What is the Zero-ETL Approach?

In the field of Data Engineering, we often hear about the so-called Zero- ETL approach, big cloud providers like Google, Microsoft & Co. They are bringing new services and tools to support this approach. In this regard, AWS is also entering the war against ETL like seen in the article linked down below:

However, what exactly is this approach and how does it differ from classical ETL processes? This article offers theoretical background on the Zero-ETL approach and shows how this can help to improve the work within the field of Data Engineering.

Definition

Let's start with the definition of this approach: The Zero-ETL approach is a method for building data pipelines that aims to eliminate the need for traditional extraction, transformation, and loading (ETL) processes and the tools used to perform them. This approach has its origin in the idea that data has to be stored and processed or even just analyzed within the the same source system e.g. with SQL. Also it should contain its original format, so that the need of a difficult data transformation or movement is avoided.

Benefits of the Approach

The main benefit of the Zero-ETL approach is the fact that modern cloud-based Data Warehouses, Data Lakes or even Data Lakehouses make use of the integrated services in order to be able to directly analyze data from various other sources. So instead of filtering data from SQL or NoSQL databases, processing them and afterwards position them into a Data Lake or Data Warehouse, etc. two times, one can just easily have the access to the data in a direct manner. This has several advantages such as:

• Less effort for building up data pipelines, especially less effort if you have previously programmed them.
• No data storage that is existing two times. This only takes up unnecessary space and costs valuable money. Also this inefficiency leads to a overall weaker performance.
• In some cases maybe also no expensive data integration solutions like talend, alteryx & Co.
• The Zero-ETL approach makes it possible for companies to collaborate with data in real-time rather than waiting for the extraction, transformation and loading of the data into another system.

Challenges of the Approach

One of the biggest challenges of the Zero-ETL approach is that it requires significant upfront planning and design. Organizations and professions such as the one of the Data Engineer need to consider their data architecture, processing requirements and scalability before implementing a Zero-ETL pipeline. Also, the subsequent processes still need data transformation and aggregation logics. If the data is for example directly analyzed within the sources or loaded without any transformations, then this data has still to be processed by Data Analysts and end users in order to be understood in the correct way.

Impact for Companies and their Processes or Products

The Zero ETL approach can be further developed by embracing advanced data virtualization techniques and real-time data processing capabilities. By leveraging virtualization, organizations can access and query data from multiple sources without the need for extensive ETL processes. Additionally, integrating real-time data processing technologies such as stream processing and event-driven architectures can enable organizations to ingest, transform, and analyze data on-the-fly, reducing the reliance on batch-oriented ETL workflows. This approach offers greater agility, reduces data latency, and allows businesses to make faster, more informed decisions based on current data insights.

Summary

In conclusion, the Zero-ETL approach indeed makes sure that there is less effort when integrating the data and, above all, can also lead to lower costs because of less duplicate data storage or any other additional tools. In order to make the data usable for use cases in the end, however, efforts are probably still necessary. The approach can bring significant advantages, especially for ad-hoc analyses and the analysis of real-time data. For classic Data Warehouse processes and BI analyses, however, data transformations will continue to be necessary, although it must be said here that the large providers are also gradually offering solutions such as metadata and services.