Tuesday Apr 30, 2013

Big Fish Selects MySQL Cluster for Real-Time Web Recommendations

The world's largest producer of casual games has selected MySQL Cluster to power its real-time recommendations platform.

High velocity data ingestion, low latency reads, on-line scaling and the operational simplicity delivered by MySQL Cluster has enabled Big Fish to increase customer engagement and deliver targeted marketing, providing a more personalized experience to its users.

You can read the full Big Fish Games and MySQL Cluster case study here - and a summary below

BUSINESS NEED

The global video gaming market is experiencing explosive growth. Competition is intense, and so to differentiate services and engage users, progressive gaming companies such as Big Fish are seeking solutions to more fully personalize the customer experience.

Using Business Intelligence (BI) and predictive analytics Big Fish can segment customers based on a range of demographic and behavioural indicators. This enables Big Fish to serve highly targeted recommendations and marketing, precisely personalized to a user's individual preferences.

Big Fish's Marketing Management Service platform, powered by MySQL Cluster, is used across all of the company's customer management systems, including customer support and the company's "Game Manager", to provide a unique customer experience to each of its millions of monthly users whenever they come in contact with Big Fish.

TECHNOLOGY SELECTION

Big Fish already has an extensive deployment of MySQL databases powering web applications, including the storefront. They knew MySQL could power the recommendations database, but would require additional engineering efforts to implement database sharding to support data ingest and future scaling needs, coupled with a Memcached layer for low-latency reads.

As a result, they began evaluations of MySQL Cluster, in addition to other database technologies. Using MySQL Cluster, the Engineering teams were able to leverage their existing MySQL skills, enabling them to reduce operational complexity when compared to introducing a new database to the Big Fish environment.

At the same time, they knew MySQL Cluster, backed by Oracle, provided the long-term investment protection they needed for the MMS recommendations platform.

Through their evaluation, the Big Fish engineering team identified MySQL Cluster was best able to meet their technical requirements, based on:

Write performance to support high velocity data ingest

Low latency access with in-memory tables 

On-line scalability, adding nodes to a running cluster

Continuous availability with its shared-nothing architecture

SQL and NoSQL APIs to the cluster supporting both fast data loading and complex queries

PROJECT IMPLEMENTATION

As illustrated in the figure below:

  • User data is replicated from the MySQL databases powering the gaming storefront to the Big Fish BI platform;
  • User data is analyzed and segmented within the BI platform;
  • Recommendations are loaded as user records into MySQL Cluster using the NoSQL Cluster_J (Java) Connector;
  • The SQL interface presented by the MySQL Servers then delivers personalized content to gamers in real-time, initially serving over 15m sessions per day.


Big Fish has subscribed to MySQL Cluster CGE providing the Engineering team with access to 24x7 Oracle Premier Support and MySQL Cluster Manager, which reduces operational overhead by providing:

  • Automated configuration and reconfiguration of MySQL Cluster;
  • Automated on-line node addition for on-demand scaling.

The online scalability of MySQL CGE can help Big Fish to meet future requirements, as it expands its use of MySQL CGE to include all new website developments, channels and gaming platforms. 

LEARN MORE

Read the full Big Fish Games and MySQL Cluster case study

Read the press release 


Thursday Nov 29, 2012

MySQL and Hadoop Integration - Unlocking New Insight

“Big Data” offers the potential for organizations to revolutionize their operations. With the volume of business data doubling every 1.2 years, analysts and business users are discovering very real benefits when integrating and analyzing data from multiple sources, enabling deeper insight into their customers, partners, and business processes.

As the world’s most popular open source database, and the most deployed database in the web and cloud, MySQL is a key component of many big data platforms, with Hadoop vendors estimating 80% of deployments are integrated with MySQL.

The new Guide to MySQL and Hadoop presents the tools enabling integration between the two data platforms, supporting the data lifecycle from acquisition and organisation to analysis and visualisation / decision, as shown in the figure below


The Guide details each of these stages and the technologies supporting them:

Acquire: Through new NoSQL APIs, MySQL is able to ingest high volume, high velocity data, without sacrificing ACID guarantees, thereby ensuring data quality. Real-time analytics can also be run against newly acquired data, enabling immediate business insight, before data is loaded into Hadoop. In addition, sensitive data can be pre-processed, for example healthcare or financial services records can be anonymized, before transfer to Hadoop.

Organize: Data is transferred from MySQL tables to Hadoop using Apache Sqoop. With the MySQL Binlog (Binary Log) API, users can also invoke real-time change data capture processes to stream updates to HDFS.

Analyze: Multi-structured data ingested from multiple sources is consolidated and processed within the Hadoop platform.

Decide: The results of the analysis are loaded back to MySQL via Apache Sqoop where they inform real-time operational processes or provide source data for BI analytics tools.

So how are companies taking advantage of this today? As an example, on-line retailers can use big data from their web properties to better understand site visitors’ activities, such as paths through the site, pages viewed, and comments posted. This knowledge can be combined with user profiles and purchasing history to gain a better understanding of customers, and the delivery of highly targeted offers.

Of course, it is not just in the web that big data can make a difference. Every business activity can benefit, with other common use cases including:

- Sentiment analysis;

- Marketing campaign analysis;

- Customer churn modeling;

- Fraud detection;

- Research and Development;

- Risk Modeling;

- And more.

As the guide discusses, Big Data is promising a significant transformation of the way organizations leverage data to run their businesses. MySQL can be seamlessly integrated within a Big Data lifecycle, enabling the unification of multi-structured data into common data platforms, taking advantage of all new data sources and yielding more insight than was ever previously imaginable.

Download the guide to MySQL and Hadoop integration to learn more. I'd also be interested in hearing about how you are integrating MySQL with Hadoop today, and your requirements for the future, so please use the comments on this blog to share your insights.




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