Introduction
Companies operate around the clock. This involves the website, back-end operations, supply chain, and all other aspects. Previously, everything was done in batches. Only a few years ago, operational systems would be interrupted while data was fed into a data warehouse and reports were performed. Reports are now coming in concerning where things stand right now. ETL is out of the question.
Hub-and-spoke models continue to be used in the majority of IT architectures. Operational systems supply data warehouses, which supply further systems. Specialized visualization software produces dashboards and reports based on “the warehouse”. But that’s changing, and system design and database architecture both need to be changed to adapt to shifts in business.
Another major transformation is the abandonment of standard ETL (extract, transform, and load) operations. Modern data platforms, contrary, rely on a variety of advanced tools and technologies to improve data processing. Hence, users get reliable and quick access to high-quality data. ELT and Zero ETL methods are key terms in this context.
The four points below are real reasons why ETL has grown less important in today’s data ecosystem.
1. ETL is time-consuming and resource-intensive
Conventional ETL operations frequently entail the movement of huge amounts of data through several systems and phases, such as data extraction of data sources, data transformation, and loading into a targeted data warehouse. These procedures, notably when placed on on-premise equipment, can be extremely slow, resource-intensive, and error-prone. This makes it challenging to meet the needs of modern data-driven businesses.
2. ETL is not flexible enough
Data must be available in real-time in today’s fast-paced business environment to offer companies the insights they need to make informed decisions. ETL methods can be time-consuming and rigid, making it difficult to rapidly adapt to changing business requirements or evolving data sources. As a result, ELT is frequently employed, in which data is imported without a first transformation, or even Zero ETL techniques, in which data is gathered or queried directly in the source system and schema changes are noticed and handled automatically.
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3. ETL is costly
Typical ETL systems cost a lot to operate and maintain in terms of hardware, software, and manpower. Many of these expenses may be eliminated by modern data platforms, allowing enterprises to focus on delivering benefits to their consumers rather than maintaining complex ETL procedures. In this case, built-in services and add-ons that can handle data integration are often both cheap and easy to build.
4. Self-service Data Preparation is supported by modern data platforms
Apart from Zero ETL and automated data integration services that handle (nearly) everything to integrate data, another key benefit of current data platforms is their ability to provide self-service data preparation, allowing users to readily access and edit data without expensive ETL processes. This strategy allows users to take a more active role in data preparation, allowing them to more effectively examine and evaluate data. Hence, after applying a Zero ETL or ELT solution for data integration, one frequently has methodologies and resources at their disposal to perform data preparation and transformation as needed. Either directly in the data warehouse using SQL, or through future business intelligence tools, which provide several possibilities for rectifying data in its raw form and, if required, adapting or enhancing it.
Conclusion
As a result of their high costs, slow speed, high resource requirements, and high rigidity, ETL methods are rapidly being pushed out of modern data systems. Modern data platforms, on the other hand, are evolving towards advanced technologies and methodologies that can provide quicker, more efficient, and adaptable services, allowing users to obtain the highest-quality data in real-time and thereby achieve better business outcomes.
