accuracy
- Data cleansing to remove corrupt data, address missing fields in data sets, and ensure data is formatted correctly
- Removing encryptions or adding protections to data, depending on the compliance requirements of the business
- Formatting data to meet business needs, such as adding or changing column headers to support consistency and ensure data works well with existing relational databases
Load
The final step in the ETL data pipeline is to load data into the right data warehouse. You would start with an initial load of data and update it periodically to ensure the business has real-time access to current information.
Real-world ETL
ETL processes are helpful for a wide variety of businesses, including those in industries such as healthcare, finance, retail, shipping, and entertainment.
Why is ETL Important?
Data is critical to the success of almost any business today. It feeds machine learning processes that support automation and helps businesses make intelligent marketing, customer service, product development, and investment decisions. ETL tools and processes help ensure accurate data from all data sources is available and accessible to support other business processes.
A few ways ETL is important to data-based processes include:
- It lets businesses consolidate data into one repository for a single source of truth
- It ensures data is standardized to support your workflows and technical systems
- It supports appropriate data access through GUIs and other tools designed for non-technical users
ETL Background
ETL is not a new process. The concepts date back to the 1970s, though, before the rise of machine learning and AI, data integration was a much more manual process. Throughout the 80s and 90s, as data, data sources, and databases increased, technology leaders began developing tools for purposeful data integration. Those are the basis of ETL today.
Perhaps the biggest driver for modern ETL processes was the advent of cloud computing. Suddenly, organizations weren’t limited by physical servers, and the amount of digital data they could store was endless. The ability to scale data storage up or down with solutions such as AWS made it easier to keep all your data. Still, organizations needed ways to extract, load, and store that information easily.
ETL vs. ELT
The choice between ETL and ELT (Extract, Load, Transform) depends on the specific needs of an organization, the volume of data, and the computational power available.
ETL is traditionally more popular in scenarios where data transformation is complex and needs to be processed before it enters the data warehouse. This approach allows data cleansing and consolidation to occur before loading, making it suitable for systems where data quality and preparation are critical.
On the other hand, ELT is gaining popularity, especially with the rise of cloud-based data warehouses that offer significant processing power. ELT allows for data to be loaded into the data warehouse more quickly and transformed as needed within the database itself, making it a better fit for handling large volumes of data in real-time or near-real-time scenarios.
Neither approach is inherently better; the choice between ETL and ELT depends on the specific requirements of the data processing tasks, the architecture of the data system, and the goals of the organization. For example, an organization dealing with massive, constantly updating data sets might prefer ELT for its efficiency and scalability. In contrast, a company prioritizing data integrity and pre-load processing might opt for ETL.
Innovations in this space continue, and one example is work in the ELT space. ELT is another data processing option that changes the order of tasks. In this process, you extract data, load data, and then transform data.
ELT offers benefits such as feeding data lakes with unstructured data or loading all data immediately and sorting it out through transform processes later.
How ETL Works (and Examples)
To better understand ETL functions, let’s look at a use case. Imagine a large-scale florist organization that has storefronts in several cities and also offers customers the option to order online for delivery to homes or businesses. You may already have ideas about the types of data this organization would need to manage, including flower and supply inventories and orders, customer information, delivery addresses and instructions, payment processes, and the orders themselves.
For this example, let’s consider the order process and the data it generates. Order data may come from:
- A mobile app, which uses an API to integrate with the company’s main CRM system
- The website, which has a digital storefront and shopping cart
- Manual entry from employees at physical storefronts
Of course, that data is typically combined with inventory and order completion information, payment processing data, and shipping or delivery information. The right ETL pipelines allow all this data to be combined into a data store to support future tasks such as:
- The ability for an existing customer to quickly place the same order again in the future
- Customer service work, such as looking up past invoices for customers who have billing questions
- Data analytics to support sales and marketing, such as a review of previous top sellers to inform decisions about what to offer on a website for Mother’s Day this year
Benefits of ETL
Using ETL to aggregate data from source systems and load it into the right target system offers a number of business benefits.
