Data tools are constantly evolving, making it harder to choose the right solutions for your organization. This is especially true with machine learning (ML) and AI use cases, for which a solid data foundation to feed training models or algorithms is more important than ever. This foundation starts with data transformation and data modeling.
Choosing the right data transformation solution can impact every stage of your organization’s data operations and the success of your data projects. Whether it’s a pristine UI, lower costs, enhanced development workflows, or a low barrier to entry, different solutions promise that their features are superior to the competition and just what the data team needs. But what do you really need? In this article, we will outline important considerations for selecting a data transformation solution to position your team to manage and scale your data foundation.
Business considerations
Understand your problems
The key to selecting the right data transformation solution for your organization is to first evaluate the problems you are aiming to solve. Without understanding those problems in detail, you won’t really know what functionality or features your team needs. Are you trying to manage a rapidly growing backlog of data analytics requests which your current data management processes can’t support? Is your team facing problems due to a lack of capacity or features? Is your organization’s reporting suffering from data quality issues? Are you trying to save development time or money? Are you migrating to a cloud data platform and want to ensure you’re getting the most out of it? These are all questions you and your team need to ask yourselves before procuring new data transformation tooling.
Next, you must determine if any specific data transformation tools will enable you to solve those problems. Without determining the answer to this fundamental question, you’ll end up with a mixture of modern data stack tools that are loosely pieced together without capitalizing on any of the investment. Or, you’ll choose to do nothing, leaving your data team to manage homegrown processes or legacy systems, and deal with the same problems you aren’t currently identifying.
Evaluate your data team
A particularly important consideration when selecting a new data transformation tool is how it will fit into your data team. There are a multitude of considerations here, including:
- Will the tool require my team to learn a new skill set, or does it compliment my current team’s abilities?
- Will existing team members feel obsolete?
- Can the size of our current team handle the tool?
- Will team members use the tool or still revert to old processes?
- How will it fit within the team’s existing workflows?
This list can go on for a while, but as you can see, there are several important factors that include the most important asset on any data team: the people. It’s important to note two specific items when selecting a solution for your team: size and experience.
Team size
Selecting a solution that is technically complex and requires lots of manual development may not be the right option for a smaller team, as they’ll need more personnel to effectively manage their data. On the other hand, a larger team would likely frown upon a tool that reduces manual engineering efforts as they may feel at risk of being replaced.
Understanding how your data transformation solution of choice matches with your team’s strengths and business needs can help you make future decisions as well. If the business doesn’t have the budget to hire additional data engineers in the future, you may be able to justify the purchase of a tool that enables your team to do more with less. If your organization has ambitious hiring plans, a tool that new team members will learn quickly may be best for your needs.
Experience
If you have a smaller, less technically experienced team, choosing a solution that capitalizes on and amplifies their current skill set can have a huge impact on ROI. If you have a team of experienced engineers, selecting a tool that they can fully manage, use to scale data transformation processes, and that saves money may be a better option.
It’s also important to note that tools that are free and often fully managed (open source) by a data team are not better or worse than a SaaS solution you pay for and that is managed for you (off-the-shelf). Picking one solution over the other is also not indicative of the maturity of a data team—it boils down to the problems you are trying to solve with the team you currently have.
Technical considerations
With a clear vision of the problems you are trying to solve and an assessment of your team, it’s time to explore the technical considerations of picking a data transformation tool.
Open source or commercial offering?
When it comes to technical considerations, arguably the most important one is whether to select an open source solution or a SaaS solution. While there are variations of open source and fully managed software, for the sake of this article, we will be focusing on these as the primary options.
Budget constraints
Business decisions eventually boil down to money. Choosing a data transformation tool is no different. Some organizations may be in a position where there is no budget to purchase a SaaS solution for your data stack. In this scenario, open source software is often the only road forward. Keep in mind that open source software often requires more management than a SaaS solution, so if your team is not equipped to handle that, you may need to reevaluate your decision.
Another important element of budget considerations is total cost of ownership (TCO). If you decide to go with an open source solution, is the cost to build and maintain that solution going to outweigh the fact that it’s free or low cost? Alternatively, if you have a small data team being asked to do the work of a much larger team, but don’t have the budget for both headcount and software, can a transformation solution help amplify the current team so a new headcount isn’t needed? The TCO for a solution goes beyond just the current month or year—factor in how costs in maintenance or additional hires may impact your selection.
Management and business value
Another helpful consideration when evaluating open source versus SaaS data transformation solutions is where your time will be spent. Often, with an open source solution there’s going to be significant overhead, some level of management and maintenance necessary to keep the software up-to-date and running. This may also mean having to leverage other open source software to complement the solution you’re currently using, which ultimately draws attention away from your data projects.
On the other hand, a SaaS solution should allow your team to focus on the data and maximize the business value you’re able to provide. Often, this is done through the automation of code and a user interface, which allows data teams to maximize their productivity when compared to maintaining open source software. If you’re paying for a data transformation tool that still requires lots of manual work and maintenance, you may want to reconsider it.
It’s important to understand the trade-off of managing a free solution that will take some time away from strategic work versus paying for a solution that should allow you to focus on generating business value.
