Zurück zum Glossar

ETL (Extract, Transform, Load): Data Integration Explained Step by Step

ETL stands for Extract, Transform, Load – a three-stage data integration process. It converts raw data from heterogeneous sources into a consistent target format that can be used for analytics and machine learning. Typical target systems include data warehouses or data lakes. For anyone needing to consolidate data from multiple systems and prepare it for BI or compliance requirements, ETL is indispensable.

‍

What is ETL?

ETL describes a rule-based integration process with three distinct steps: Extracting, Transforming, and Loading. The goal is to build a consistent dataset from distributed, inconsistent raw data. This dataset forms the basis for subsequent analyses and machine learning workflows.

‍

How does ETL work?

Step 1 – Extract

Relevant data is copied from the source systems into a staging area. This temporary storage, also known as a landing zone, receives the raw data before further processing. According to AWS, the staging area is often temporary and can be cleared after successful completion; however, in case of error resolution, it also serves as an archive or reference area. Depending on requirements, three extraction modes are available: full extraction (reload all data), incremental extraction (only changes within a specific period), and update notifications.

‍

Step 2 – Transform

Transformation takes place in the staging area. Fundamental steps include data cleansing, deduplication, and mapping source data to the target format. Specific examples include: removing erroneous entries, replacing empty fields with defined values, standardizing character sets, units of measurement, or date values (e.g., converting kg to pounds). Additionally, there are more complex transformation types:

     
  • Derivation: Calculation of new values from existing data, e.g., profit from revenue
    •  
  • Joining: Linking data from different sources, e.g., aggregating costs across vendors
    •  
  • Splitting: Dividing attributes, e.g., separating first and last names
    •  
  • Summarization: Aggregation of many values, e.g., consolidating invoice values into a Customer Lifetime Value (CLV)

‍

IBM adds that transformations often use business rules to meet BI and compliance requirements. Depending on governance specifications, encryption and the protection of sensitive data may also be part of this step.

‍

Step 3 – Load

The transformed data is transferred from the staging area to the target system, typically a data warehouse. According to AWS, this step is usually automated and batch-oriented. During initial loading, all data is transferred in the first run. Incremental loading only incorporates changes (delta) since the last successful query – either as a streaming variant for timely decisions or as a batch variant for large data volumes.

‍

Advantages of ETL

     
  • Improved Data Quality: Cleansing and validation reduce errors in the dataset.
    •  
  • Consolidated View: Multiple databases and data types are merged into a unified data foundation.
    •  
  • Compliance Support: Consistent validation helps ensure compliance with legal standards.
    •  
  • Automation: Repetitive tasks such as moving, formatting, and standardizing data are automated, saving time.

‍

Opportunities and Risks

ETL requires a precise definition of requirements at the beginning of the project. Analytics goals and the target schema must be established early, as the transformation builds upon these specifications. This distinguishes ETL from the related approach ELT (Extract, Load, Transform)In ELT, data is first loaded into the target system and only then transformed. A separate staging area for transformation is eliminated because the conversion takes place directly within the target database. Those who don't yet fully know their requirements or prefer flexible transformations should consider ELT.

‍

Conclusion

ETL converts raw data from heterogeneous sources into a consistent target format in three clearly defined steps. The separation of extraction, transformation in the staging area, and loading, along with the use of defined business rules, ensures data quality, consistency, and compliance. For analytics and machine learning projects, a well-implemented ETL process provides a reliable data foundation.