The ETL Process: Extract, Transform, Load
Analyzing your data is one of the best ways to evaluate performance and make better business decisions. But in order to analyze your data effectively, you need to aggregate, cleanse, and store your data from multiple sources. That's where an ETL (Extract, Transform, Load) tool can help. In this article, I'm going to explain what ETL tools are, how they work, and how they can help you streamline and automate your data transformation processes.
What is ETL?
ETL is an acronym that stands for Extract, Transform, Load. Essentially, it's the process your data has to go through before you an analyze it. First, you extract the source data from different platforms, then transform the data into a different format, and finally, load the data into a data warehouse.
Typically, organizations use ETL tools to help them manage large volumes of data from multiple sources, like ad campaigns or their CRM. You can pull data from as many locations as you need, create a data flow based on preset parameters, and get a clean set of data at the end. ETL tools are the quickest and most efficient way to manage all that data and turn it into a usable format.
Should I Use an ETL Tool?
There are numerous reasons why companies turn to ETL tools to streamline their data transformation processes. Some of the most common reasons include:
- Making data easier for leadership and outside stakeholders to understand.
- Handling more data from more sources than manual processes can.
- Customizing and automating the data aggregation process.
- Scaling as you generate more data and run more campaigns.
- Improving efficiency, saving money, and reducing the number of working hours needed on data transformation.
- Formatting the end data exactly how you need it.
- Easily uploading data into a warehouse, where you can easily pull reports.
- Reducing the margin for human error.
The ETL Process Explained: Extract, Transform, Load
To help you better understand how the ETL process works, here's a breakdown of each stage.
The first stage is extraction. If you have many data sources, such as files, databases, spreadsheets, etc. that you want to transform into a different format, an ETL tool will automatically aggregate all this data for you. This data goes into what's called a "staging area," where the information is temporarily housed.
Specifically, there are two main types of extraction methods: logical and physical.
When it comes to logical extraction, there are two subtypes.
Full Extraction—Full extraction is used when you are extracting data for the first time, and all of the data is extracted at the same time.
Incremental Extraction—Incremental extraction is used to extract data from the last successful extraction. In an ETL tool, you'll be able to see the timestamp of every data extraction, and view recent changes in a table.
There are two types of physical extractions: online and offline.
Online Extraction—Online extraction is when the ETL tool has a direct connection to the data sources. Improvado uses online extraction to connect to all your different data sources automatically.
Offline Extraction—Offline extraction is when the data isn't extracted directly from the source. Instead, it gets aggregated into a flat file, which can be used to create charts and analyze the data manually.
The transform stage is where data transformation takes place. This is when you apply the filters, functions, and any other criteria you want. As the user, you'll have goals and clear visions of how you want the data presented upon completion. Because ETL tools are highly customizable, it's up to you to tell the tool what you want.
For example, you may want to combine several data sets to present all the data in uniform. Or, display sales data in a way that's easy to analyze and spot strengths and weaknesses for geographic areas, sales teams, products, and so on.
Some of the key benefits of this stage over manual transformation is telling the software to make global changes to the data sets. For example, you can eliminate special characters, completely change the layout of data, remove erroneous records, and more.
Once the data transformation process is complete, the final stage is loading the data into a data warehouse. Loading large amounts of data into a warehouse makes it easy to access and use the data. Regardless of how many different types of data went through the ETL process, the result is one clean set of data that is ready to use.
Benefits of Using ETL Tools
If you still need convincing that you should be using ETL tools to help with your data processes, let's look at some of the other main benefits.
Data transformation is a time-consuming process when done manually. Writing bits of code for each process, managing data transformation, and developing internal processes requires a lot of time and management. An ETL tool allows for a more "hands-off" approach, so you don't have to invest as many hours into managing the process.
Many companies recruit a point person to manage their different source data types. For example, one person might be in charge of overseeing email marketing data, and another is in charge of Google Adwords data. That can create inconsistencies and errors when gathering the data.
As a result, a lot of organizations turn to ETL tools because they know the data they are analyzing will be consistent and accurate. It significantly reduces the risk of human or processing errors.
No Developer Expertise
One of the biggest perks to using an ETL tool is that you don't need to work with a developer. There's no coding, custom scripts, or languages you need to know. The best ETL tools on the market have all the built-in features and tools you'll need to set up and execute data transformation yourself.
Time is money, and efficient processes save time. ETL tools are capable of saving organizations many hours every week by speeding up their data transformation processes.
Implementing ETL tools early on is just as important as bringing them in when your data processing tasks become too difficult to manage. The software enables you to scale up your processes without the need for rewriting methods that were previously in place.
Companies today need to take advantage of their data in order to stay competitive. But you don't need to rely on time-consuming manual processes to gain valuable insights from your data. ETL tools can help you save time, money, and reduce the risk of human error.
If you're interested in learning how you can benefit from an ETL tool, check out a list of Improvado's integrations to see how easy it is to aggregate your data from various platforms in minutes.