As one of the leading SaaS data preparation tools, Paxata is geared toward helping businesses that are struggling with the data deluge of spreadsheets and data marts, but lack the technical expertise to clean up their data with code or other less-intuitive software. Functionally like a big brother to the Power Query Editor in Microsoft Power BI, you can load in your data and slice/dice, append, join, update, and pivot to your heart’s desire. However, it is focused solely on data preparation; there are no report-building or visualization functions, and it shouldn’t be used as a replacement for an entire database. So, should you spend money on this tool? Here’s a quick breakdown of the main pros and cons of using Paxata and why you should or shouldn’t spring for it.
Pros to using Paxata
Paxata is quite easy to learn. That is easily the biggest draw to using the tool – with a few clicks and zero lines of SQL, Python, or R, you can transform your data from a mess into something functional for building reports/visualizations in a external BI tool. Users still need an understanding of data structuring, but you won’t need to have coding experts on your team to get results. This often means less money spent training or hiring new personnel to get your data preparation done.
Paxata’s built-in versioning system makes sure that every single preparation step creates a new version of the project, while saving the prior ones. This means if you go down the wrong path during your work and need to backtrack, you can easily go back 1, 2, or 30 steps and start again. It also means that someone else can easily pick up where you left off, seeing every step you took to get to the current state.
Paxata offers a variety of deployment options: Paxata-hosted cloud, on-premise, private cloud, or a hybrid of those options. Paxata’s cloud sits on top of the Hadoop Distributed File System (DFS), and allows users to work without worrying about the underlying file management/storage systems. You can also keep multiple tabs open at once to view multiple projects/datasets side-by-side while you work.
Preparation projects can easily be automated by a built-in Paxata function so repeated projects can be executed without having to redo the same steps.
Cons to using Paxata:
Paxata is not a relational database and should not be used for complex ETL
This isn’t necessarily a “con” as much as an easy misunderstanding of Paxata’s functionality: Paxata is built solely for data preparation. It works great when focused on the intermediary cleaning steps between the database and BI tools, but trying to make it any more than that is a recipe for failure. It’s not structured like a relational database, and you will run into roadblocks if trying to use it for complex ETL processes.
Paxata handles small amounts of data with ease, but you can run into performance issues once you get up to millions of records. Additionally, it lacks some functionality that would make development smoother, such as a search function for your prep steps, a quick and easy way to query the data as you work, or the ability to automatically push changes through multiple related prep projects.
So, should you use Paxata?
If your data infrastructure isn’t massively complex or you lack the technical skills to clean data with code, and you want a quick and easy solution: Paxata is the perfect choice. If you’re looking for a clean solution to complex data problems, code should be your answer.
If you have any other questions data preparation or data management solutions, the Aptitive team is happy to help. Contact us to learn more.