Modern data platforms have changed dramatically in recent years. Classical planned traditional on-premise Data Warehouses are increasingly shifting towards more cloud-based Data Lakes and Lakehouses.
Another notable change is the move away from traditional ETL (extract, transform, load) processes. Instead, modern data platforms rely on a range of advanced tools and technologies that streamline the data processing and preparation process. As a result, users gain faster and more reliable access to high-quality data. Keywords here are ELT and Zero ETL approaches.
The following four statements are key reasons for why ETL is becoming increasingly less important in the modern data landscape.
Reason 1: ETL is slow and needs lots of resources
Traditional ETL processes often involve moving large volumes of data across multiple systems and stages, including extraction from source systems, data transformation, and loading into a target Data Warehouse. These processes can be slow, resource-intensive and error-prone, especially when installed on on-premise infrastructure. This makes it rather difficult to keep up with the demands of modern data-driven enterprises.
Reason 2: ETL is not agile enough
In today’s fast-paced business environment, data must be available in real time to provide organizations with the insights they need to make informed decisions. ETL processes can be slow and inflexible, making it difficult to respond quickly to changing business needs or evolving data sources. Therefore, often ELT is used, where data is loaded without an initial transformation or even Zero ETL approaches where data is collected or queried directly in the source system and things like schemas changes are detected and handled automatically.
Reason 3: ETL is expensive
Traditional ETL tools also require significant investments in hardware, software, and personnel to operate and maintain. Modern data platforms can eliminate many of these costs, allowing organizations to focus on delivering value to their users rather than managing complex ETL processes. Here, built-in services and add-ons which can take over the data integration are often cheaper and easier to implement. An example for that is the Google Data Stream which can handle real time CDC without much programming or installations.
Reason 4: Modern Data Platforms support Self Service Data Preparation
Besides Zero ETL and automated data integration services which handle (almost) everything to integrate data, another key benefit of modern data platforms is their ability to support self-service data preparation, allowing users to easily access and manipulate data without complex ETL processes. This approach enables users to take a more active role in data preparation, allowing them to explore and analyze data more efficiently. So after using a Zero ETL or ELT tool for data integration, you often have techniques and tools which you can use to realize data preparations and transformation if necessary. Either via SQL directly in the Data Warehouse or in subsequent business intelligence tools, which also offer many options for correcting data from its raw form and, if necessary, adapting or enriching it.
In conclusion, one can state that modern data platforms are nowadays shifting away from ETL processes due to high costs, slow pace, high amounts of resources and a high state of inflexibility. These data platforms are rather moving towards advanced technologies and approaches that are able to offer faster, more efficient and more flexible services so that users can gain access to high-quality data in real-time and, hence achieve greater business outcomes.