Reverse ETL as a data-driven secret of success

Reverse ETL: Key component in the modern data stack

A data warehouse makes it possible to remove data silos, but ironically, it can become one itself. The collected data is often useless in the warehouse, because its continued use presents companies with a major challenge. The integration of reverse ETL pipelines is helpful.

Modern companies store huge amounts of transactional and analytical data in Data warehouses such as Snowflake, Google BigQuery, Amazon Redshift, or Azure Synapse. The data warehouse has developed into a company's central nervous system and functions as a single source of truth, in which all data is saved and brought together in an organized manner. By investing in a data warehouse, companies are, among other things, forcing the removal of data silos, which are known to be a blockage to efficient and powerful corporate orientation.

Because if important data cannot be found centrally in one place, but is incomplete and distributed in various tools, specialist departments will ultimately work with different information and perspectives. With reverse ETL pipelines, data from the data warehouse is integrated into the tools and processes used by employees on a daily basis in order to be able to exploit their full potential.

Data integration approaches: The derivation of reverse ETL

To understand reverse ETL, a look at the ETL and ELT integration approaches is helpful.

ETL describes the sequence of classic data pipelines: Extraction — Transform — Load. Here, raw data is retrieved, transformed and fed into a database. The process of transformation is cleaning, filtering, formatting, enriching, and organizing this data to make it easier to build models in the database.

However, most cloud-based databases follow the modern ELT approach: Extraction — Load — Transform. The data sources are transferred directly to the target system and only then transformed. This approach is particularly beneficial for large amounts of data, as scalability is supported and uses fewer resources.

Reverse ETL On the other hand, refers to an integration approach in which current data is extracted from the data warehouse, transformed for further use and loaded into operational business applications or ready-to-use systems, in contrast to the approaches described above.

Why move data out of the warehouse when it is already stored there?

Reverse ETL enables companies to operationalize data in the systems and processes they use and take meaningful action with verified and trustworthy data. According to the “close the loop” principle, relevant and useful findings are obtained and then used and used in a targeted manner. In addition, a company-wide uniform definition of core metrics can be ensured. Benefits of reverse ETL include:

data automation
Work processes can be automated and transferred directly within the operating system. Reverse ETL enables an efficient and optimized structure of workflows and the reduction of time-consuming manual requests for data.

Reducing cross-team data silos
A verified database facilitates cross-departmental collaboration and prevents errors. The data required for decisions and strategies does not have to be searched for, but can be integrated directly into the appropriate tools and systems.

Increasing customer satisfaction
With reverse ETL, the customer experience can be improved and have a positive impact on sales. This is how, with the help of existing data, the customer experience becomes a Customer Data Platform personalized to find simple solutions to problems that customers may be facing. In addition, personalized marketing campaigns can be played out to potential customers and expand the customer base.

The Role of Reverse ETL in the Modern Data Stack

Reverse ETL is an elementary component for the adequate preparation of data within the Modern Data Stack Dar. The Modern Data Stack is a complex system of modular tools that fully process a company's data, from connecting data sources to data storage and data visualization. This system follows the principle of reliably filtering out data along the digital value chain so that it can be used strategically, scalably and efficiently. Ideally, data management follows the following principle:

Step 1: Acquisition

As explained at the beginning, investing in a cloud data warehouse as a single source of truth is essential for modern and data-driven companies. This primarily serves as a central storage location for all data that comes from different sources.

Step 2: Preparation

Before the collected data can be used, it must be made usable for various systems. This is also where reverse ETL pipelines come into play, which close gaps and integrate data from the data warehouse into target systems. Transformations and models prepare the data in such a way that its format, values, and properties are optimally prepared for analysis.

Step 3: Analyze

In order to be able to fully exploit the value of the data, an analysis process including the creation of visualizations should be established. This makes it possible to derive insights, discover correlations and make forecasts.

Reverse ETL for agile data warehouse management

A bidirectional ETL approach makes it possible to integrate data and the resulting insights more quickly within the company and into the tools used on a daily basis and to place them with decision makers. According to this, reverse ETL tools are key components of the modern data stack, avoid the creation of data silos in the data warehouse, automate manual data queries and increase efficiency. In addition, updated data can ensure a more seamless and personalized customer experience and therefore increase customer satisfaction.

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