InfoSphere BigMatch for Hadoop v11.4 - SPVC

Course Details
Code: 2Z850G-AV-SPVC
Tuition (USD): $650.00 • Self Paced (2 days)
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Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

The IBM InfoSphere Big Match on Hadoop course will introduce students to the Probabilistic Matching Engine (PME) and how it can be used to resolve and discover entities across multiple data sets in Hadoop.  
Students will learn the basics of a PME algorithm including data model configuration, standardization, comparison and bucketing functions, weight generation, and threshold.
During the exercises, the student will work on a large use case, where they will apply their knowledge of Big Match to discover relationships be two data sets that can be used to understand the full view of the member data.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms

Who Can Benefit

The course is designed for a technical audience that will be setting up a custom algorithm for the Probabilistic Matching Engine to use Big Match on Apache Hadoop to compare, match and/or search member records across multiple data sets.

Prerequisites

This course has no pre-requisites.

Objective

  1. Understand the capabilities of the Probabilistic Matching Engine
  2. Understand how the Probabilistic Matching engine is used with Big Insights to solve certain use cases.
  3. Understand the technical framework of the Big Match solution and how member data is derived, bucketed and compared to produce a complete entity from multiple data sets.
  4. Create a project and data model using the Big Match Console
  5. Configure the HBase tables that will be used in a Big Match solution
  6. Configure an algorithm using he Big Match console that includes Standardization, Comparison and Bucketing functions.
  7. Set up Strings for Anonymous value, Equivalency values, Frequency values, and character maps using the Big Match console
  8. Set up and run the Weight Generation process
  9. Evaluate and set thresholds for the algorithm
  10. Deploy a new algorithm to Big Match
  11. Evaluate Entity results and reconfigure algorithm based on evaluation.  E.g. Large Buckets, Large Entities, Member not belonging to any buckets, etc