Treasure Data Closes $15M In Series B Funding For Fully Managed, Cloud-Based Big Data Platform

This week, Treasure Data announced the finalization of $15M in Series B funding led by Scale Venture Partners. The funding will be used to accelerate the expansion of Treasure Data’s proprietary, cloud-based platform for acquiring, storing and analyzing massive amounts of data for use cases that span industries such as gaming, the internet of things and digital media. Treasure Data’s Big Data platform specializes in acquiring and processing streaming big data sets that are subsequently stored in its cloud-based infrastructure. Notable about the Treasure Data platform is that it offers customers a fully managed solution for storing streaming big data that can ingest billions of records per day, in a non-HDFS (Hadoop) format. Current customers include Equifax, Pebble, GREE, Wish.com and Pioneer, the last of which leverages the Treasure Data platform for automobile-related telematics use cases. In addition to Scale Venture Partners, all existing board members and their associated funds participated in the Series B capital raise, including Jerry Yang’s AME Venture Fund.

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Treasure Data Partners With Yahoo Japan To Promote Its Cloud-based Big Data Processing And Analytics Platform

Today, Treasure Data announces a partnership with Yahoo! JAPAN whereby Yahoo! JAPAN will resell the Treasure Data platform to customers interested in leveraging the platform’s Big Data capture, processing and analytics capabilities. Branded the Yahoo! JAPAN Big Data Insight, the collaboration between Treasure Data and Yahoo! JAPAN will allow organizations to store and run analytics on massive amounts of real-time data without managing the relevant hardware infrastructure or mastering the intricacies of MapReduce. The Treasure Data platform embodies the intersection between cloud computing and Big Data given that customers have the opportunity to take advantage of Treasure Data’s cloud for storing Big Data as illustrated below:

The graphic above illustrates the Treasure Data platform’s ability to collect, store and run real-time analytics on massive amounts of cloud-based data. Worth noting about the Treasure Data platform is that although the platform specializes in Big Data processing and analytics, data is not stored within the HDFS Hadoop data format. Instead of HDFS, the Treasure Data platform stores data as Plazma, its “own distributed columnar storage system” that boasts attributes such as scalability, efficiency, elasticity and a schema-less architecture. Plazma’s columnar storage structure means that queries can focus on swathes of data in contrast to the entire dataset, thereby enabling faster queries, more effective use of the platform’s schema-less data model and superior performance all around. Plazma is achieved by transforming row-based JSON data into a columnar format that optimizes storage and the processing of analytical queries. Treasure Data’s resulting analytical platform features use cases such as web-based data from software applications and mobile applications in addition to data from the internet of things such as appliances and wearable devices. Today’s announcement represents a huge coup for Treasure Data because of the co-branding of its technology alongside Yahoo, one of the industry’s experts in the storage, processing and analysis of Big Data. Moreover, the collaboration with Yahoo promises to strengthen Treasure Data’s market presence in Japan and potentially pave the way for greater market expansion into Asia and the Pacific Rim, more generally.

Treasure Data Reveals Big Data Analytics Solution For Digital Gaming Industry

Treasure Data, a cloud-based big data acquisition and analytics vendor, recently elaborated details of its first solution for the digital gaming industry. The solution takes advantage of Treasure Data’s managed solution for Big Data to provide game developers actionable business intelligence regarding the usage of their products. Customers can define their own rules for data collection and the kinds of user interactions of interest without being constrained to generic, templates that govern data acquisition. Moreover, customers can nimbly update the rules for data collection as their analytic interest in user behavior evolves over time. Because setup can typically be completed in less than two weeks, Treasure Data gaming customers can stand to derive rich, analytic insights about game user behavior in less than a month from the date of configuration of their game/application. Subsequent analytics will be delivered in real-time through a combination of dashboards, queries and data visualization technology.

The speed and frequency at which insights about user behavior can be delivered to business and product development stakeholders means that customers can shorten their product development lifecycles as a result of the analytic insights delivered by the Treasure Data platform. Treasure Data’s gaming solution represents a specific use case for its platform for acquiring, storing and analyzing streaming data. Data analysis takes place either via SQL, Treasure Data’s proprietary dashboards or connections to business intelligence applications such as Tableau for data visualization purposes. Importantly, the platform is deployed via a fully managed service whereby customers make no investment in the hardware required for data acquisition and storage but are nevertheless able to export their data at any time for deeper dives and customized analytics. Other use cases for the Treasure Data platform include acquisition and analysis of meteorological, cartographic or telemetry-based data, web-based data, or data from the internet of things.

Treasure Data Partners With Tableau Software For Its Big Data Acquisition, Storage And Analytics Platform

Today, Treasure Data partnered with Tableau Software to enhance the ability of its customers to visualize and produce analytics on data stored within its platform. As a result of the partnership, “Tableau technology [will be] directly integrated into the Treasure Data Service,” meaning Treasure Data customers can easily take advantage of Tableau’s renowned data analytics and business intelligence capabilities. Tableau, meanwhile, stands to benefit from contributing to Treasure Data’s cloud-based, managed service for big data acquisition and analysis. Like Amazon Kinesis, Treasure Data is designed to accommodate the collection of data from sensors, internet feeds, machine logs and machine data from the internet of things. Given the managed nature of the offering, Treasure Data handles data management functionality such as the scaling of the database, fail-over, load balancing and replication. Subsequent to loading, customers can analyze and visualize their data in minutes as illustrated below:

As the graphic indicates, the Treasure Data service features two modalities of acquisition: deployment of a code-based “Treasure Agent” designed to collect real-time streaming data, or a bulk import from databases or applications by means of a parallel loading process. Customers have the option of filtering incoming data or storing it raw, within Treasure Data’s “proprietary database technology as well as Hadoop components.” Once data has been successfully loaded, customers can analyze the data using Tableau’s integrated technology, Treasure Viewer, SQL queries. Metric Insights or Excel.

Today’s news of Treasure Data’s partnership with Tableau builds upon a recent announcement regarding the company’s market traction as evinced by a quarterly growth rate of 50% for the addition of new customers, alongside a 90% quarterly growth rate for the amount of data stored within its platform. Treasure Data’s continued success will hinge on its ability to convince customers to opt for its fully managed big data acquisition and analytics platform as opposed to integrating a business intelligence platform on top of a Hadoop deployment. That said, Treasure Data’s technology for facilitating the acquisition of streaming data has few counterparts in the industry, until, of course, competitors emerge and multiply within the big data landscape.