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IBM Accelerator for Machine Data Analytics, Part 2: Speeding up analysis of new log types

Sonali Surange (ssurange@us.ibm.com), Software Architect, IBM

Sonali Surange is an IBM Software Architect working on IBM's big data products and technologies. She has filed numerous patents, published over 15 technical papers with IBM developerWorks, and presented in numerous technical conferences. Sonali is a past recipient of the IBM Outstanding Technical Achievement Award, Women of Color STEM Technical All Star Award, and was recognized as an IBM developerWorks Professional Author in 2012.

Summary: Machine logs from diverse sources are generated in an enterprise in voluminous quantities. IBM® Accelerator for Machine Data Analytics simplifies the task of implementation required so analysis of semi-structured, unstructured or structured textual data is accelerated.

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Date: 17 Jan 2013
Level: Intermediate
PDF: A4 and Letter (2869 KB | 35 pages)Get Adobe® Reader®

Activity: 7166 views
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Conclusion

In this tutorial, you created a completely new log type to support email data. You can also add any of the existing rules to this log type to enrich it further!

At the Sample Outdoors company, Extraction configuration was changed to export all records to the CSV file as opposed to the top 2000. Further ad-hoc analysis was performed combining customer order information with email information to identify customers to follow up for remediation.

Acknowledgements

Thanks to Amit Rai (amitrai4@in.ibm.com) for his technical review, and to all the Machine Data Accelerator team members contributing to this feature. Also thanks to Thomas Friedrich and Robin Noble-Thomas for their help on BigInsights tooling.

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