TITLE: SCALING BIG DATA WITH HADOOP AND SOLR

PUBLISHER....: PACKT                LANGUAGE......: ENGLISH
LINK.........: http://is.gd/r6cAw3  RELEASE TYPE..: RETAIL
FORMAT.......: PDF                  RELEASE DATE..: 2014.01.23
ISBN.........: 9781783281374        STORE DATE....: 2013
SAVED.MONEY..: 27 DOLLAR            DISKCOUNT.....: 01 x 05MB

AUTHOR.......: HRISHIKESH KARAMBELKAR

As data grows exponentially day-by-day, extracting information
becomes a tedious activity in itself. Technologies like Hadoop
are trying to address some of the concerns, while Solr provides
high-speed faceted search. Bringing these two technologies
together is helping organizations resolve the problem of
information extraction from Big Data by providing excellent
distributed faceted search capabilities.

Scaling Big Data with Hadoop and Solr is a step-by-step guide
that helps you build high performance enterprise search engines
while scaling data. Starting with the basics of Apache Hadoop and
Solr, this book then dives into advanced topics of optimizing
search with some interesting real-world use cases and sample Java

Scaling Big Data with Hadoop and Solr starts by teaching you the
basics of Big Data technologies including Hadoop and its
ecosystem and Apache Solr. It explains the different approaches
of scaling Big Data with Hadoop and Solr, with discussion
regarding the applicability, benefits, and drawbacks of each
approach. It then walks readers through how sharding and indexing
can be performed on Big Data followed by the performance
optimization of Big Data search. Finally, it covers some
real-world use cases for Big Data scaling.

With this book, you will learn everything you need to know to
build a distributed enterprise search platform as well as how to
optimize this search to a greater extent resulting in maximum
utilization of available resources.

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