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About This Course

Solr (pronounced "solar") is an open source enterprise search platform, written in Java, from the Apache Lucene project. Its major features include full-text search, hit highlighting, faceted search, real-time indexing, dynamic clustering, database integration, NoSQL features and rich document (e.g., Word, PDF) handling. Providing distributed search and index replication, Solr is highly scalable and fault tolerant. Solr is the most popular enterprise search engine.

Solr is written in Java and runs as a standalone full-text search server. Solr uses the Lucene Java search library at its core for full-text indexing and search, and has REST-like HTTP/XML and JSON APIs that make it usable from most popular programming languages. Solr's powerful external configuration allows it to be tailored to many types of application without Java coding, and it has a plugin architecture to support more advanced customization.

COURSE SYLLABUS

  • Lesson 1 - Search Engines
    • Understand the importance of text search engines
    • Understand the Solr search procedure
    • Identify Solr components
  • Lesson 2 - Configure and Add Documents to Solr
    • Identifying the important files in a Solr installation
    • Define the schema for documents in the index
    • Understand the various ways to add documents to Solr
  • Lesson 3 - Analyzers and Queries
    • Use analyzers, tokenizers, and filters
    • Construct queries
  • Lesson 4 - SolrJ and Customization
    • Create SolrJ applications
    • Understand the customization options available in Solr

Requirements

  • Basic Linux Operating System knowledge
  • Basic understanding of SQL and Java would be helpful

RECOMMENDED SKILLS PRIOR TO TAKING THIS COURSE

  • Basic knowledge of operating systems (UNIX/Linux)

Course Staff

Course Staff Image #1

James Priebe

James Priebe is an IBM intern located in Toronto, Ontario. He spends his time creating proof of concept applications for IBM business partners and developing courses for customer education. He has worked with a variety of technologies in Big Data family, including Streams, Hadoop, and Annotation Query Language (AQL). James is from McMaster University, where he has completed his third year of the Software Engineering & Management program.

GRADING SCHEME

  • The minimum passing mark for the course is 60%, where the review questions are worth 40% and the final exam is worth 60% of the course mark.
  • You have 1 attempt to take the exam with multiple attempts per question.
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    1. Course Number

      BD0137EN
    2. Classes Start

      Any Time, Self-Paced
    3. Estimated Effort

      3 Hours
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