Data transformation sits squarely in the middle of the complex data management ecosystem.
The data ingestion infrastructure you build is tailored to the types of transformations the system needs to run. And the storage and analytics layer depends on fast, reliable transformation.
So when you’re choosing a data transformation tool, it’s not as simple as selecting the “best” one from a list and plugging it in. Rather, a balance of the following five parameters should inform your ideal choice:
- Performance
- Cost
- Scalability
- Security
- Ease of Use
The question is, how do you balance these things?
The Cascading Dependencies of Data Transformation
Performance, cost, scalability, security, and ease of use are all relative to your unique circumstances.
Data volume, type, and structure; data sources and destinations; internal resources; and use cases all determine how important performance is relative to — for instance — ease of use and security.
So your first step in evaluating a data transformation tool should be an in-depth audit of the following:
- The nature of your data, including volume, type, structure, and expected use.
- The systems where your data is sourced, sent, stored, changed, and used.
- The people who manage and use your data.
In the sections below, we’ll briefly discuss what to look for in each aspect of this audit and how each is relevant to performance, cost, scalability, security, and/or ease of use.
The end goal will not be to tell you what tool to use but to give you a framework for evaluating data transformation tools.
Data: Auditing the Nature of Your Data
The type, volume, format, and expected use of your data impact everything about what makes a data transformation tool a good fit or not.
For instance, let’s say you’re using Azure and want to optimize transformation performance in Azure Data Factory and Azure Synapse Analytics pipelines. Depending on your volume of data, Azure’s documentation recommends different optimization techniques.
These optimization techniques may have downstream effects on latency, cost, and scalability. The same concept will hold for AWS and other cloud service providers.
Similarly, whether or not your system is transforming sensitive data will determine what security features you’ll require.
As our post on data migration best practices explained, “It’s only with a deep audit of your system’s existing data that you can determine how data must be transformed, consolidated, and otherwise processed before (or as) it is moved to the new system.”
While that post was about data migrations, the words ring equally true for evaluating a data transformation tool.
Systems: Data Sources, Destinations, and Transformations
Where is your data? Where does it need to go? And how and when does it need to be transformed?
Exploring the answers to these questions will help you evaluate data transformation tools in the proper context.
At the same time, it’s essential to recognize that it’s rare that a transformation affects only one source and destination.
Usually, various downstream systems depend on and connect to a single transformation. And ignoring or failing to understand these connections will lead to suboptimal technology choices.
People: Resources and Know-How
Powerful tools are useless if you don’t have the people to wield them. Similarly, equipping data engineers with overly simplistic tools doesn’t maximize their capabilities.
Fortunately, data transformation tools run the gamut regarding ease of use. And the best solutions combine ease of use with extensibility. For instance, StreamSets simultaneously enables non-technical users to quickly stand up data pipelines with a graphical user interface while allowing engineers to write scripts to optimize performance.
Data Transformation Features To Look For
Once you have a better understanding of your needs, you can narrow your focus to specific features and capabilities. The following are important data transformation features that you’ll want to prioritize based on your needs:
-
No-Code, Low-Code, and Pro-Code Data Transformation
No-code and low-code data transformation tools enable non-technical users to develop data transformation pipelines. But they can be limited when it comes to more advanced use cases or performance optimizations. Ideally, you’ll have a solution enabling non-technical and technical users.
-
Language Compatibility
Engineers might know several languages, but that doesn’t mean they don’t have a favorite. Plus, certain languages are better than others at certain jobs. So make sure you prioritize a tool that supports the languages that make the most sense for your business.
-
Automation
Things like mapping data models and scheduling jobs are repetitive and time-consuming. And there’s no reason to make people do them manually. So prioritize data transformation tools that enable automation where possible.
-
Data Transformation Templates
To get the most out of your data engineers, you need to be able to reuse, recycle, and repurpose their work. So a tool’s ability to build and share data transformation templates is paramount.
-
Integrations With Third-Party Platforms
At the risk of stating the obvious, you want tools that will work well together. This is especially true of data transformation solutions since they often sit between two separate third-party systems.
A Note on Related Data Management Features and Support
Two final areas to focus on are 1) functionality that is related to data transformation and 2) product support and community.
You’ll likely find data transformation tools with various features related to data lineage, data ingestion, metadata management, and other data management features. You may prefer other specialized tools for these functions, and that’s fine. But it’s worth noting that vendors selling data transformation tools likely also provide other native tools.
Finally, you can expect to find data transformation tools with varying degrees of support and community.
Assuming all else is equal, a tool with a robust community and strong support will ease implementation and maintenance. Note that open-source data transformation tools will provide no support (though they often have rich communities), while hosted solutions will offer support at a cost.
Transform Your Data Management Practice
StreamSets Transformer Engine is one example of a tool that addresses all of the considerations we’ve laid out above.
In addition to broad source and destination compatibility, StreamSets is fully supported with a strong user community. Its intuitive design canvas allows data engineers, SQL developers, and business users alike to bring powerful data transformations for any workload to their entire team.
To learn more about StreamSets Transformer, head to our product page to learn how it helps you configure and manage ETL pipelines on Spark or Snowflake.
The post How To Choose a Data Transformation Tool appeared first on StreamSets.
