Extracting Business Value From Big Data With Pig and Hive

Level: Intermediate
Rating: 5/5 5.00/5 Based on 7 Reviews

This course will teach you to leverage Pig and Hive for big data to prepare & analyze large data sets on Hadoop to make more informed and timely business decisions. You will learn to increase productivity by avoiding low-level Java coding characteristic of MapReduce, and rapidly begin extracting business value for competitive advantage. In this Pig & Hive for Big Data training course, you will learn to gain access to previously inaccessible data, gather and feed data into Hadoop for storage, transform and filter data using Pig, and extract value using Hive and Spark SQL.

Key Features of this Pig and Hive for Big Data Training

  • After-course instructor coaching benefit
  • Learning Tree end-of-course exam included
  • After-course computing sandbox included

You Will Learn How To

  • Manipulate complex data sets stored in Hadoop for competitive advantage
  • Automate the transfer of data into Hadoop storage with Flume and Sqoop
  • Filter data with Extract-Transform-Load (ETL) operations using Pig
  • Query multiple data sets for analysis with Pig and Hive
  • Perform real-time queries on Hadoop data with Tez and Spark SQL


CPE 23 Credits

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In Class & Live, Online Training

  • 4-day instructor-led training course
  • After-course instructor coaching benefit
  • Learning Tree end-of-course exam included
  • Earn 23 NASBA credits (live, in-class training only)
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Standard $2990

Government $2659




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In Class & Live, Online Training

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Note: This course runs for 4 Days

  • Dec 15 - 18 9:00 AM - 4:30 PM EST Online (AnyWare) Online (AnyWare) Reserve Your Seat

  • Mar 23 - 26 9:00 AM - 4:30 PM EDT Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare) Reserve Your Seat

  • Jun 1 - 4 9:00 AM - 4:30 PM EDT New York / Online (AnyWare) New York / Online (AnyWare) Reserve Your Seat

  • Sep 21 - 24 9:00 AM - 4:30 PM EDT Herndon, VA / Online (AnyWare) Herndon, VA / Online (AnyWare) Reserve Your Seat

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Pig and Hive Course Information

  • Recommended Experience

    • Knowledge of databases and SQL

Pig and Hive Course Outline

  • The Hadoop Ecosystem

    • Hadoop overview
    • Surveying the Hadoop components
    • Defining the Hadoop architecture
  • Exploring HDFS and MapReduce

    Storing data in HDFS

    • Achieving reliable and secure storage
    • Monitoring storage metrics
    • Controlling HDFS from the Command Line

    Parallel processing with MapReduce

    • Detailing the MapReduce approach
    • Transferring algorithms not data
    • Dissecting the key stages of a MapReduce job

    Automating data transfer

    • Facilitating data Ingress and Egress
    • Aggregating data with Flume
    • Configuring data fan in and fan out
    • Moving relational data with Sqoop
  • Executing Data Flows with Pig

    Describing characteristics of Apache Pig

    • Contrasting Pig with MapReduce
    • Identifying Pig use cases
    • Pinpointing key Pig configurations

    Structuring unstructured data

    • Representing data in Pig's data model
    • Running Pig Latin commands at the Grunt Shell
    • Expressing transformations in Pig Latin Syntax
    • Invoking Load and Store functions
  • Performing ETL with Pig

    Transforming data with Relational Operators

    • Creating new relations with joins
    • Reducing data size by sampling
    • Extending Pig with user–defined functions

    Filtering data with Pig

    • Consolidating data sets with unions
    • Partitioning data sets with splits
    • Injecting parameters into Pig scripts
  • Manipulating Data with Hive

    Leveraging business advantages of Hive

    • Factoring Hive into components
    • Imposing structure on data with Hive

    Organizing data in Hive Data Warehouse

    • Creating Hive databases and tables
    • Contrasting available data types in Hive
    • Loading and storing data efficiently with SerDes

    Designing data layout for maximum performance

    • Populating tables from queries
    • Partitioning Hive Tables for optimal queries
    • Composing HiveQL queries
  • Extracting Business Value with HiveQL

    Performing joins on unstructured data

    • Distinguishing joins available in Hive
    • Optimizing join structure for performance

    Pushing HiveQL to the limit

    • Sorting, distributing and clustering data
    • Reducing query complexity with views
    • Improving query performance with indexes

    Deploying Hive in production

    • Designing Hive schemas
    • Setting up data compression
    • Debugging Hive scripts

    Streamlining storage management with HCatalog

    • Unifying the data view with HCatalog
    • Leveraging HCatalog to access the Hive metastore
    • Communicating via the HCatalog interfaces
    • Populating a Hive table from Pig
  • Interacting with Hadoop Data in Real Time

    • Performing low-latency queries with Impala
    • Leveraging the Tez execution engine to improve performance
    • Reducing data  access time with Spark SQL

Team Training

Pig and Hive for Big Data Training FAQs

  • What do Pig and Hive contribute to Big Data Processing?

    Hadoop programming at the low level is done in Java. Pig and Hive provide ease of programming by allowing the programmer to write scripts in a simpler language, Pig Latin or HiveQL. Those scripts are compiled and optimized internally and equivalent Java code generated and executed without the programmer having to write the Java code.

  • Where do Pig and Hive fit in my data science training?

    Data science algorithms need to ingest data from an appropriate storage technology like a relational database, NoSQL database, or Hadoop distributed file system. Before this data can be fed to the algorithm it has to be cleaned and structured. The data wrangling stage is where Pig and Hive are particularly useful.?

  • What is Pig in big data?

    Apache Pig is a platform for analyzing large data sets. Programs are written in a high-level, Pig Latin. They are converted by Pig's infrastructure into sequences of Java MapReduce programs which are then executed on Hadoop. Without writing Java one can use Pig to leverage Hadoop's ability to process data in parallel. 

  • What is Hive in big data?

    Apache Hive is data warehouse software that translates commands written in a SQL-like language, HiveQL, into Hadoop MapReduce jobs that are then executed on Hadoop. Without writing Java one can use Hive to leverage Hadoop's ability to process data in parallel. 

  • When should one use Pig and when should one use Hive?

    Pig is typically used early in the data pipeline to clean and structure data. Hive is typically used later when there is structure and well-defined fields. Since Hive has the concepts of tables, rows and columns it integrates easily with BI tools.

  • Can I learn to use Pig and Hive to extract value from big data online?

    Yes! We know your busy work schedule may prevent you from getting to one of our classrooms which is why we offer convenient online training to meet your needs wherever you want, including online training.

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