Scalability
Scalability is important to understand as the lack of it can be incredibly costly. We’ll highlight three problems that stem from choosing a solution that does not scale with your data.
1. Lock-in and migration friction: Regardless of whether you choose an open source or SaaS solution, a tool that does not scale with your data will eventually lock you in with whatever constraints it enforces, Solutions that have no automation, poor standardization, and a lack of portability make this concern very real. This can make migrating to a new solution incredibly painful and time consuming as you would have to incorporate all of your work to meet the requirements of a new system, whether by copy and pasting code and customizing it, or having to completely rebuild it from scratch.
2. Maintain instead of build: With a solution that can’t scale with the needs of the business, you’ll eventually hit a point where simply maintaining your current data projects and infrastructure will consume most of your time, leaving little to no bandwidth for your team to build data products that deliver value to the business. This looks like a situation where you are fighting fires, updating SQL, or arguing about metric definitions instead of using a platform that enables you to develop faster, with full transparency to stakeholders. You don’t want to get stuck with a product that ultimately requires you to maintain instead of build.
3. Extra costs: Committing to a data transformation solution and then finding out it doesn’t scale to your needs can cause additional costs for your organization. This may look like needing to buy additional features you didn’t know were not included with your current subscription, hiring for specialty roles in order to use certain functionality, or needing an army of engineers to manage an open source solution. In either scenario, considering the potential costs you could incur if the solution is not what you think it is can help eliminate frustration in the future.
There are also costs incurred from inefficient workflows resulting from your data transformation tool. Data teams working in open source solutions may be writing varying efficient levels of SQL based on experience. Additionally, they may not have a clear picture of data models they could reuse, and as a result, create duplicate objects. Teams using SaaS solutions may find they need to create vast amounts of objects in order to accomplish simple tasks—resulting in inefficient workflows. Either way, inefficient data workflows can add an unforeseen cost to the mix, regardless of the solution you choose.
Control
Open source solutions provide complete control of the development of your data. While this may sound appealing, it comes with the responsibility for managing each component of the system yourself. On the other hand, black box solutions don’t give any flexibility for customizing the tool to your needs. Sometimes a solution in the middle of the spectrum of control provides an ideal balance of customizable control without the worry of constant maintenance.
Security and compliance
A final consideration when it comes to choosing an open source or SaaS data transformation solution is security and compliance. Open source solutions may require more effort and commitment to enforce the security—and especially compliance—necessary for your organization. If you choose a SaaS solution, pay special attention to the security and compliance frameworks that each solution has in place. In this case, you are paying for peace of mind that you don’t need to manage. There is a trade-off between managing an open source solution versus paying for a fully managed solution that just works.
Features
If you are still on the fence about an open source versus SaaS data transformation solution, you should evaluate the functionality and features that each tool provides, and understand how they help solve your problems and amplify your team. For example, if you have a team of engineers who primarily write SQL but are not particularly comfortable with Python, choose a solution that is SQL-based and allows them to perform complex transformations without writing code.
If your team wants to have complete control over the entire solution and customize every single element, effectively allowing them to build the features they want themselves, open source may be a better fit. Still, keep in mind that data transformation solutions should first and foremost enable you to manage and scale your data. So while building custom features may sound exciting, choosing a solution that has robust functionality out of the box can often deliver the highest business value.
This business value may be tied to choosing a solution that has a GUI versus going with a code-first tool. While the code-first solution may provide full customizability for data workflows, the data team is responsible for managing both the code and any customizations that are created. If choosing a GUI-based solution, data teams should look for solutions that meet the flexibility they need in development without constraining them to just a user interface.
Not all features need to focus on developer experience; some will prioritize simplifying data transformation capabilities. For instance, instead of manually writing each column reference in a SQL transformation, you may look for a tool that does bulk editing. Or if your team works with frequently changing data from multiple sources, tools with impact analysis features such as column-level lineage can save significant time and headache. Because this solution will be processing data, built-in data quality features can be invaluable in catching issues before they happen.
Each data transformation solution will have a myriad of features that come with it, so when it comes to feature selection, spend time to identify features that solve pain points and enhance your data team’s effectiveness.
Integrations
Sometimes the biggest roadblock in selecting a data transformation solution is its extensibility. It’s already a time commitment to learn and become productive with a new data transformation solution, but not being able to integrate into existing systems or use pre-existing work from other software can be incredibly frustrating and limiting. It’s important to ensure that the data transformation tool you choose has integrations with the solutions you are already using in your data stack—that allows you to get a higher ROI from your current architecture.
If you use a tool like Fivetran for your ETL processes, having an integration that allows you to run your data flows when a Fivetran job is finished can establish real-time data analytics. To integrate data processes across the entire data stack, a solution that provides a view into each process can promote full transparency for both data and business teams.
Find the right fit
While picking a data transformation solution may include several other variables, we hope these key considerations can help you navigate the tricky task of determining the best solution for your needs. And we might be a teensy bit biased, but if you’re looking for a solution that combines the flexibility of code with the ease of use and speed of development from a graphical user-friendly interface, you should check out Coalesce.
