ETL modernization refers to the process of updating and improving traditional ETL workflows and systems to meet the evolving needs and challenges of modern data integration and analytics.

While traditional ETL processes have certainly served their purpose in the past, today they face several challenges that hinder their effectiveness in modern data integration and analytics. This includes: scalability issues when handling large volumes of data; the inability to support real-time or near real-time data integration; struggles with diverse data sources and formats; rigidity and inflexibility in accommodating changes or updates; insufficient mechanisms for data quality validation and cleansing, and performance issues and latency– just to name a few. 

Something had to be done. 

The History of ETL

The history of ETL dates back to the 1970s when companies began managing data in multiple data repositories. As organizations started to accumulate data from various sources, the need arose to extract data from these sources, transform it to meet specific requirements, and load it into a centralized repository for analysis and reporting. During this time, companies like Ab Initio, Prism, and Informatica emerged as pioneers in the ETL space, offering solutions to address these data integration challenges. These companies provided robust ETL tools and platforms that became the go-to choice for organizations looking to streamline their data integration processes.

However, as time passed, the data landscape became more complex. With the advent of data warehouses, organizations sought to consolidate and centralize their data in a structured manner to enable efficient querying and analysis. Data warehouses offered a way to store and organize data in a schema-oriented fashion, making it easier to extract insights from the data.

Additionally, the rise of data lakes introduced a new paradigm in data storage. Data lakes allowed organizations to store vast amounts of raw and unstructured data, providing a repository for storing diverse data types and formats. This opened up possibilities for exploratory data analysis and enabled data scientists to derive insights from data that had not been pre-defined or structured.

As data warehouses and data lakes became integral parts of the data ecosystem, ETL processes had to adapt. They needed to incorporate the extraction and transformation of data from these repositories, not just traditional databases. ETL tools evolved to support integration with data warehouses and data lakes, enabling organizations to extract and transform both structured and unstructured data from various sources.

The challenges with ETL as traditionally implemented

ETL is still a fundamental building block in the process of bringing data together and presenting a unified view of a business. However, as technology and data requirements have evolved, the traditional approach to ETL is showing its age and limitations.

Traditional ETL pipelines are still hand-rolled and hard-coded, which was fine when data was less diverse and storage was a scarce and expensive resource. But today, as businesses generate and collect vast amounts of data from numerous sources, the continued reliance on manual coding and inflexible pipelines has resulted in a complicated and brittle data infrastructure, where data engineers are constantly putting out fires and struggling to keep up with changing data needs.

The assumptions upon which the old ETL structures were built no longer hold true in the modern data landscape. The advent of cloud computing has revolutionized the industry, offering scalable storage solutions and the ability to process data in parallel at an unprecedented scale. Storage, which was once a scarce resource, has now become a commodity, enabling businesses to store and process massive volumes of data cost-effectively.

Furthermore, the demands for analytics have become more stringent. Businesses now require near-real-time data to make timely and informed decisions. Batch processing, the traditional approach to ETL, where data is collected and processed in scheduled intervals, is no longer sufficient to meet these demands. Real-time analytics, which provide instant insights and enable proactive decision-making, have gained prominence. 

To top it off, data corruption and poor fault handling, which can result from maintaining the traditional approach, disrupt the flow of critical information. It is essential to adopt modern ETL architectures that incorporate robust error handling mechanisms, data validation techniques, and fault-tolerant designs.

The components of ETL modernization

ETL modernization comprises several crucial components for optimizing data integration. Firstly, migrating workloads from legacy ETL systems to Apache Spark enables leveraging its distributed computing capabilities and support for various data formats. Secondly, investing in automation streamlines repetitive tasks, enhances productivity, and reduces errors. Thirdly, harnessing the power of cloud technology provides scalability, cost-effectiveness, and robust data security. Lastly, porting existing workloads over to Spark involves rearchitecting and optimizing ETL pipelines to unlock the benefits of real-time analytics and efficient data processing.

Let’s look at these in detail. 

Invest in automation 

The real disruption in data engineering comes through the utilization of agile practices, particularly when combined with automation. While traditional data engineering processes have often been characterized by manual interventions, lengthy development cycles, and rigid methodologies, the integration of agile principles with automation introduces a new era of efficiency and productivity.

Automation plays a vital role in keeping data flowing constantly. Manual interventions and repetitive tasks are prone to errors and can cause delays in data processing. By automating data extraction, transformation, and loading tasks, organizations can eliminate manual errors, reduce processing time, and maintain a consistent data flow. Automation enables scheduled or event-triggered data ingestion, ensuring that data is always available for analysis and decision-making.

Fragile data pipelines often suffer from issues like data corruption, inconsistencies, and inaccurate transformations. Implementing robust testing processes helps identify and rectify such issues before they impact the data flow. Unit tests, integration tests, and data quality tests can be automated to validate data at different stages of the ETL process. Anomaly detection techniques can also be employed to identify unusual patterns or data discrepancies, allowing for proactive remediation.

