Examining Data Transformation Tools: A New Perspective

Many current data transformation tools are overkill, requiring investments in time and money when alternatives now exist without the same commitments. Explore a day in the life of a product manager – and the data transformation solution that will get you from point A to point B simply and quickly. 

On a beautiful sunny day in Tel Aviv, I had the privilege of hanging my own shelves in my new apartment. I looked in front of me at this seemingly impossible task, as I own no tools, not even a drill, it dawned on me that I had 2 options: buy a drill or call a handyman.

As a product manager by nature and by job title, I did some research and determined the cost of a handyman was approximately the cost of purchasing a drill. But what is a good drill? What should it cost? What features do I need? It was at that point I stepped back and started counting exactly how many holes I required because ultimately – that was what I was looking for. 

Will I use my new tool, my new drill frequently? Do I want to own a drill? Do I have a place to store it? Do I need it for other purposes? The answers to these questions will tell me what I need to know but again, all I really need right now is a hole in my wall. 

In my view, it’s the exact same with data.

Data Transformation Tools vs Data Warehousing Solution 

Life seems to have many of these conundrums. It starts with a drill and a hole and next you are considering whether you use this common data transformation tool, find some all-encompassing data warehousing solution or develop one on your own.   

Most companies today engrain ETL (Extract, Transform, Load) solutions and capabilities into their core. They have numerous disparate data sources, even more destinations, and processes that not even they can explain. No matter if we are trying to determine whether to buy a drill or pay a handyman, these problems are one and the same, in that the same logic can be applied.    

The drill or the many current data warehousing solutions today are overkill, requiring investments in time and money when the alternative will get you from point A to point B, without the commitment. At the end of the day, we just want a handyman to put a hole in our wall, or a data transformation tool that answers our problem, and that’s where Datorios comes into light.

Focusing on the main goal at hand, Datorios’ customizable data pipeline solution is the handyman needed to do exactly what’s needed – help put up your shelves.  Offering advanced transformation capabilities, endless connectivity options to any and all data sources, and complete observability of all processes for easier maintenance, it provides the tools you need to get the job done, without bells and whistles. 

Three Ways to Use Data Transformation Tools:

In the data transformation landscape, this means we just want something easy. Something that works with our existing infrastructure and doesn’t make us sacrifice our current stack, with complete observability, complete control, an easy interface, and a straightforward pricing model with no strings attached. 

How can modern Data transformation tools be utilized today:

1. They can be used to simplify source connectivity allowing for automation and real-time data ingestion for the most accurate insights

2. They can be used to apply advanced transformation capabilities offered in a user-friendly environment to reduce maintenance, speed-up debugging, and transform our data into the files and formats needed by end-users. 

3.  They can be used to offer complete observability allowing for complete clarity of continuous operations as well as adaptability meaning changes can be easily implemented to data flows suiting future needs.   

As a product manager myself, what do we want? We want templates with speedy onboarding to production time, advanced configurations that can be chosen or discarded as needed, and the ability to add the missing piece to our pipeline without needing to give up on processes and methods that are tried and true for our organization. And what I’m trying to say is, it is now possible. 

How Data Transformation Configurations Can Create a Simple Tool

So how does the right data transformation configuration play into this? 

Taking my putting-a-hole-in-the-wall analogy and applying it to the data world – we developed a simple solution to those all-too-common data transformation issues every company seems to deal with.

Common Steps and Simple Solutions in the Data Transformation Process

1. Consolidation, one place for everything:
   Working with and combining any data type from any source, we offer one clean, clear, and easy interface to merge everything together. 
2. Transformation, advanced processes offered with simplicity:
   By engraining easy data transformation configurations into the system itself, we’ve created the simple solution for advanced computations the industry was missing. 
3. Observation over data flows: Offering unparalleled data lineage for inputs, and outputs the Datorios Platform shows complete data schemas before and after implementation guaranteeing your needed results. 

It makes sense to me and you should try it for yourself. Test out our interactive demo and let me know what you think. 

Why One Solution Can Be All You Need  

My neighbor is a DIY fanatic so he actually ended up helping me with the shelves and the same can be said for your data flows. One place, one platform with all needed processes that can adapt to your ever-changing data needs while working with existing set-ups – does exist. Rather than looking for a drill here, nails there, and a handyman somewhere else, one solution that gets the job done and puts your shelves on the wall, delivering your needed data in the format and file type you need in the timely method you require is all we were looking for. So we developed it.  

Lessons learned, maybe an easy-to-use, all-encompassing platform, is all you really need, and sometimes it’s smart to know your neighbors. 

About the Author:
Amir Goldberg

A senior product manager at Datorios, Amir has met with many leaders in the data realm, has had interviews, and has taken the time to dig deep into data flow issues companies have.   

He realized many companies have developed temporary solutions to fix broken processes but simply put; current solutions were not up to par. Amir worked with Datorios’ engineers to develop the solution to these common issues and as a result, developed the Datorios Platform as the answer.