10 can’t miss posts for big data and analytics developers: June 2014

IBM big data and analytics? Why the and? What could possibly be missing from the big circle that includes all the data pouring in from social media, mobile apps and devices, instrument sensors, machine logs, cloud services and traditional data sources?

As it turns out, big data alone, as large and varied as it is, lacks the power to deliver real-time insights to transform a business. With the right analytics, however, big data becomes a monitor of what’s happening, a diagnosis of why it’s happening, a prediction of what’s likely to happen and a prescription for the best action to take now.

The combination, big data and analytics, leads to interesting opportunities and business-changing revelations. To support this worthy partnership, IBM developerWorks is merging content for big data and analytics into a single, easy-to-search site. Beginning in June, the merged, weekly newsletter will include the newest content on big data and analytics. Subscribe now to the combined newsletter, the “Big data and analytics roundup,” so you won’t miss an issue.

Until then, explore these resources, which help developers tackle big data and analytics on an enterprise level.

  1. Data warehouse augmentation, Part 1: Big data and data warehouse augmentationLearn how to combine traditional and big data technologies to maximize and augment the effectiveness of existing data warehouses to accommodate the load that new data sources and analytic workloads bring to an enterprise data warehouse.
  2. Agile migration of a single-node cluster from MapReduce Version 1 to YARNYARN, a completely rewritten architecture of the processing platform in Hadoop, makes computation much more scalable, efficient and flexible. Learn how to migrate a Hadoop cluster from MapReduce Version 1 to YARN in an agile way.
  3. Integrate DB2 for z/OS with InfoSphere BigInsights, Part 1: Set up the InfoSphere BigInsights connector for DB2 for z/OSUsing a scenario that is common to all DB2 for z/OS users, learn how to enable access to structured and non-structured data that is stored in the Hadoop Distributed File System and send the results back to DB2.
  4. Real-time data analytics using IBM Predictive Maintenance and QualityFor capital-intensive assets-based industries such as oil and gas exploration and production, you need access to real-time production figures and accurate predictions of future production. Use IBM Predictive Maintenance and Quality to load production data in real time, aggregate data, predict production and populate the data store to refresh dashboards.
  5. Improve performance of Big SQL in InfoSphere BigInsights with memory-related parametersLearn how to configure memory-related parameters for Big SQL to improve the performance of the Big SQL server. Take advantage of automatic configuration for certain parameters.
  6. Monitor and adjust resource allocation in a PureData for Analytics system using IBM Netezza Performance Portal 2.1Apply new features of the IBM Netezza Performance Portal 2.1 and PureData for Analytics 7.1 to monitor and adjust resource utilization of the data warehouse appliance. Learn how to manage scheduler rules to exert more direct control over the scheduling and execution of queries.
  7. Proven Practice: Time Dimension in IBM Cognos TM1 Performance Modeler and IBM Cognos InsightFind out how time dimensions work in IBM Cognos Insight and IBM Cognos TM1 Performance Modeler. Learn how to create a date time dimension from scratch or from an existing data source.
  8. Pig versus Hive: Benchmarking high level query languagesExplore the benchmarking results of two benchmarking sets applied to Hive and Pig, running on Hadoop 0.14.1. Which is faster? The two studies show conflicting results.
  9. Proven Practice: IBM Cognos BI Report Studio - Measure Based Value PromptCreate a value prompt on a measure data item in an IBM Cognos BI Report Studio report using a relational package and prevent the aggregation of those values in the prompt selection, using the steps described in this article.
  10. Use SPSS Statistics direct marketing analysis to gain insightLearn how to use the RFM analysis process of the Direct Marketing module of IBM SPSS Statistics. Using this process, nontechnical users can analyze their customer data.

Find more deep-dive technical how-to content on big data and analytics tools, technologies, and software at IBM developerWorks big data and analytics.