Keep in mind that it’s essential to store workflows as code on Git using 100% open-source APIs. Storing workflows as code provides version control, collaboration, and transparency, allowing teams to track changes and manage workflow configurations effectively. Additionally, workflows should include comprehensive tests, such as unit tests, integration tests, and data quality tests, which should also be stored on Git. Running tests on every change ensures that any modifications to the workflows can be thoroughly validated, providing confidence that the workflows will function as expected.

To maintain consistently high quality, continuous integration practices should be implemented, where tests are run on every commit. This ensures that any potential issues or regressions are identified and resolved promptly, contributing to the overall reliability and stability of the ETL process.

Furthermore, the deployment of ETL workflows should be automated to minimize manual errors and streamline the approval process. Automated deployment ensures that the responsible person's approval triggers a seamless and error-free promotion of the workflows. This eliminates the risks associated with manual deployment and helps maintain a smooth and efficient data pipeline.

Leverage cloud technology

Leveraging cloud technology is paramount in modern ETL practices. Cloud platforms offer a wide range of services and tools that enable organizations to scale their data operations, store data efficiently, and handle the ever-increasing volume.

One key aspect of cloud technology in ETL is the use of object storage with Delta Lake .. Object storage allows for storing data in its original format at scale. This flexibility ensures that organizations can efficiently store and access large amounts of data without the costly maintenance. Delta Lake optimizes the object storage layer to provide the foundation for storing data and tables in the lakehouse platform. By leveraging object storage and Delta Lake, ETL processes can operate on data directly from its source form, reducing overhead and minimizing cost.

Cloud platforms also provide tools like Docker and Kubernetes (e.g., AWS EKS, Azure AKS) that enable scaling of transformation tasks to meet the demands of incoming data. Docker allows for containerization, which enables consistent and portable deployment of ETL workflows. Kubernetes, on the other hand, allows for auto-scaling and dynamic resource allocation based on the incoming workload. These technologies empower organizations to handle varying data volumes efficiently, ensuring that ETL processes can scale up or down as needed to accommodate the data flow.

Port existing workloads over to Spark 

Porting existing workloads over to Spark requires careful planning and considerations. It is essential to plan financially before executing a migration plan to ensure that the necessary resources are allocated and budgeted effectively. Assess the costs associated with migrating to Spark, including infrastructure, licensing, training, and any potential impact on productivity during the transition.

Selecting the right tools can significantly facilitate the migration process– look for tools that simplify and streamline. Low-code tooling can be particularly valuable, enabling faster construction and conversion of pipelines with intuitive interfaces and pre-built components. These tools can help accelerate the migration timeline and reduce the complexity involved in rewriting or refactoring existing ETL code.

To ensure a successful migration, it is crucial to invest in educating your team on the new procedures and tooling. Provide training and resources to familiarize the team with Spark's architecture, programming paradigms, and best practices. This knowledge transfer empowers the team to effectively leverage Spark's capabilities and enables a smoother transition. Working closely with the team to address any concerns or questions prior to the migration can help build confidence and ensure a successful adoption of Spark.

Moreover, developing a well-defined migration plan is key. Prioritize workloads based on their importance and complexity, and establish a clear roadmap with specific milestones and timelines. Break down the migration into manageable phases, allowing for incremental progress and reducing the risk of disrupting critical business operations. Regularly communicate the plan to stakeholders and maintain open lines of communication to address any challenges or modifications along the way.

Accelerate your ETL modernization with low-code Spark

While ETL remains a vital component in bringing data together and providing a unified view of a business, the traditional approaches are struggling to keep pace with the evolving data landscape. The availability of cheap storage, the emergence of cloud computing, and the increasing demand for real-time analytics have rendered old batch processing approaches less effective. To meet the growing requirements of businesses, organizations must embrace modern ETL architectures that prioritize agility, fault tolerance, and the ability to handle diverse data sources effectively. By doing so, they can leverage the full potential of their data assets and gain a competitive edge in the dynamic business environment.

Adopting a low-code approach to Spark can dramatically speed up the development process, enabling organizations to achieve up to a 10x increase in efficiency. And with a tool like Prophecy, users can rapidly develop new data workflows in just five minutes.

Prophecy streamlines the development process by providing a user-friendly interface and a vast library of reusable components. This empowers data engineers to assemble and configure ETL workflows rapidly, accelerating the time from conception to deployment. The visual nature of low-code tools also enhances collaboration among teams, as stakeholders can easily understand and provide feedback on the workflow designs.

By adopting a low-code approach with Prophecy, organizations can unlock the benefits of Spark for data engineering at an accelerated pace. The agility and speed enabled by low-code development can significantly reduce time-to-market for new data workflows, ensuring faster availability of valuable insights. This approach also reduces the dependency on specialized coding skills, enabling a broader range of team members to contribute to the ETL modernization process. Try Prophecy today.