Posted by Jon Bock
Jul 23, 2015

KIXEYE is a Snowflake customer who demonstrates how our customers are rethinking what technology they need to process and analyze non-traditional data. As mentioned in our recent press release, KIXEYE is an online gaming company that is using Snowflake to help them analyze game event data in support of ongoing experimentation with new features, functionalities and platforms.

Existing Systems Don’t Meet Current Needs

Gaming analytics is a great example of how data and analytics have changed in ways that don’t fit traditional data warehousing solutions, but that aren’t easy to solve with big data platforms like Hadoop either. The reasons that a traditional data warehouse wasn’t going to meet KIXEYE’s needs were similar to what we’re seeing at other gaming companies:

  • Their game event data (which is the largest share of their data) is created as JSON. Traditional relational data warehouses don’t handle JSON well if at all–either you transform the data before loading, which adds delays and makes your data pipeline fragile, or you load the data into an unoptimized data type and pay a performance penalty every time you access it.
  • They needed to allow access to data at multiple stages of refinement. Their data scientists want access to raw data as quickly as possible, while other analysts want more refined data that can be accessed with visualization and BI tools. Since a traditional data warehouse is a repository for only refined data, it can’t support the full range of these needs by itself.

KIXEYE wasn’t unique in initially using Hadoop to store, process, and analyze their semi-structured data. Although Hadoop is great for many things (e.g. machine learning algorithms, unstructured data storage), what KIXEYE and a lot of other companies have realized is that it wasn’t designed for fast SQL analytics. Trying to use it to support that adds a lot of latency and complexity, let alone the challenge in finding skilled people to keep the system up and running.

One System for Storing and Analyzing JSON Data

That’s why gaming analytics is an area where Snowflake is getting a lot of interest. To them, the fact that with Snowflake you can load JSON data as is without transformation or flattening and without needing to define a fixed schema, but still get great performance on queries of that data in a SQL engine is a huge win. Josh McDonald from KIXEYE said it best: “I can’t say enough about how fantastic the native JSON support is. I’ve never actually seen anything that worked until now. My analysts are really happy about this.” Not needing to have staff focused on implementing and maintaining the system makes Snowflake even more compelling.

I encourage you to take a look at our case study on KIXEYE to learn more about how KIXEYE is using Snowflake. Our recent webinar with DoubleDown Interactive is another great example of a gaming company taking advantage of Snowflake to help them get easier access to data and faster analysis of that data.

At the end of the day, gaming companies like KIXEYE want to focus on developing the best possible games while optimizing revenue. Implementing and maintaining complex data infrastructure shouldn’t get in the way of doing that.