Is an older ETL platform slowing your business down? Before you start the migration process, ask yourself when and why you need a new ETL solution.

ETL (Extract-Transform-Load) processes have become critical to business’ success; how else can they harness the power of the modern data deluge? However, traditional on-prem ETL solutions are struggling, thanks to their high costs, static storage capacity, and limited infrastructure. Thus, we’re seeing an increasing number of businesses contemplate the move to a new ETL.

Admittedly, new automated ETL migration tools have made this process easier and faster. But that’s not the real reason behind the move. In this article, we’ll look at the why, when, and what of ETL migration:

  • Why do businesses decide to change ETLs?
  • When should you think about migrating to a new ETL?
  • What features should you look for in a new ETL?

Let’s answer each of these questions, starting with the first one.

Why Do Businesses Decide to Change ETLs?

There are several reasons why companies may decide to move to a new ETL solution:

  • They want to move to a Cloud-based solution and their current setup doesn’t support that
  • Moving to a Cloud ETL offers more flexibility and scalability than traditional ETL tools
  • They want to increase their agility, responsiveness, and adaptiveness beyond what their current setup offers.
  • New ETL tools provide cost-effective data storage and other budget-friendly features.
  • They need something that handles bigger data loads and/or offers more security.
  • Running several ETLs in one organization is costly and ineffective for their business needs; they want to use one solution for the whole company.
  • They’re using an obsolete or near-obsolete solution and it’s time to upgrade.

As you can see, there are plenty of reasons to make the switch to another (possibly newer and better) ETL solution. But how do you know if you’re ready to pull the trigger on a migration?

When Should You Think About Migrating to a New ETL?

New advances have made ETL tools more flexible, more powerful, and more user-friendly. Even with automation, though, the ETL migration process is still quite challenging. How do you know if it’s time to upgrade to a new solution?

You’re dealing with changes in data variety, volume, and velocity: Data extraction has evolved over the last decade. Earlier, most data came from databases, spreadsheets, and text files; it was structured and formatted in a few familiar systems. Much of the data for today’s analyses is unstructured, coming from places like web scraping and streaming data. Traditional ETLs struggle with this.

Another thing to think about is the volume of data you must process. While a terabyte of data seemed like a huge amount a decade ago, today it’s seen as quite easy to handle – for a modern ETL solution, anyway. Older ones simply aren’t set up to handle large amounts of data. You will wind up paying the price in terms of performance and maintenance.

Also, we know now that companies have to do more than just collect data to benefit from it – they have to analyze it. Newer ETL systems can not only handle a variety and vast amount of data, they can also facilitate creating analytical lakes and warehouses as well as traditional data warehouses.

You want to use Cloud-based platforms: Traditionally, BI systems have been installed (and optimized for use) on the premises. This, of course, meant that scaling them or adapting to new data sources was difficult and expensive. Given Cloud ETL tools’ flexibility and scalability, it’s easy to see why many organizations are switching to them. In turn, this means your ETL has to efficiently interact with Cloud platforms and open-source solutions. Today’s ETLs can do this while processing data with minimal latency – thus solving issues related to maintenance costs and data accessibility.

You’re adopting or scaling up AI and machine learning initiatives: BI platforms have evolved from showing you the “what” (hard facts and figures) to the “why” (the insights underlying the changes represented in those facts and figures). This calls for machine learning and AI, which make more complex analyses possible. And this, in turn, makes ever-more-complex reporting essential to businesses who want to maintain their competitive edge.

Once again, we’re getting into territory that old-school ETLs can’t efficiently handle. Open-source technologies, R, Python, machine learning algorithms, new APIs, automation – an ETL has to be able to work with all of these and more to do its job.

Additionally, modern ETL platforms are putting some emphasis on becoming more user-friendly. They’re designed to handle complex queries, support drag-and-drop data profiling features, and allow machine learning code.

Maybe you realize that you should start looking for a new ETL, even if there’s still some time before the actual migration process. Excellent choice – you should research your needs and learn what’s available. But which features should rank highest on your list?

What Features Should You Look for in a New ETL?

As with any new technological investment, it’s important to shop for an ETL solution that will meet your future needs as well as your current ones. With that in mind, we recommend finding a tool with the following features:

  • Efficient and stable connections with data sources and tools.
  • Flexibility to accommodate different data sources, including traditional sources (databases, spreadsheets, text files) and unstructured data from videos, audio files, social media, etc.
  • Data profiling, cleaning, and management. The ability to extract insights from unstructured data depends on being able to transform it into usable, analysis-ready formats. This means there should be transformation functions capable of making sense of unstructured input, getting rid of the noise, and profiling the cleaned data. This will help ensure the AI and machine learning readiness necessary for future use. And, incidentally, it’s something that old ETLs don’t usually do.
  • Integration with Big Data tools like Hadoop.
  • Performance is a critical part of ETL efficiency; too bad it’s often overlooked in favor of fancy additional features. Look for a solution that can process large data sets with a focus on quick execution – it will make your machine learning and Big Data tools more efficient as well. And don’t forget about legacy code; your new ETL solution should be able to work with code written for older ETL platforms. This will save your developers from having to reinvent the proverbial wheel during your migration.
  • User-friendly interfaces make automating and streamlining data processes easier, even for tech-savvy analysts and developers. Plus, drag-and-drop capabilities allow technical/functional employees to work and interact directly with the ETL, which streamlines their workflow. This also makes insights much more readily available.

Choosing a new ETL is a major decision and should not be undertaken lightly. The wrong selection will result in greater expense and may not even provide the reporting and analysis capabilities you’re looking for. How sad if, after all the work involved in an ETL migration, you wind up with something that generates basic, low-level reports that you could just as easily get from an old-school ETL solution!

Getting Started

The moral of the story is simple: Do your research and choose wisely. ETL migration – even with the help of automation – is complicated and risky. It’s labor-intensive trying to get a new system to operate like your old solution.

But on the other hand, upgrading to a modern ETL can have many benefits. It can boost the efficiency, scalability and flexibility of your data analysis endeavors. And it can put your organization in a better position to achieve its business goals.

Understanding how different platforms and strategies can help you achieve these goals is foundational to an ETL migration. Stay tuned to this blog; in an upcoming post, we’ll show you how to define your migration strategy, assess and prepare, and finally develop an implementation roadmap. See you soon!


Authored by: LK Sharma, Director of Technology Services at Absolutdata and Ashish Mamgain, Technical Lead at Absolutdata



Related Absolutdata products and services: AI & Data Sciences, NAVIK AI PlatformData Integration, BI + Dashboards