Skip to content

What is ETL (Extract, Transform, Load)?

  • Data

The Extract, Transform, Load process (short: ETL) describes the steps between collecting data from various sources to the point where it can finally be stored in a data warehouse solution. When large amounts of data are to be visualized, the individual stages come into play.

What is ETL?

Companies and organizations are faced with the challenge of having to deal with ever-larger volumes of data. This information also comes from many different systems with their own data structures and logic. This data should be stored as uniformly as possible in a central data warehouse in order to be available for data mining or data analytics.

For this information to be reliable and resilient, it must be pulled from the various source systems, prepared, and then loaded into a target system. All of this happens in the ETL process.

Das Bild zeigt den Extract, Load, Transform Prozess von ETL.
Extract, Transform, Load Process

The ETL Process Steps

To better understand the Extract, Transform, Load process, it is worthwhile to look at the individual phases in detail:

ETL Extract

Extraction is the process step in which data is retrieved from various sources and stored centrally. In this step, among other things, data quality checks are performed to ensure a clean state in the data warehouse. These checks can include, for example, matching the data type or looking for missing values. For example, checks could be made to see if all items that represent a price are also marked in USD. If the data has gross quality defects, it can also be rejected at this stage. If there are no or only a few deficiencies, the data is passed to the next stage, where the necessary changes are made.

The Extract step involves loading information from a wide variety of sources. These include, among others:

  • SQL and NoSQL databases
  • CRM or ERP systems
  • Files
  • Websites or API queries

ETL Transform

In this stage, all data is transformed into a structure that matches the data model of the data warehouse or the application. If there are still data quality problems, these are now processed. This includes, for example, filling in missing values or correcting errors. In addition, basic calculations are already made here with which the data can be summarized or prepared. This could include, for example, that the turnover is already aggregated on a daily basis and not each order is saved individually if this is required.

ETL Load

The data we prepared in the previous steps can now be loaded into the data warehouse or the target database. If there is already older information of the same type there, this must be supplemented accordingly or even exchanged. This is often done using a unique ID or by entering the time at which the information was saved. In this way, the data can be compared and the obsolete information can be specifically deleted.

ETL Applications

The ETL process is primarily used when you want to store data centrally in a data warehouse within a company or organization. For this purpose, it must be collected from various data storage and merged. It is not uncommon for the information to have completely different data structures which ask for some work in the transformation step.

When information is to be displayed in a business intelligence solution, the ETL process is used to ensure that the data is stored in such a way that it can be illustrated in diagrams as easily as possible and without errors.

The Extract, Transform and Load process steps can be used for a wide variety of applications. These include:

  • Central storage of information in a data warehouse.
  • Data visualization with the help of business intelligence
  • Merging of data from different systems

ETL challenges

The ETL stages pose the greatest possible challenge when many and very different systems and their data are to be migrated. Then it is not uncommon that the data models are completely different and a lot of work must go into their transformation.

Otherwise, the transformation step is also very costly if the data quality has deficiencies that must first be corrected. In some applications, missing values cannot be prevented at all and must still be handled accordingly. For example, if we have measurement data in a production line that measures components on the assembly line, but maintenance was performed between May 30, 2021, and June 1, 2021, and thus no data was collected during this period. Accordingly, we are missing part length measurements for all records during this period. We could now either disregard the records from the period or, for example, replace the missing fields with the average of the measurements from the days immediately before and after.

ETL vs. ELT

The ETL process has already been established for several years and decades. However, the amount of data that accumulates in many applications has also increased significantly over the same period. Therefore, it would be a very expensive matter to structure all the resulting data directly and store it in a data warehouse. Instead, in most cases, unstructured data is first stored in a so-called data lake. There, the data is stored in its raw format until it is needed for a specific application.

For this, the ETL process was modified to the so-called ELT process. As the arrangement of the letters already indicates, the first step in filling a data lake is the “load” step. In the data lake, the data is still unstructured, unsorted, and not corrected.

ETL vs. ELT | Source: LinkedIn

As soon as this information is required for a specific use case and the target data format has been defined, the “transform” process takes place in which the data is prepared. Hadoop, for example, can be used as the technology for the data lake.

This is what you should take with you

  • ETL contains important process steps for collecting data from different systems, preparing it, and storing it in a target system.
  • The individual stages ensure that the information is aligned with the data model and that certain quality standards are met.
  • ETL is primarily used when data is to be stored in a data warehouse or displayed in BI applications.

Other Articles on the Topic of ETL

  • Here you will find a comparison of popular ETL tools that automate many of the steps described.
  • Otherwise, many basic ETL steps can also be done within Python. You can find some basic commands in our related section.
close
Das Logo zeigt einen weißen Hintergrund den Namen "Data Basecamp" mit blauer Schrift. Im rechten unteren Eck wird eine Bergsilhouette in Blau gezeigt.

Don't miss new articles!

We do not send spam! Read everything in our Privacy Policy.

Cookie Consent with Real Cookie Banner