Data is perhaps one of the vastest subjects to consider. There is a lot to think about and know. Every little piece takes into consideration things like data quality, data integrity, raw data, the data source, and more. All data has a destination and an end goal. The question is, how can you take information from the data warehouse, extract what you need to know, and put it all into a relational database to use in the format that you need?
ETL or an ETL tool can come in handy here. This process is all about data transformation and learning how to use analytics and the data pipeline to your favor. In the following guide, we will cover the steps in ETL and just what each step means. You can even consider an ETL tool once you understand a bit more about the subject overall.
ETL is an acronym that stands for “extract, transform, load.” The process is all about moving data from one data warehousing location into an open-source system so that you can then use data analytics and other tools for best practice decisions. The idea is that you understand how to use this data in your toolbox. An ETL tool or ETL service might also be helpful for data management and analytics.
Now, let’s look at what the different steps are and what they mean.
The first part of the ETL process is to extract. In the extract workflow step, you take from the source data or source system. This is the original source that the data comes from. Extraction is the act of pulling out the data in ETL. In some cases, you might even be extracting from different sources to later compile your data together to transform data. Extracting data can work through connectors. As you walk through the workflow, you will follow a data integration process to bring it all together. It simply starts with the source code.
Once the unstructured data is pulled from the original source, you can transform it for your needs. In this part of the process, you take unstructured data and clean it up so it will look a different way when it is put to use. This will make it compatible with a different system and make the data more user-friendly.
This is your chance to adjust scripts, clean up the data set, and create big data from your sources to use in compilation with each other. The data flow needs to be such that the end system can accept and recognize the data. This might even require changing the style or source to prepare for the data load in your next phase.
Some data profiling of your end database might be helpful as you move from the source system to a target data factory of some sort.
Finally, you will use your ETL tool or a data integration service for the load phase. Your transform step prepared the data for the staging area, and now it’s time for the end data load. By this time, your data store will be in the proper format for your data warehouse destination.
If you followed the data pipeline properly to prepare your data types to come together, you should now have detailed information in a format that you can use to achieve high performance on a different scale. Your ETL tool or data integration software should be able to help with most of the heavy lifting so you can get on to the next step in your project.
ETL is designed to help manipulate data from one source to a destination system. With an ETL tool, the ETL process is simplified significantly. The sooner you can get your data integrated, the sooner you can try out your end processes to put the data to use. The data transfer process doesn’t have to be a massive chore when you learn how to make the ETL process smooth and effective.