Big Data and cloud computing are closely related. Treasure Data, the first end-to-end managed service in the cloud for data acquisition, storage and analysis, is hoping to integrate them seamlessly with its data analytics platform, which runs in the cloud.

Taking the big data analytics platform to next level, the company today released its first self-service custom solution for the digital gaming industry for product managers, developers, designers to significantly accelerate game interaction.

The Digital Gaming Analytics Solution is an end-to-end adaptable, scalable, and simple self-service analytics solution that uses Treasure Data’s managed service to acquire, store and analyze data from any game interaction. The solution includes innovative real time data collection tools stored securely in the company’s own columnar cloud database, Plazma, which help gaming companies to set up services in production in less than two weeks. The big data solution also comes with customizable dashboards, prebuilt for common gaming metrics; a SQL interface for ad hoc queries; standard BI/analytics tool connectors; and easy, free data export anytime.

Acquire data quickly and early so that developers can do the “fun stuff”

During BigDataSV, John Furrier and Jeff Kelley caught up with Director of Treasure Data Hanna Smalltree in theCUBE to speak directly about the data acquisition model and why getting data sooner and providing data infrastructure is so important for data management.

“We offer essentially Software-as-a-Service for Big Data,” explained Smalltree. “So we have technology to help people acquire data and to store it, and then we give different interfaces for a analysis. But a lot of our secret sauce is in our acquisition technology so we focus on data that’s created very, very rapidly and we get it into our cloud environment. There was a lot of pent-up demand about that.”

Because Treasure Data provides an easily attached agent, developers need only plug in the Treasure Data Agent and let it do all the heavy lifting by grabbing the metrics from the platform and putting it into the cloud. From there, Treasure Data provides a SaaS solution that lets development and operations deal with the data in the cloud to do analytics; Treasure Data provides a huge set of tools to development so that they can extract the value from that stored data without having to manage it themselves.

Smalltree feels this is the killer app of Treasure Data’s cloud-storage and cloud-analysis option for Big Data. While the company provides a very specific service, it’s used by customers to provide for many different solutions for an ever-evolving marketplace of needs.

“We tend to talk about [our service] in three phases: data acquisition, data storage, and data analysis,” Smalltree said. “Some people are using that all in line, so they’re streaming their data into our cloud environment and then they’re doing their analysis in the cloud, they’re keeping it all in the cloud environment. Other people are putting us as part of a larger data ecosystem. So perhaps using us to ingest all that data that’s coming in very rapidly to store it at scale—billions and billions of rows, sometimes ten billion rows a day… And they might then do some aggregations to bring down the size of the data and then export the data to another system entirely.”

Best traction in the online gaming for developers

The digital gaming analytics solution is much more efficient in terms of query processing and allows game developers to optimizing query processing and manageability and change their KPIs in a matter of minutes. Making the system more modular can help developers to gain insightful, actionable analysis in near real-time. Treasure Data says the flexibility enables gaming companies to test, develop and explore the insights from the data.

The differentiator in Treasure Data’s offering is far more proprietary and helpful for swifter querying. As a result the solution significantly improves game performance–and makes it much easier to look-under-the-hood without impacting user experience.

Game companies are free to access their Treasure data in any way they want including a SQL interface to run queries and a dashboard tool with predefined metrics. Treasure Data also developed its own intelligent agent technology, called Treasure Agents, which pre-process and transform data before it’s loaded into the database for analysis. The Treasure Agent is designed for high-performance parallel batch loads to multiple concurrent targets. It maintains a continuous feed of new data to reduce subsequent load times, enabling near-real-time and event-based analysis. The solution also includes client-side analytics SDKs and bulk file import capabilities.

Developers can continuously feeds Treasure Data environment with information about its players, including what customers are playing games, how long they’ve been playing, and at what stage of the game they’re in. In addition, Treasure Data gaming customers are able to track new game events and publish new KPIs in their dashboards.

“The number of people playing digital games has exploded in the past several years, which can be intimidating to address from an analytics infrastructure point of view. Acquiring, storing and analyzing the most important asset, data, can be a challenge. With Treasure Data, we remove the operational and technical friction associated with chasing down your data sources, collecting all the relevant data, and getting them all into a database, and preparing the data for analysis. Leveraging Treasure Data’s service allows gaming development teams to focus on the most important thing, players, and leave the rest of the data pipeline to us,” said Hiro Yoshikawa, CEO of Treasure Data.

Treasure Data is trying to achieve a leadership position in providing the first end-to-end public cloud-based big data analysis service. To further streamline analysis, Treasure Data has developed its own columnar file format system to replace HDFS. This allows analysis tools to load only data in those columns that are relevant to the question rather than an entire database, drastically cutting the time it takes to conduct an analysis in many cases.

Contributors: Saroj Kar and Kyt Dotson