Overview:

Cloudera University’s Search training course is for developers and data engineers who want to index data in Hadoop for more powerful real-time queries and integrate Cloudera Search with external applications. This course is part of the developer learning path.

Delivery Method and Course Duration:

OnDemand: 180 days

Classroom: 3 days

Objectives:

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:

• Performing batch indexing of data stored in HDFS and HBase
• Indexing streaming data in near-real-time with Flume
• How to index content in multiple languages and file formats
• Processing and transforming incoming data with Morphlines
• Creating a user interface for an index using Hue
• Integrating Cloudera Search with external applications
• Improving the experience using faceting, highlighting, and spelling correction

Intended Audience & Prerequisites:

This course is intended for developers and data engineers with at least basic familiarity with Hadoop and experience programming in a general-purpose language such as Java, C, C++, Perl, or Python. Participants should be comfortable with the Linux command line and should be able to perform basic tasks such as creating and removing directories, viewing and changing file permissions, executing scripts, and examining file output. No prior experience with Apache Solr or Cloudera Search is required, nor is any experience with HBase or SQL

Advance Your Ecosystem Expertise

Cloudera Search brings full-text, interactive search and scalable, flexible indexing to an enterprise data hub. Powered by Apache Solr, Search delivers scale and reliability for a new generation of integrated, multi-workload queries

Course outlines:

1.      Introduction

2.      Overview of Cloudera Search

• What is Cloudera Search?
• Helpful Features
• Use Cases
• Basic Architecture

3.      Performing Basic Queries

• Executing a Query in the Admin UI
• Basic Syntax
• Techniques for Approximate Matching
• Controlling Output

4.      Writing More Powerful Queries

• Relevancy and Filters
• Query Parsers
• Functions
• Geospatial Search
• Faceting

5.      Preparing to Index Documents

• Overview of the Indexing Process
• Understanding Morphlines
• Generating Configuration Files
• Schema Design
• Collection Management

6.      Batch Indexing HDFS Data with MapReduce

• Overview of the HDFS Batch Indexing Process
• Using the MapReduce Indexing Tool
• Testing and Troubleshooting

7.      Near-Real-Time Indexing with Flume

• Overview of the Near-Real-Time Indexing Process
• Introduction to Apache Flume
• How to Perform Near-Real-Time Indexing with Flume
• Testing and Troubleshooting

8.      Indexing HBase Data with Lily

• What is Apache HBase?
• Batch Indexing for HBase
• Indexing HBase Tables in Near-Real-Time

9.      Indexing Data in Other Languages and Formats

• Field Types and Analyzer Chains
• Word Stemming, Character Mapping, and Language Support
• Schema and Analysis Support in the Admin UI
• Metadata and Content Extraction with Apache Tika
• Indexing Binary File Types with SolrCell

10.    Improving Search Quality and Performance

• Delivering Relevant Results
• Helping Users Find Information
• Query Performance and Troubleshooting

11.    Building User Interfaces for Search

• Search UI Overview
• Building a User Interface with Hue
• Integrating Search into Custom Applications

12.    Considerations for Deployment

• Planning for Deployment
• Determining Hardware Needs
• Security Overview
• Collection Aliasing

13.     Conclusion

I.       Advance Your Ecosystem Expertise