The extract, transform, and load process (ETL) is critical for any organization handling large volumes of data. Without efficient ETL tools to move data from many sources into a central location, businesses end up with data silos and incompatible data sets that can’t be used across the company. ETL provides a way to prevent data silos and brings raw data from many different sources together into a cloud data warehouse so a business can use it for analysis, decision-making, and product development.
ETL is most often used by enterprise DataOps teams to manage data analytics operations, but small businesses, startups, and entrepreneurs can use it too. Need to move all your CRM data into a cloud data warehouse? ETL handles that at any scale. Want to build an analytics dashboard that’s regularly updated? ETL makes that possible too. Here’s how it works.
Its three main steps work exactly as they sound:
- Extract data from a source system
- Transform it into a useful format
- Load it into a target system (your cloud data warehouse or data lake)
With the right data tools to run your ETL processes, you can automate manual data extraction processes, improve your data analysis capabilities, and have clean data sets ready for machine learning (ML) algorithms.
Let’s look at how this data management process works from start to finish and see how data science teams use ETL.
What is ETL?
ETL is a process that moves data from periphery and external data sources into a central data warehouse or data lake. But it doesn’t just move the dataset to a new data storage location, it transforms data into usable formats and schema before it loads the extracted datasets into your warehouse, data lake, or databases.
ETL has been around since the 1970s, and it’s even more valuable today because of the drastic increase in volume and complexity of modern business data.
Many companies have teams of dedicated data scientists, data engineers, data analysts, and machine learning specialists to make all their big data useful to the business. Running DataOps at that scale requires a modern data stack and automated processes like ETL to make sure data analytics teams have constant streams of accurate and up-to-date data.
With a reliable data science pipeline feeding clean and structured data to each junction along the way, your business gets improved data quality, reduces wasted time, and increases decision-making speed across the business. Ensuring you understand ETL and get it right at whatever scale you’re operating is crucial to become a successful data-driven organization.
Why do you need ETL?
You need ETL if you have multiple data sources—like Salesforce, Marketo, Hubspot, Google Ads, Facebook ads, social media, website analytics, APIs, or cloud apps—and you want to combine different types of data from all those sources into valuable insights. Set up an automated ETL pipeline from each data source to your data warehouse.
Lifetime customer value, brand loyalty metrics, and advanced customer journey insights come from strategically using ETL. Once all your data sets are extracted, transformed into compatible formats, and loaded into your central data warehouse, those advanced business intelligence metrics become possible to calculate, visualize, and use for decision-making.
Here’s a quick list of the high-level benefits your business can get from using ETL:
- Increased decision-making speed
- Improved data quality
- Faster and more efficient data integration
- More accurate data analysis
- Better data visualizations
- New datasets for machine learning analysis
- Central source of truth for company data
Clean, accurate, and real-time datasets also make it possible to build new software products and features faster. For example, when your product data is cleansed, structured, and accessible, you can quickly iterate on new searches and filter e-commerce features. Every time you use ETL to move data from a new source to your data warehouse, you add to the list of possible opportunities for your business.
Steps of ETL process
Once you know which data source you want to extract data from, the ETL process is almost as straightforward as it sounds. Extract data from the target source, transform data into a usable format, and load data into a data warehouse or data lake.
But there are some important details locked up in each step that you’ll need to know about to get ETL right.
Extract data
This first step requires extracting structured and unstructured data from a data source and consolidating it into a staging area. The staging area is where the next step of data transformation occurs before the extracted data loads into a data warehouse or target database. This approach makes it easier to catch errors early and start over if needed.
Here are some common data sources to extract from:
- Customer data platforms (CDPs)
- Customer relationship management (CRM) tools
- Website analytics
- Product information databases
- Advertising and marketing platforms
- Analytics tools
In the past, data engineers would hardcode the extraction process, but that method is slow and prone to errors. ETL tools make it easy to set up data pipelines between data sources and your cloud data warehouse to keep a consistent stream of data feeding the business.
Transform data
This step is where the data transforms to meet data quality and schema requirements of the data warehouse. Your external data sources will provide data in different formats, organizations, and varying degrees of completeness. The data transformation process ensures your data will be useful when it gets to your data warehouse or data lake.
Here are some of the common sub-processes that make up data transformation.
- Data cleansing: Cleans up errors, missing values, and inconsistencies in the data
- Data validation: Scans the data is scanned for errors andanomalies, and removes unusable data
- Data standardization: Applies the formatting rules of your data warehouse to the extracted data set
- Data deduplication: Removes duplicate or redundant data
- Custom data tasks: Runs any custom tasks on the data to meet specific company DataOps requirements
Now that your data is transformed, it’s ready to be loaded in the data warehouse.
Load data
The final step in the ETL process can happen in two different ways—full loading or incremental loading.
