Over the last decade the explosion of SaaS and Mobile applications, IoT, and web API’s has brought with it a massive influx of data in increasingly complex forms. Collecting all that data is one thing — analyzing it is another job entirely. That’s where we come in.
Building a custom parser for JSON data in Python might take days or weeks —and then all the updates. SlamData | REFORM takes minutes, and updates automatically.
There are plenty of existing ETL tools for transforming data, but none of them handle complex JSON data using multi-dimensional relational algebra (MRA). We do.
No matter the industry, department, or source of the data, we can turn it into usable, analytics-ready tables. We’ll even put it in your data warehouse for you.
Modern data is only getting more complex. With the rise of IoT devices, SaaS companies, mobile apps, gaming, the digitalization of medical data, and a thousand other factors, the amount of data that companies have to work with is exploding. It’s also getting more complicated — data models like JSON make data easier to capture due to their tremendous flexibility, so creating and capturing complex data is cheap and easy. But that flexibility comes at a price — lack of consistent structure makes analytics a nightmare.
Contemporary applications generate enormous quantities of data, from user event data to IoT sensor data to transaction data. The sheer volume is daunting. In addition, these applications capture and store data in complex ways, making analytics a serious challenge. Many companies resort to hiring engineers who manually write custom code in order to deal with the complicated and changing nature of the data, but the process is expensive, tedious, and slow. It’s also completely inaccessible to non-engineers who don’t know how to write or use the code themselves.
When discussing this challenge with hundreds of users across many companies, two common themes emerged. First, existing tools did not work as data complexity increased. Some tools could handle very simple JSON data, but once the data became more complex, they failed, and ultimately users had to revert to the data engineer and coding solution. Second, existing tools were far too complex to use and required users to have a deep understanding of the data and structure ahead of time, which defeats the purpose for most end users.
Users wanted a tool that was easy to use and could handle any data regardless of complexity. So we built one. The result is a powerful new tool: SlamData | REFORM. It natively understands the most complex JSON you can throw at it and provides a user experience that is as easy to use as a file browser. SlamData is taking a new approach to complex data. We are able to natively understand the complex and changing nature of the data and provide any user the means to browse and build analytics-ready tables. Now any user can access complex JSON stored in S3, Azure, Google Cloud, MongoDB, or just about anywhere and easily turn it into analytics-ready tables. And they can edit and iterate on these tables quickly and easily as they discover what they need.