As various reports from various organizations like Gartner suggest, the majority of the businesses across the globe are getting ready for cloud migration. But, while working on a cloud migration strategy, ETL (Extract, Transform, Load) tools are often less thought about as part of the process. This is where you start limiting your cloud capabilities, by hindering performance and increasing cost. Before we dive into the details of solving this challenge, it is first important to understand what the problem is all about.
ETLs in Your Legacy Data Warehouse
As all of us know, ETL pipelines are basically coding instructions on how to move data from a source to a target. ETLs extract data from various data sources, transform it, and load it into the data warehouses. Further into the data warehouses, there are more ETLs that carry out the same function internally. Because data sources, ETL, and data warehouses are in the same on-premises environment, their dependency on the network is significantly less, contributing to the better performance of the ETL tools, and they don’t become problematic until the data warehouses expand in size. Additionally, increased usage of unstructured data, like images, audio, and video, as well as semi-structured data, including email and texting, slows down the ETL process. Latency in the ETL process is a result of the time and computing resources required to convert the data prior to actually loading.
Issues Caused by Legacy ETL Tools
We know that as data warehouses expand, various issues such as performance, scalability, cost, etc. start rising in the data warehouses as well as ETLs. And restricting the collection of data in order to maintain performance in today’s data-centric world is like missing insights about millions of business opportunities. That is the reason almost all businesses are moving their traditional data warehouses to the cloud. Cloud platforms enhance performance, solve scalability issues, and are cost-effective. But as most businesses migrate to cloud platforms, they start facing performance issues and latency in data loading. This happens due to the incompatibility of legacy ETLs with modern cloud platforms. Hypothetically, if we consider migrating existing ETL tools to the cloud, it would be of hardly any use. Because if you change all the codes and migrate them to the cloud, all the different data sources would still be on-premise. This will separate the environment of the ETL, increasing the dependency on network infrastructure, which will add to the existing performance issues caused by the incompatibility of legacy ETLs.
What is the Solution?
Basically, there are two primary methods through which ETLs are migrated to the cloud. These methods are a bit complex and have varied impacts on the migration process.
Rewrite is recognized as an effective way forward when it comes to ETL modernization or migration. Even though ‘rewrite’ addresses significant performance, cost, lookup, and data transfer issues, it is nearly impossible to rewrite ETL codes manually. Our automated ETL code transformation tool, ‘Raven’ eases the process of ETL code conversion and even allows you to retire legacy ETL tools altogether. Our automated migration suite not only helps you get rid of the headache of rewriting ETL codes but also makes your entire cloud migration process seamless and optimized.
The benefits of rewriting jobs outweigh the drawbacks, making it the best choice. To achieve this pattern, businesses should adopt automation. Due to time constraints and understanding repoint issues, enterprises can also plan to perform this migration in two phases. Following the cloud migration of the EDW with repointing, the second phase of rewrite can be implemented.
Read the detailed solutions on ETL migration and modernization now on Forbes
This blog provides detailed insights into the ETL migration solution patterns and their pros and cons.
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About Datametica
A Global Leader in Data Warehouse Modernization & Migration. We empower businesses by migrating their Data/Workload/ETL/Analytics to the Cloud by leveraging Automation.