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Four Tips to Modernize Legacy ETL Processes

Modernizing ETL, especially the underlying infrastructure that supports it, across all your lines of business is vital for enhancing downstream analytics and decision-making.

Organizations must optimize their data workflows to quickly make better decisions and stand out in a competitive market. With the pivot towards cloud infrastructure, the need for modernization has never been greater. Central to this digital evolution is the intensified focus on artificial intelligence (AI) and analytics to propel businesses toward heightened efficiency and agility. However, doing so is not easy. 

For Further Reading:

Why Modernizing ETL Is Imperative for Massive Scale, Real-Time Data Processing

Calculating Your ETL ROI

Data Wrangling Versus ETL: What’s the Difference?

Extract, transform, and load (ETL) serves as a data integration process, collecting data from various sources, standardizing it, and loading it into a target destination for analysis and reporting. Crucial for decision-making, ETL bridges the gap between raw data and actionable insights. Though often overlooked due to its perceived mundane nature, ETL is the unsung hero that ensures the delivery of the clean, high-quality data that is imperative for powering the latest use cases, such as generative AI.

Traditionally, engineers within data platform teams have managed data movement and manipulation across systems. However, in today’s data-centric landscape, it's imperative for all data users in the business to expertly handle data transformation and interpretation on their own. Modernizing ETL, particularly the underlying infrastructure that supports it, across all lines of business is essential for enhancing downstream analytics and decision-making.

Challenges with Legacy ETL

Legacy ETL, which puts the burden on engineers due to complex coding requirements and specialized tools, is creating several issues.

Lack of extensibility. The lack of extensibility in legacy ETL systems poses significant challenges to maintaining order and efficiency within data pipelines. Without a centralized platform for publishing frameworks and standards -- and subscribing to them -- organizations are thrust into a world of chaos where pipeline development operates without standardized guidelines or compliance measures. This environment introduces complexity into the development process and increases the risk of non-compliance with regulatory requirements or internal policies.

This further impedes developer performance and productivity. Without standardized out-of-the-box components, developers are forced to reinvent the wheel by building essential functionalities from scratch for each project. This lack of standardized packages introduces room for error and performance/cost issues, impacts developers new to the enterprise as they struggle with understanding existing logic, and creates compliance risk. 

The dependence on these legacy ETL tools also restricts flexibility and innovation because migrating away from legacy tools entails significant effort and resources. For example, an organization’s ability to adopt ETL for unstructured data can be blocked if its legacy ETL tool doesn’t provide the required components to support it. With more flexibility, an engineering organization can simply write the logic themselves or adopt an open-source community package to meet their needs. The longer the legacy tool is used, the more challenging a migration to a modern solution becomes -- especially if you have millions of lines of code written in a proprietary format and the engineers who wrote it have left the company. 

Lock-in. Legacy ETL tools also create vendor lock-in by generating proprietary code that has limited interoperability and portability. Organizations often invest substantial money in developing ETL code tailored to legacy environments, which subsequently binds them to specific vendors and ecosystems -- creating a “trap” that is difficult for companies to escape, especially as they continue to build critical pipelines supporting production workloads. 

As a result, they are at the mercy of a vendor’s product road map, pricing changes, and potential lack of innovation. The ability to have complete control and ownership of the underlying code introduces the flexibility needed to future-proof the data infrastructure supporting analytics and AI initiatives.

Fragmented architecture. Line-of-business (LOB) and platform teams often find themselves unable to collaborate effectively due to the disparate tools they utilize, leading to inconsistent standards, incompatible data formats, and disconnected governance. Plus, legacy ETL tools tend not to be cloud-native, hindering an organization’s ability to leverage the scalability, agility, and cost-efficiency offered by the cloud.

Relying on multiple data platforms and tools creates a tangled web of data, governance, and security silos. Each platform operates within its own ecosystem, with its unique set of standards, protocols, and security measures, making it challenging to integrate and orchestrate data workflows seamlessly. Consequently, organizations grapple with issues of data inconsistency, governance lapses, and heightened security risks.

Lower productivity. Although some legacy ETL tools offer a degree of self-service through visual interfaces, they still demand specialized engineering resources for effective utilization. After dedicating time and resources to learning its language and workflows, users can achieve productivity for one proprietary tool. However, all that effort doesn’t always translate to other tools because they are confined to a black-box solution that makes debugging difficult. There is also a limited pool of new candidates familiar with these proprietary tools because students are not taught these technologies in traditional colleges and universities.