When you use the full load process in your ETL pipeline, you put all the transformed data into new records in your cloud data warehouse. This can be useful in some cases, but generally, it’s not recommended because full loading grows your datasets exponentially and makes your data warehousing harder to manage.
Incremental loading compares your incoming data with the records in your data warehouse and only creates new records if the loaded information is unique. This makes it much easier to manage your datasets over time with less effort and fewer resources.
ETL process example
Let’s say your company uses Salesforce as a CRM and Hubspot as a marketing tool. To combine data from those two solutions, extract data from each tool and move it to a data warehouse to turn it into useful models.
Here’s what that ETL process would look like:
1. Outline the business needs: Before starting the ETL process, it's important to understand the specific business needs for which the data will be used. What metrics do people expect? What information do they want in the end? How do they want to use it? Define the problem and what you know you need to solve it, then you can identify all the data needs for the project.
2. Identify the data sources: Now that you have defined the business problems, it’s time to list all the data sources you’ll need to extract. This may involve working with different business units, teams, or databases within an organization. You might have to do some detective work to find where certain data is stored. Make a list of all the possible data sources you’ll need to extract, and then you can build your ETL pipelines.
3. Build ETL pipelines: Use an ETL tool like Shipyard or Stitch to build pipelines for each data source. If you’re merging all customer data from your CRM and marketing tool, you may also need to bring in data from website analytics and other platforms. Once you have all the pipelines built, run each process in a cadence that makes sense for your DataOps team to manage.
4. Extract the data: Now you’re ready to extract the data from all relevant sources. Run your ETL pipelines and make sure you monitor the extraction process. Once you have the data you need from each source, it’s time to transform it.
5. Transform the data: You need to transform data to meet your data warehousing requirements. Typically, this involves adding new columns of data or adjusting values in existing columns based on your business needs.
6. Load the transformed data into a data warehouse: After transforming all of your raw, original data, you need to load it into a data warehouse or data lake for analysis and modeling. Remember to choose between fully loading data and loading it incrementally. Chances are, an incremental data load is the best fit for your business case.
7. Analyze data and build models: Now that all your data from Salesforce, Hubspot, website analytics, and other platforms is in the data warehouse, you can set your data analysts and machine learning specialists to work. They analyze the data and build models in the data warehouse that answer all the business questions your teams started with (and maybe find unexpected, valuable answers along the way).
This process will look different for every business problem you're trying to solve. But the foundation stays the same. The tools you use make the technical side of the ETL process easy.
Before we get to those tools, it’s important to know about another process that's very similar to ETL. It’s called ELT (extract, load, transform).
What’s the difference between ETL and ELT?
When you start searching for ETL tools, you’re going to find ELT tools, too. An extract, load, transform (ELT) tool does exactly what it sounds like—it loads your extracted data into a data warehouse and then transforms it using a data transformation tool like dbt.
The main difference between ETL and ELT is the location where data transformation happens. In ELT tools, it happens inside the data warehouse instead of in a staging area in the ETL process. Ultimately, you have to decide if ELT tools are a better fit with your data warehousing and data transformation capabilities.
If you’re not sure and want to bring all your data into a data warehouse right away, start with ETL tools.
For a more in-depth look at the differences, check out our writeup.
Which ETL tools should I use?
You can choose from enterprise ETL tools, open-source ETL tools, cloud-based ETL tools, and custom-built ETL tools. When evaluating, it’s important to define your use cases, budget, desired capabilities, and data sources to ensure you choose the right ETL tool for your business.
Here are some of our favorite ETL tools:
Fivetran data pipelines
When you collect data from many sources to feed your data science pipeline, Fivetran helps you securely access and send all data to one location. This tool allows data engineers to effortlessly centralize data so that machine learning algorithms can then cleanse, transform, and model the data.
Stitch ETL
Stitch delivers simple, extensible ETL built specifically for data teams. It delivers analysis-ready data into your data science pipeline. With Stitch, extract data from the sources that matter, load it into leading data platforms, and analyze it with effective data analysis tools. From there, your machine learning algorithms take over and find the patterns you need to solve business problems.
Shipyard data orchestration
Shipyard integrates with dbt, Snowflake, Fivetran, Stitch, and many more ETL tools to build error-proof data workflows in minutes without relying on DevOps. It allows your data engineers to quickly launch, monitor, and share resilient data workflows and drive value from your data at record speeds. This makes it easy to build a web of data workflows to feed your data science pipeline with many data sets.
Get started with ETL
Any of these ETL tools might be the missing piece of your ETL puzzle — it just depends on your current data infrastructure. We built Shipyard’s data automation tools and integrations to work with your existing data stack or modernize your legacy systems.
If you want to see for yourself, sign up to demo the Shipyard app with our free Developer plan—no credit card required. Start to build data workflows in 10 minutes or less, automate them, and see if Shipyard fits your business needs.