InfoSphere BigMatch for Hadoop v11.4 - SPVC
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
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.
This course has no pre-requisites.
1. Introduction to Big Match for Apache Hadoop
- What is Big Match
- How Big Match Works
- Big Match Components
- Big Match Architecture
2. Big Match Data Model Definition
- Attribute Types
- Member Attributes
- Information Sources
3. PME Algorithm
- Comparison Functions
4. Bucket Analysis
- Bucket Optimization
- Bucket Concerns
- String Weights
- Numeric Weights
- Multi-dimensional Weights
- Troubleshooting Weights
6. HBase Tables
- HBase concepts
- Big Match commands
- Big Match Tables (.pmebktidx, .pmemdmidx, .pmeentidx)
- Best Practices
7. BigMatch Applications
- PME Derive
- PME Compare
- PME Link
- PME Analysis
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