| Cost Savings | The data transformation step lets you weed out unnecessary, incorrect, or duplicate data, so you’re not paying to store data you don’t need. When clean data is stored correctly and more accessible to workflows, artificial intelligence, and employees, you also reduce how long many processes take, positively impacting labor costs. |
| Increased Production | Better access to source data helps end users do their jobs better, leading to better employee morale, faster processing times, and increased production. |
| Enhanced Communication | When large data sets are sourced, transformed, and loaded quickly and consistently, it supports better communication. For example, when real-time data is available to customer service representatives, they can easily provide details in response to customer queries. |
| Better Decision-Making | The volumes of data supported by strong ETL processes supports business intelligence that generally leads to better decision-making across all departments. |
ETL Challenges
Perhaps the most common ETL challenge has to do with data quality. When you extract data from multiple sources — especially when you allow for customer-generated sources — you can’t automatically ensure its integrity. Missing information, inconsistent information, and outdated data are a few issues you’ll deal with in the ETL pipeline.
Other common ETL challenges include:
- Ensuring the security of data throughout the process, as you create potential vulnerabilities when you move and store data
- Supporting ongoing data performance, such as the efficiency of ETL processes and continual access to data once it’s in the target database
- Integrating data with existing databases, API tools, and other platforms so that it can be used to support business processes
- Ensuring data is governed appropriately by compliance protocols at every stage of the process
How to Get Started With ETL + Tools
Organizations that aren’t already using ETL processes can get started by learning more about ETL and ensuring they have the basics of extract, transform, and loan supported. You might consider hiring in-house technical support with ETL experience or working with vendors who can provide ETL support as a service.
Next, ensure that you have the right tools in place and take some time to practice data extraction from various sources. Before you go live with your ETL processes, test each step and troubleshoot any issues you have, especially in the data transformation tasks.
Once you’re ready, you can build an ETL pipeline by:
- Creating your reference data set so you know what the outcome of data transformation should be
- Connecting your sources via extraction tools such as APIs
- Building workflows to validate and transform the data
- Setting up more tools to automatically load the data into the target databases once transformation is complete
ETL Tools to Get You Started
Always research and test tools before you integrate them into your processes. You can start your quest for great ETL tools by considering some of these options:
- Informatica PowerCenter – This tool helps you build and deploy different data pipelines, and it offers no-code options to support fast integration by a variety of teams. You can also leverage numerous data lakes and data warehouses with this option, including those offered by Google Cloud, AWS, and Azure.
- IBM Infosphere Datastage – This tool works within the context of IBM’s ecosystem and provides benefits such as speed and access to AI applications.
- Oracle Data Integrator – This option lets you build and support complex data lakes and warehouses, and it comes with a graphical interface option that supports use by non-technical business users.
- AWS Data Pipeline – This is a managed service that helps you leverage data management benefits offered by Amazon Web Services.
Best Practices for ETL
The best thing you can do for new ETL processes is launch them with a well-thought-out and tested plan. You can also follow some of the best practices below to maximize the benefits of ETL for your organization:
- Support better data quality by integrating validations at every step of the process. That includes in the original data-entry task when possible. For example, if you want individuals to enter a date, have them select it from a calendar API that ensures the data is formatted consistently rather than having users manually type the information.
- Work with trusted vendors and solutions when building connectors and other parts of your ETL pipeline. Always ask vendors how they secure data so you can be sure your digital assets are well protected.
- Enhance the performance of your ETL tools by making smart technical decisions from the data sourcing process to the load process. Optimize your ETL pipeline with choices such as implementing caching techniques or using incremental loading.
- Implement metadata tools and other data management processes so you can best monitor the data and ensure accurate access and control over who accesses what data in the future.
More Resources for Further Learning
To learn more about ETL and related topics, consider some of the curated resources below:
- Hevo offers a list of open source tools for ETL to help you build your ETL framework
- Check out this list of top ETL tools
- Roundup of the best data warehouse tools
- Dremio has an advanced guide to types of ETL
- Coursera offers a number of free ETL courses and certificates
- Find ETL education opportunities via Udemy
Takeaways
With data growing bigger literally every minute, CTOs and other business and technical leaders can’t afford to ignore the benefits of ETL. If you’re not leveraging these processes to support efficiency, accurate data sourcing and storage, take time to learn more and present ETL as an important investment for your organization.
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