Transitioning from ETL to ETLT: Mastering Data Transformations in the Age of Big Data

In the data-centric world of today, businesses and organizations are constantly striving to harness the power of data to drive intelligent decision-making. Data processing is a critical step to achieving this. Traditionally, data processing and integration strategies were dominated by ETL (Extract, Transform, Load), but as data landscapes evolve, different variations have emerged transitioning from ETL to ELT (Extract, Load, Transform), then to X-ETL (variations such as Reverse ETL, Zero-ETL), and now to the promising EtLT (Extract, transform, Load, Transform). The once-popular ELT, anchored in the Hadoop era, has now fallen behind with the advent of real-time data warehouses and data lakes. Its successor, the EtLT architecture, is fast becoming the go-to framework for real-time data loading in the modern data landscape.

Understanding EtLT

EtLT, an extension of the traditional ETL process, adds an additional transformation layer after the loading stage. In the world of big data and real-time analytics, data needs are more complex. Raw data comes from a myriad of sources and in different formats. While ETL is good for structured data, it often falls short when dealing with large volumes of unstructured and semi-structured data. The small ‘t‘ in the EtLT architecture focuses on data normalization, which transforms the extracted data from its complex, heterogeneous form into structured data that can be efficiently loaded into the target data storage system. The large ‘T‘, on the other hand, allows for further cleaning, integration, and structuring of data, making it suitable for complex analytical purposes.


The Evolution of Data Processing Architectures

The dawn of data warehousing started with the ETL era from 1990 to 2015, where data was extracted, transformed, and then loaded into data warehouses. However, this led to high hardware costs, particularly for large data volumes, and took longer to fulfill business requirements as all processing was performed by data engineers.

The advent of big data around 2005 sparked a shift from ETL to ELT, as data volumes swelled and hardware costs soared. Despite ELT‘s ability to utilize high-performance computing for large data volume processing and handle complex business logic with SQL, it struggled to cope with complex data sources and real-time data warehousing requirements.

The rise of Operational Data Store (ODS) as a transitional solution, and the fragmentation of the Enterprise Data Community, marked the early emergence of the EtLT architecture, dividing tasks among different roles based on complexity.

Today, the EtLT architecture has evolved in response to changes in the modern data infrastructure, characterized by Cloud, SaaS, and Hybrid Local Data Sources; Data Lakes and Real-time Data Warehouses; the rise of Big Data Federation; a boom in AI applications; and the fragmentation of the Enterprise Data Community.

Benefits of EtLT

EtLT offers several significant benefits over traditional ETL:

  1. Greater Flexibility: EtLT gives businesses the flexibility to perform additional transformations on their data after it has been loaded into the warehouse. This is beneficial for businesses that work with real-time or near-real-time data and need to make modifications quickly.
  2. Enhanced Efficiency: EtLT can be a more efficient approach when working with big data platforms like Hadoop or Spark. It enables the heavy lifting to be done in the data lake, taking advantage of the scalability and power of distributed processing. Moreover, EtLT balances the requirements for real-time data processing and streamlines the handling of vast and diverse data sources.
  3. Superior Data Quality: The extra transformation stage helps to improve data quality, ensuring the accuracy, consistency, and validity of data. This leads to more accurate insights and better decision-making.
  4. Lower Latency: For businesses that require low-latency analytics, EtLT can deliver processed data more quickly. By conducting transformations within the data lake, it can significantly reduce the time it takes to deliver insights.
  5. Cost-effective: EtLT can be more cost-effective, especially when dealing with vast amounts of data. By leveraging the resources of big data platforms, businesses can reduce the computational demand on their data warehouses.

Who Handles Each Phase?

In the EtLT architecture, the responsibilities are split based on the expertise required at each phase:

  1. EtL Phase: Data engineers handle this phase. Their expertise is focused on converting complex and diverse data sources into a structured format for subsequent analysis and utilization. They focus on the accuracy and timeliness of data but do not necessarily need to understand the intricacies of business metric calculations.
  2. T Phase: This is typically the realm of data analysts, AI engineers, and business SQL developers, who possess a deep understanding of enterprise business rules. They focus on the transformation of structured data into actionable insights by translating business rules into SQL statements.

The Prominence of EtLT in the Modern Data Landscape

As we continue to navigate the evolving landscape of data processing, it’s clear that EtLT will play a significant role in shaping future frameworks and architectures. There are several open-source implementations of the EtLT architecture in the current data landscape, including DBT, Apache DolphinScheduler, and Apache SeaTunnel. These provide valuable tools for data engineers, data analysts, business SQL developers, and AI engineers, allowing for rapid conversion of complex and heterogeneous data into structured, usable forms.


As we move deeper into the era of big data, it’s clear that traditional ETL processes may not suffice for the evolving needs of businesses. EtLT offers an improved approach to data processing by adding an extra layer of transformation, which in turn enhances flexibility, efficiency, and data quality. As with any technological implementation, organizations should thoroughly evaluate their data needs and infrastructure before transitioning to an EtLT strategy. Ultimately, the goal is to harness the full power of data to drive informed decision-making, and EtLT, with its open-source implementations like Apache SeaTunnel, offers a powerful tool to help achieve this.

In this complex and rapidly changing data landscape, it’s crucial to partner with experts who can guide you through your data processing journey. Anyon Consulting is well-equipped to help your organization harness the power of EtLT. Our team of experienced data engineers and analysts can assist in implementing and optimizing the EtLT architecture, tailor-made to meet your specific needs. From understanding your business requirements and current data infrastructure, to designing and implementing an EtLT process that delivers quality insights in real time, Anyon Consulting is committed to helping you turn your data into a strategic asset. As we work together, our goal is not just to deliver immediate results, but to equip your team with the skills and knowledge necessary to leverage the full potential of your data, today and in the future. For more information about our services and how we can assist you, please don’t hesitate to contact us. We look forward to exploring how we can transform your data journey together.

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