These resources will provide a basic understand of where cloud and data technologies intersect

Kane Scarlett

Databases, big data, and analytics -- regardless of the shape of your data resources environment, cloud infrastructure is designed to contain, move, and use that data to make your development, administration, and business tasks more efficient.

Since the beginning of the cloud era, developerWorks has been illuminating the technical relationship between data and cloud. Here is a sample of the knowledge we've supplied to make your work in the data cloud more successful:

• Processing large datasets: One of the best tools for large-scale dataset processing in the cloud is Hadoop. Follow along as Martin Brown explains how to overcome the challenges and maximize the advantages of a cloud-based Hadoop deployment.
• Database interoperability: Usable data can be found in any type of storage, so it is critical to be able to have all the data repositories you use be able to communicate. These two articles -- Creating tables, loading data and Handling queries, reporting business intelligence -- demonstrate database interoperability between Big SQL and HBase.
• Integrating powerful analysis tools: In the example Using R with databases, you see how to bring the strength of an efficient data-analysis programming language, R, to bear on data that's housed in relational database servers.
• The role of security: Data access cannot be mentioned without considerations for the security of that data. developerWorks security site is an excellent place to start to understand how to provide data security. In this article on machine data analytics, you get a tour of how to deliver drop-in-place safety and security monitors that are combined with cloud-based data analytics, which make it possible to quickly deploy monitoring in areas without power or networking infrastructure.
• Hands-on tool and app building: Best of all, developerWorks offers lots of hands-on documentation to show you how others have built the cloud data application or tool they needed; often, the apps were built in a cloud development environment such as IBM Bluemix. For example:
  • You can learn how to develop a public transportation system simulator with Node.js, then connect the app to BLU Acceleration which provides the ability to process and evaluate large amounts of data in a short time.
  • You can see how to use Cloudant and Raspberry Pi to build a remote surveillance app that manages a lot of video data.
  • You can build a data mining app using Java, Weka, and an Analytics Warehouse service.

Visit the developerWorks big data and analytics site for more insight on the relationship between data resources and the cloud environment.

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