Finally, these tools don’t scale with data, so workarounds are often applied to process large amounts of data, creating additional management complexity and costs. 

Four Tips for Navigating the Path Toward Modernizing ETL

Done well, ETL modernization overcomes these challenges and lays the groundwork for future data-centric advances. Modern ETL processes enable faster data delivery and actionable insights across the broadest possible spectrum of users. To ensure success, organizations should consider the following four tips.

Tip #1: Build with standards to scale

For Further Reading:

Why Modernizing ETL Is Imperative for Massive Scale, Real-Time Data Processing

Calculating Your ETL ROI

Data Wrangling Versus ETL: What’s the Difference?

By defining clear standards from the outset, organizations can ensure that pipelines are built correctly the first time, mitigating the need for costly rewrites or revisions while ensuring higher-quality data products. These standards should not be confined to engineering teams alone; they should be made readily accessible to all stakeholders involved in pipeline development, including business subject matter experts. 

A well-structured and extensible ETL environment offers a repository of standardized components and frameworks, empowering developers to leverage pre-built solutions and streamline the development process. By minimizing redundant work and promoting consistency, extensibility fosters a more efficient and focused approach to data pipeline development.

Tip #2: Avoid vendor lock-in

Legacy ETL tools are proprietary, forcing customers to learn their platform and limit access by not exposing the underlying code. They typically exhibit lower performance levels, requiring higher maintenance costs as organizations scale their data loads and types. In contrast, modern ETL solutions adopt an open source approach, eliminating barriers to accessing and porting underlying code. 

By selecting a platform that prioritizes both open source formats and high-quality code, engineering teams can ensure adherence to software engineering best practices while avoiding vendor lock-in. This openness fosters greater flexibility and control, enhances scalability, and reduces long-term costs associated with maintaining and scaling ETL processes.

Tip #3: Optimize your tool and processes to eliminate risk

Here the focus is on centralizing the tools and code-standards layer with an eye toward supporting all data, users, and types of pipelines while leveraging the most suitable engine for the job. Ideally, this tool will combine a modern visual replacement for legacy ETL systems with the customizable power and portability of open source code. This approach reduces "swivel seat syndrome," where employees engage in repetitive tasks across multiple tools, enhancing efficiency and reducing errors.

Begin with small-scale implementations rather than diving into fully-fledged production projects immediately. This incremental approach allows for testing and validation on a manageable scale, mitigating the risk of investing resources in projects that may not yield desired outcomes. Although ambitious projects hold the promise of significant business value, rushing into them without proper validation can lead to wasted time and resources. By starting small and iterating based on feedback and outcomes, organizations can minimize risks and ensure the success of their modernization efforts in a shorter timeframe.

Tip #4: Accelerate productivity through self-service

It's imperative that modern ETL solutions maintain existing levels of self-service and strive to enhance them. The crux lies in catering to both engineering and LOB personas. The true power of self-service extends beyond productivity gains; it lies in elevating the performance of all data users.

A common pitfall encountered by organizations is over-optimization toward a singular persona. For instance, some enterprises may invest heavily in legacy ETL tools, while others may lean entirely toward do-it-yourself solutions. However, organizations must ensure that anyone who wants to operationalize data can remain productive without having to rely on other teams to make it happen. By embracing a tool that works for both skilled coders and less-technical visual developers on business teams, organizations can democratize data access and empower all data users to contribute meaningfully to the data transformation process.

How Modernization Raises the Bar for Data-Driven Innovation

The modernization of ETL processes represents a pivotal step forward for enterprises seeking to harness the power of data-driven innovation. It involves accelerating productivity for LOB and engineering teams alike, embracing openness to avoid vendor lock-in, standardizing pipeline development, and starting small with implementation.

Looking ahead, the trajectory of data-driven innovations is intrinsically linked to the modernization of ETL processes. As organizations continue to embrace modernized ETL tools and practices, they pave the way for continuous innovation. More important, they can elevate their data-driven capabilities, empower all teams to make more informed decisions, and chart a course toward sustained success in an increasingly dynamic and competitive business environment.

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