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Glossary
Data Management

ELT Extract, Load, Transform

ELT stands for Extract, Load, Transform and is a data integration process that migrates data from various sources into a target system.

Meaning in detailContact us now

What are the three phases of the ELT process?

1. Extract

  • Data from various sources extract.
  • Sources can databases, files, cloud applications, social media platforms, and other data storage.
  • Various techniques such as APIs, connectors, or custom scripts can be used.

2. Loading

  • Load extracted data into the target system.
  • The target system can be a Data warehouse, Data Mart, Data Lake or be another data storage system.
  • Various techniques such as APIs, batch jobs, or streaming processes can be used.

3. Transform

  • Transform data in the target system.
  • Transformations can include cleansing, standardization, aggregation, enrichment, and other tasks.
  • Flexibility, as transformations take place closer to the time of analysis.

What are the benefits of ELT?

  • Faster and more efficient, especially with large amounts of data. Reduced pre-processing in the target system.
  • More flexible when adapting transformations on analysis requirements. Subsequent adjustments are possible.
  • Lower hardware requirements: Transformation distributed in the target system.
  • ‍Well suited for cloud environments: Scalability and flexibility through cloud infrastructure.

What are disadvantages of ELT?

  • Higher complexity of the target system: Transformations require additional resources and processes.
  • ‍Potential impact on data quality: Transformation after loading can mask errors.
  • ‍Requires good data knowledge: Transformations must be defined in the target system.

When is ELT the right choice?

  • Large amounts of data and complex transformations.
  • Flexible analysis requirements with potential changes.
  • Cloud-based data environment.
  • Focus on rapid data integration and availability.

What alternatives to ELT?

The choice of method depends on specific data requirements, which data quality, flexibility, costs and scalability. We would be happy to watch this together in the architecture and data management on.

Note: This glossary was created and maintained with the support of AI technologies such as Gemini and ChatGPT.

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