Why Big Data needs ELT

Summary

The article emphasizes the necessity of ELT (Extract, Load, Transform) in Big Data environments to ensure performance, scalability, flexibility with data formats, and ease of use, particularly with the emergence of Data Lakehouse concepts.

Abstract

In the realm of Big Data, the ELT approach is increasingly favored over traditional ETL (Extract, Transform, Load) methods due to its superior performance and scalability, especially when dealing with large datasets. The ELT process allows for the storage of raw data in a Data Lake, which can then be transformed as needed for various use cases such as Self Service BI and Machine Learning. This approach aligns with the Data Lakehouse concept, which aims to merge the benefits of Data Lakes and Data Warehouses. Additionally, ELT provides the flexibility to handle non-structured, semi-structured, and poly-structured data through a variety of interfaces, which is crucial for modern data processing needs. For smaller to medium-sized companies, purchasing a comprehensive solution like Talend or Alteryx may be more practical, while larger enterprises might opt for custom-built pipelines or specialized connectors. The article concludes by reinforcing the rise of ELT in Big Data, suggesting it is becoming an essential prerequisite for rapid data transformation and processing.

Opinions

The author believes that ELT is more effective than ETL in the context of Big Data due to its ability to handle large volumes of data without the need for transformation during transport.
The Data Lakehouse concept is presented as a beneficial hybrid that combines the strengths of Data Lakes and Data Warehouses.
Flexibility in handling various data formats is highlighted as a critical requirement for data integration tools, with the ability to access all possible interfaces being essential.
The choice between purchasing a data integration solution versus developing custom pipelines depends on the company size, the complexity of use cases, and available developer expertise.
The article suggests that graphical development environments are preferable for data preparation processes, unless there is a long-term commitment to maintaining developer knowledge in-house.
It is implied that ELT is not just a trend but a necessary evolution in data processing, particularly for Big Data projects that require swift transformation and analysis.

Modern Approaches like Data Lakehouses need ELT based Data Integration

In the area of Big Data, three particular factors are important in data integration: Performance and Scalability, Flexibility and Data Formats, Ease of Use. Here, the ELT process is usually the more effective one. If you’re wondering what Big Data is all about, this article might be of help.

Factor 1: Performance and Scalability

Large amounts of data consume a lot of computing power when they are transformed. It is often no longer possible or necessary to distribute the data in the transport, when the data is first stored raw in a Data Lake, for example. From there, they can be transformed for further use cases. For example, Self Service BI, ML and others. This would be a typical ELT approach.

Data Lakehouse Concept

This approach is called a Data Lakehouse. The Data Lakehouse should combine the advantages of Data Lakes and Data Warehouses into a hybrid concept. Read more about the Data Lakehouse concept here[1].

Factor 2: Flexibility and Data Formats

Non-structured, semi-structured and poly-structured data must be easily processed, stored and analyzed. In order to extract these different formats from the source systems, the software must be flexible and be able to open up all possible interfaces. There is the possibility to program pipelines yourself or to buy ETL/ESB software or a special connector.

Factor 3: Ease of Use

If it is a small or medium sized company, a purchased solution may make sense. If several different systems and possibly ESB and API use cases are to be implemented in addition to ETL, software such as Talend or Alteryx may make sense. If only a few data sources are to be accessed, for example for a Data Warehouse, a special connector makes more sense. If possible, the data preparation processes should be able to be graphically designed by comfortable integrated development environments. The use of script languages only makes sense if (long-term) developer know-how is available [2].

Summary

ELT is on the rise and is replacing ETL more and more, especially in the Big Data environment it is a prerequisite anyway. Only in this way, data can be transformed and processed quickly enough. Approaches such as Data Lakehouses are a suitable concept for making the raw data available for the further analysis process. If you want to get further into the topic and know what project phases are involved in Big Data projects, please click here:

Why Big Data needs ELT

Modern Approaches like Data Lakehouses need ELT based Data Integration

Factor 1: Performance and Scalability

Factor 2: Flexibility and Data Formats

Factor 3: Ease of Use

Summary

Sources and Further Readings