Although gaming is a business built on Big Data, we don’t want to be in the business of Big Data. That’s why we chose Qubole to tackle our Big Data challenges for us. Qubole’s Big Data as a Service solution has addressed our scalability, integration, cluster management issues, and much more.
Online Games Company
A global leader in the mobile and social gaming industry. The company uses Big Data to increase the use and monetization of its games.
Succeeding in the gaming industry isn’t child’s play. The gaming developer needed to understand player behavior to optimize its games so that players stay in their “flow zone” and keep coming back for more. The game company also had to better monetize its games and improve virality factors to attract more players.
The gaming company realized from the very start that Hadoop was the best platform for its Big Data requirements to analyze petabytes of granular player interactions and contextual data, both structured and unstructured, every month. Yet, the business had trouble with its internal efforts to realize the full potential of this Big Data platform, running into scalability, usability and integration issues.
Analyzing millions of users, billions of user events, and hundreds of metrics across dozens of titles proved problematic. Jobs would take too long to run or just simply time out. The issue was made worse during crunch time when marketing, product management and analysts needed immediate insights critical to moving forward with game production decisions. Compute resources couldn’t keep up with peak demand.
Gaming analytics involves a cauldron of data sources to obtain the broad understanding of players and their activities that will help identify the features gamers like, how to expose additional players to the game, and how to get players to keep playing. For example, the gaming company wanted to merge social interaction data with game logs to analyze conversations within a game. Or, to acquire users likely to stay and play, it needed to access US census data such as income and paying status and player demographics in its CRM system.
The gaming company began its Hadoop initiative as a proof-of-concept with a single cluster. As it moved into production, the implementation naturally grew in size, leaving the gaming company wanting a more automated approach to configuring, deploying and managing its Hadoop clusters.
Qubole Data Service (QDS) in Action
Using QDS’ fully elastic Hadoop engine, the gaming company no longer hits a wall when processing large jobs or during peak demand. Auto-scaling automatically adds or removes compute resource based on actual usage to meet the Big Data volume and velocity requirements for granular gaming analytics.
QDS tackled the gaming company’s tough structured and unstructured data mashup challenges using its dozens of pre-built connectors as well as its ability to build custom connectors. No changes to the company’s on-premise infrastructure were required to integrate on-premise, cloud and social media data sources.
The game company lets QDS’ automated cluster management take care of all the work to configure, deploy and manage its clusters. Plus, the company now has less downtime due to a reduction in configuration errors and features such as auto-healing to replace failed instances.
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Qubole is a significantly more polished product than EMR. Data scientists can explore their data in S3, create tables and query those tables all via an easy-to-use web UI
Qubole’s fantastic support has been key in our successful deployment. They continue to deliver of new features and revisit the ones that we ask for
Our goal at MediaMath was to take our existing industry leading infrastructure to the next level handling new complex analytics tasks. Qubole has helped us enable this goal with minimal risk.
Instead of worrying about provisioning clusters of machines or job flows or whatever, Qubole lets you focus on your data and your queries … The Qubole guys have been extremely helpful!
The service spins up users’ clusters only when a job is started, then automatically scales or contracts them based on the workload, and spins the servers down once the job is done.
Qubole’s Hadoop and Hive interfaces are vastly superior to the default CLIs, which scare business analysts and hinder meaningful analyses of the gaming logs that we collect. With Qubole, business analysts are self-sufficient in using a Big Data platform to meet their advanced analytic needs.
Online Gaming Company
top-performing technologies in the data industry are definitely taking aim at democratizing data tools and bringing the power of data to smaller businesses. This is a major change in the data industry, and Qubole Data Service is a great example
I’m very happy to be using Qubole in production. Qubole has saved me a lot of time, effort, and trouble in getting my data processing pipelines up and running. My data pipelines process Appnexus data in Amazon S3 which is then stored in Vertica. The engineering team understands the complexities and provided awesome support!
Real-time Ads Retargeting Startup
There’s a whole world of web companies, SMBs and other non-Facebooks or Yahoos that will want to use Hadoop but not want to run it in-house…offering a cloud service makes it easier for these users to get started with the platform and for Qubole to keep improving.
Qubole offers a big data ETL and exploration service through auto-scaling Hadoop clusters with a web user interface for data exploration and integration with various data sources. The service can do (nearly) everything EMR can do, and it goes further
Big Data Republic
Simba knows Big Data access. Qubole knows Big Data. Qubole’s founders authored Apache Hive, built key parts of the Hadoop eco-system and brought Apache HBase to Facebook
“The integration of Tableau and Qubole makes it faster and easier for our customers to operationalize Big Data…lowers the resource barriers to deriving the benefits of Big Data because customers can deploy our joint solution seamlessly and cost effectively.”