Cloudera Search Training
Description
This course is aimed at developers and data engineers who want to index data in Hadoop to create powerful real-time queries and relate Cloudera Search to external applications. This course is part of the Developer Learning Path.
Cloudera Search brings full-text, interactive search and scalable, flexible indexing to Hadoop and an enterprise data hub. Powered by Apache Solr, Search delivers scale and reliability for a new generation of integrated, multi-workload queries.
PUE, Cloudera Strategic Partner, is authorized by this multinational to provide official training in Cloudera technologies.
PUE is also accredited and recognized to carry out consulting and mentoring services in the implementation of Cloudera solutions in the business field with the added value in the practical and business approach to knowledge that is translated in its official courses.
Audience and 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.
Objectives
At the end of the training, the participant will know:
- Perform batch indexing of data stored in HDFS and HBase
- Perform indexing of streaming data in near-real-time with Flume
- Index content in multiple languages and file formats
- Process and transform incoming data with Morphlines
- Create a user interface for your index using Hue
- Integrate Cloudera Search with external applications
- Improve the Search experience using features such as faceting, highlighting, spelling correction
Topics
Introduction
Overview of Cloudera Search
- What is Cloudera Search?
- Helpful Features
- Use Cases
- Basic Architecture
Performing Basic Queries
- Executing a Query in the Admin UI
- Basic Syntax
- Techniques for Approximate Matching
- Controlling Output
Writing More Powerful Queries
- Relevancy and Filters
- Query Parsers
- Functions
- Geospatial Search
- Faceting
Preparing to Index Documents
- Overview of the Indexing Process
- Understanding Morphlines
- Generating Configuration Files
- Schema Design
- Collection Management
Batch Indexing HDFS Data with MapReduce
- Overview of the HDFS Batch Indexing Process
- Using the MapReduce Indexing Tool
- Testing and Troubleshooting
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
Indexing HBase Data with Lily
- What is Apache HBase?
- Batch Indexing for HBase
- Indexing HBase Tables in Near-Real-Time
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
Improving Search Quality and Performance
- Delivering Relevant Results
- Helping Users Find Information
- Query Performance and Troubleshooting
Building User Interfaces for Search
- Search UI Overview
- Building a User Interface with Hue
- Integrating Search into Custom Applications
Considerations for Deployment
- Planning for Deployment
- Determining Hardware Needs
- Security Overview
- Collection Aliasing
Conclusion
