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MySQL & Big Data

"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.

Download the White Paper: MySQL and Hadoop - Big Data Integration »

With the exponential growth in data volumes and data types, it is important to consider the complete lifecycle of data, enabling the right technology to be aligned with the right stage of the lifecycle.


Tools to Integrate MySQL with Hadoop

Tools to Integrate MySQL with Hadoop


  • 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 either in batches using Apache Sqoop or in real time with the MySQL Applier for Hadoop. Streaming of new data directly from MySQL to HDFS enables "speed of thought" analytics.
  • 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 analytics for BI tools.

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