In recent years, companies have discovered that data is one of their most valuable assets. They’re able to use their big data insights to guide investment decisions, optimize best practices, and even refine supply chains. The possibilities are endless with big data. However, it’s difficult to get the most out of your big data when you have different types of data from vastly different source systems. That’s where data integration comes in.
Data integration is a process by which data scientists consolidate raw data and transform it. ETL is one of the more popular integration methods. In this article, we’ll discuss the basics of ETL and the purpose of ETL diagrams. Let’s dive into the world of data transformation and integration—ETL style!
What is ETL?
If you’re not a data scientist, then it’s likely that the first thing that comes to your mind when you hear ETL is, “What in the world is ETL?” In short, it’s an acronym for extract, transform, load, which is a data integration process. It’s only a three-step process, but it can take a while depending on the volume of data involved in the transformation process.
Speaking of the transformation process, it’s the most critical part of ETL processes. The first step is extraction, during which data scientists extract data from various sources. The transformation process is where they transform data to fit the format of new systems. During this step, you can pick a programming language and format for all of your data types.
The final step is the load process. During the loading process, data scientists load the new data from the ETL system into a data store or data warehouse for the future.
What are some of the benefits of ETL?
ETL is one of the most labor-intensive integration strategies, but it also has some strengths. One of the best things about ETL is it gives your company complete autonomy over the integration process. You can choose what data and which sources to include. You can implement your own business rules for data sets, and you can also create a logical data warehouse in which you can store your newly structured data. So, there are plenty of reasons to do the work that comes along with ETL integrations.
What is an ETL diagram?
Sometimes, visuals are the greatest learning aids. ETL diagrams are visuals that explain the different machines and steps involved in ETL processes. You can use one of these diagrams to map out your ETL processes and to help others explain your ETL processes.
You’ll notice that each ETL diagram will have at least three nodes and paths along which data travels. At each node, something different happens. The first part is where data extraction takes place. The next step is where the extracted data undergoes data transformation. Finally, the diagram will show the destination system where you load the data.
How can you use an ETL diagram?
It’s a good idea to create a diagram to go along with your workflows during ETL integration processes. One common problem integration teams face during ETL processes is maintaining the integrity of large data sets. Drawing a diagram of your ETL systems and processes also enables you to post workflows and business rules as a constant reminder of the goals of the project and the proper data transformation format.
ETL is one of the oldest and most trusted integration processes. It’s labor-intensive and time-consuming, but the rewards are numerous. Using the right ETL tool can help you maximize your business intelligence and increase your ROI on analytics processes. An ETL diagram is merely a tool to help you understand ETL processes and platforms so you can capitalize on them. So, are you ready to implement ETL in your big data operations
