Get Started with Hadoop Hive HiveQL Languages

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Get Started with Hadoop Hive HiveQL
Languages
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Career Options Of Hadoop Big Data Certification

• Hadoop to HiveQL
• Uses of Hadoop
• Hive
• Remember that Hive is not
• Uses of HiveQL
• Major Reasons to use Hadoop for Data Science
• Bottom Line

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Hadoop to HiveQL
Apache Hadoop is the storage system which is
written in Java, which is an open-source,
fault-tolerant, and scalable framework. It gives
a platform to process a large amount of
data. Hadoop makes use of Data Lake, which
supports the storage of data in its original or
exact format. Hadoop is designed in such a way
through which there can be a scale up from single
servers to thousands of machines, each of which
offering local computation and storage.
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Uses of Hadoop
Uses of Hadoop

• There is no need to preprocess data before
  storing it (you may store as much data as you
  want and decide later how to use it)
• You may easily grow your system to handle more
  data easily by adding nodes (only a little
  administration is required)
• It is convenient to use for millions or billions
  of transactions
• Many cities, states, and countries make use of
  Hadoop to analyze data. For example, figuring out
  the traffic jams which can be controlled by the
  use of Hadoop (Concept of Smart City)
• Big data is also used by many businesses to
  optimize their data performance in an effective
  manner

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Hive

• Big Data Analyst
• Apache Hive is a data warehouse software project
  which was built on the top of Apache Hadoop for
  supplying data query and analysis.
• It makes use of declarative language, which is
  similar to SQL called HQL.
•  Hive allows programmers who are well-known with
  the language to write custom MapReduce framework
  to perform more knowledgeable analysis.

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EcoSystem Components
The functional features of Hive are-

• Data Summarization
• Query
• Analysis

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HQL

• The Hive Query Language is a SQL like an
  interface which is used to query data stored in
  the database and file systems that are integrated
  with Hadoop. It supports simple SQL like
  functions- CONCAT, SUBSTR, ROUND, etc. and
  aggregate functions like- SUM, COUNT, MAX, etc.
• It also supports clauses- GROUP BY and SORT BY.
  Also, it is possible to write user-defined
  functions using Hive Query Language (HQL).
   Basically, it makes use of the well-known
  concepts from the relational database world,
  like- tables, rows, columns, and schema.

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Uses of HiveQL

• HQL is the twin of SQL
• HQL allows programmers to plug-in custom mappers
  and reducers
• HQL is scalable, familiar, extensible, and fast
  to use
• It provides indexes to correct queries
• HQL contains a large number of user function APIs
  which can be used to create custom behavior into
  the query engine
• It perfectly fits in the requirement of a
  low-level interface of Hadoop

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Major Reasons to use Hadoop for Data Science

• When you have to deal with a large amount of
  data, Hadoop is the best option to choose When
  you are planning to implement Hadoop on your
  data, the first step is to understand the
  complexity level of data and the data-rate based
  on which data is going to grow.
• In this case, cluster planning is required.
  Depending upon the size of data of the company
  (GBs or TBs), Hadoop is helpful here.
• Different types of data
• Numeric data
• Nominal data
• Different specific applications

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Bottom Line
Hadoop has become de-facto of Data Science and is
the gateway of Big Data related technologies. It
is the foundation of other Big Data technologies
like Spark, Hive, etc. As per Forbes Hadoop
market is expected to reach 99.318 by 2022 at a
CAGR of 42.1 percent. So, this is the right time
to give a push to your skills in the field of Big
Data. Happy Reading!
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Thank you
Happy learning