The Legacy ETL Dilemma – Part 1: Why Modernize Your ETL in the Cloud

Introduction

Data is like the fuel that keeps modern businesses running. It’s important for making smart decisions and staying ahead of the competition. Traditionally, ETL (Extract, Transform, Load) processes have been the go-to for data integration. However, legacy ETL systems are increasingly creating new challenges for organizations.

This blog, the first in a two-part series, will explore the challenges faced by legacy ETL systems in today’s data-driven world. We’ll discuss how these systems are struggling to keep up with the increasing volume, variety, and velocity of data. Additionally, you can learn more about the benefits of modernizing ETL processes using cloud-based solutions and AI/ML technologies. By the end, you’ll understand why ETL modernization is essential for businesses to remain competitive and drive innovation.

The Legacy ETL Landscape

Legacy ETL systems have been around for decades, serving as the backbone for data integration and processing. These systems were designed for structured data from relational databases and have limited capabilities to handle the diverse and voluminous data we encounter today. Some common challenges with legacy ETL systems include:

• Distribution of data at different locations:Traditionally, separate data silos were established at various locations due to the limited scalability of existing data centers. For example, in the retail industry, different pricing systems may be created for distinct customer segments, such as loyal or regular customers. These systems would be housed in different locations, leading to multiple issues such as high maintenance costs and increased latency.
• Scalability issues: In traditional systems, scalability issues arose when data volumes increased significantly. For instance, in the retail industry, product sales surge during the festive season, causing invoice data to quadruple compared to regular periods. Because traditional systems lacked scalability, businesses had to maintain infrastructure capable of handling this 4X data volume throughout the entire season, resulting in high maintenance costs.
• High maintenance costs: In addition to the scalability issues leading to high maintenance costs, other factors include maintaining the physical security of data servers, creating backup systems for disaster recovery, retaining resources with specialized skill sets to manage cybersecurity and a lot more.
• Limited flexibility:Traditional systems were designed for structured data, such as flat files and RDBMS. However, nowadays, various semi-structured and unstructured data sources are available, making it extremely difficult for traditional systems to manage.

Why Modernize ETL?

The digital transformation wave necessitates a shift from legacy ETL systems to more robust, scalable, and flexible cloud ETL solutions. It not only overcomes the challenges mentioned with legacy ETL process but also helps you with modern requirements such as the following:

• Increase in real-time data processing use cases: Although legacy ETL tools can handle real-time data processing, they often encounter issues such as performance bottlenecks, latency problems, resource intensity, and integration challenges. These issues can be more effectively managed with modern cloud-based platforms while migrating ETL workloads to the cloud.
• AI and machine learning integration: Integrating AI and machine learning with cloud platforms is simpler than with on-premises setups as they offer easy access to tools, frameworks, and collaborative features, making it more flexible and resource-efficient for developing and deploying AI models.

To illustrate, Bitwise recently worked with a transportation ministry in Canada that faced limitations with its legacy data integration platform and set a strategy to migrate Informatica ETL to Azure Data Factory (ADF) to leverage the advanced capabilities of the Azure Data & AI ecosystem.

The Need for ETL Modernization

The limitations of legacy ETL systems are hindering businesses. Cloud-based ETL solutions offer a more scalable, flexible, and cost-effective approach. By modernizing with a cloud-based ETL system, you can:

• Improve data processing speed and efficiency
• Enable real-time data analytics
• Integrate AI and machine learning capabilities
• Reduce operational costs
• Enhance data security and compliance

A great example comes from a multi-national retail chain that had long-running ETL jobs in its legacy system in DataStage. With automated ETL migration of DataStage to Azure Data Factory, Bitwise helped the retailer optimize long-running jobs to enhance the efficiency of the data integration system.

Conclusion

Embracing modernization is not just an option but a necessity for businesses seeking to thrive in the digital age. In our blog post, 3 Real-World Customer Case Studies on Migrating ETL to Cloud, we explore successful ETL migrations covering different legacy systems and cloud platforms to highlight the shift in technologies driving today’s data integration needs.

Coming up next in Part 2 of this two-part series, we will delve into the specific steps involved in migrating legacy ETL systems to the cloud. We’ll cover topics such as choosing the right cloud platform, designing a migration strategy, and leveraging automation tools to streamline the process.

By making this strategic shift, organizations can improve operational efficiency, gain valuable insights, and ultimately achieve a competitive advantage. It’s time to break free from the legacy ETL constraints and embark on a journey towards a data-driven future.

Share

Share on: