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Big Data Hadoop Certification Training (Advanced Data Analytics)

Big Data Hadoop Certification Training (Advanced Data Analytics)

Big Data Hadoop Certification Training (Advanced Data Analytics)

  • Category
    Big Data
  • Duration
    50 Hours
  • Rating
  • Price
     $499  $299

Course Introduction

Course Highlight
  • Duration
    50 hours
  • Type
    Online Classroom
  • Includes
    Certificate
About the course

In this 50 hours training you will be briefed on various aspects on Data Analytics right from Data Extraction to Data Mining, Data Transformation, Data processing and Data Analysis. You will be walked through different types of Data Analytical Models and solutions available for the same.

Course Objectives
  •  Understanding Data Analytical Models
  •  Drive through cognitive and predictive Data Analytical Models
  •  Understand importance of Data Analysis
  •  Walk through use cases of Data Analysis
  •  Study Tools used for Data ingestion, processing and Analysis
  •  Visualize Data using different tools

Who is the target audience?

Programmers, Developers, Technical Leads, ETL and Data Warehousing Professionals, Freshers, Data Scientists, Data Analysts, Business Intelligence Managers.

Basic knowledge:

 There are no such prerequisites for Big Data & Hadoop Course.

Curriculum
Total Duration : 50 Hours
Course Index
  • Introduction to Data Analysis  
    • Process of Data Analysis
    • Types of Data Analysis
    • Quantitative Messages
    • Techniques for Analysing Data
    • Barriers to effective Data Analysis
    • Available solutions
  • Introduction to Big Data  
    • Understand meaning of Big Data
    • Explore Big Data possibilities
    • Measure the depth of Big Data
    • Get introduced to the 3 Vs of Big Data
    • See through different Big Data solutions
  • Introduction to Hadoop  
    • Understand the Hadoop Architecture
    • Work around the different components of Hadoop
    • Install Hadoop Cluster in single and Multi-Node Environment
    • Work with Hadoop Job process
    • Understand Map Reduce Programming Model
    • Execute Map Reduce Programs
  • Data Ingestion  
    • Import data from a Table in a relational database into HDFS
    • Import the results of a query from a relational database into HDFS
    • Import a table from a relational database into a new or existing Hive table
    • Insert or update data from HDFS into a table in a relational database
    • Given a Flume configuration file, start a Flume agent
    • Given a configured sink and source, configure a Flume memory channel with a specified capacity
  • Data Transformation  
    • Write and execute a pig script
    • Load data into a pig relation without a schema
    • Load data into a pig relation with schema
    • Load data from a Hive table into a pig relation
    • Use pig to transform data into a specified format
    • Transform data to match a given Hive schema
    • Group the data of one or more pig relations
    • Use pig to remove records with null values from a relation
    • Store the data from a pig relation into a folder in HDFS
    • Store the data from a pig relation into a Hive table
    • Sort the output of a pig relation
    • Specify the number of reduce tasks for a pig MapReduce job
    • Join two datasets using pig
    • Run a pig job using Tez
    • Within a pig script, register a JAR file of user Defined Functions
    • Within a pig script, define an alias for a user Defined Function
    • Within a pig script, invoke a user Defined Function
  • Data Analysis  
    • Write and execute a Hive query
    • Define a Hive-managed table
    • Define a Hive external table
    • Define a partitioned Hive table
    • Define a bucketed Hive table
    • Define a Hive table from a select query
    • Define a Hive table that uses the ORC File format
    • Create a new ORCFile table form the data in an existing non-ORCFile Hive table
    • Specify the storage format of a Hive table
    • Specify the delimiter of Hive table
    • Load data into a Hive table from a local directory
    • Load data into a Hive table from a HDFS directory
    • Load data into a Hive table as the result of a query
    • Load a compressed data file into a Hive table
    • Update a row in a Hive table
    • Delete a row from a Hive table
    • Insert a new row into a Hive table
    • Join two Hive tables
    • Run a Hive query using Tez
    • Output the execution plan for a Hive query
    • Use a subquery within a Hive query
    • Set a Hadoop or Hive configuration property from within a Hive query
  • Hadoop Yarn  
    • Brief through the limitation of Hadoop 1
    • Understand of working of Yarn, HA and Federation
    • Install Hadoop Yarn on Single Node
  • Core Spark  
    • Create an RDD
    • Create an RDD from a file or directory in HDFS
    • Persist an RDD in memory or on disk
    • Perform Spark transformations on an RDD
    • Perform spark actions on RDD
    • Create and use broadcast variables and accumulators
    • Configure spark properties
  • Spark SQL  
    • Create Spark Data Frames from an existing RDD
    • Perform operations on a Data Frame
    • Write a Spark SQL application
    • Filter data using Spark
    • Write queries that calculate aggregate statistics
    • Join disparate datasets using Spark
    • Produce ranked or sorted data
    • Use Hive with ORC from Spark SQL
    • Write a Spark SQL application that reads and writes data from Hive tables
  • Data Visualization and Integration  
    • Creating Transformation using Pentaho
    • Creating Graphs using tableau
    • Visualize Data with interactive Models