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About Course

This course is designed for professionals who are familiar with programming, linux commands and looking a career path in Data Analytics area. The targeted audience for this course can be –

Developers
IT Architects
Programmers
Business Intelligence professionals
Recent graduates looking for a career path in Big Data
This course is aiming to prepare you for the Big Data world by delivering in-depth knowledge of key concepts required to ingest and process data on a Hadoop cluster. In this course you will get the insight understanding of Hadoop ecosystem like Spark, Hive, Flume, Sqoop, and Impala.
After completing this course you will be able to -

Understand the core concept of HDFS
Distribute and process data using Hadoop cluster and Apache Spark
Use Sqoop and Flume for data loading
Work with Pig, Hive and YARN to perform data analytics
Understand the data structure in tables using Impala and Hive
There is no prerequisite required to join this course.

CURRICULUM

Big Data and Hadoop Developer

  • 1.1 Problems with Traditional Large-Scale Systems
  • 1.2 Hadoop!
  • 1.3 Data Storage and Ingest
  • 1.4 Data Processing
  • 1.5 Data Analysis and Exploration
  • 1.6 Other Ecosystem Tools
  • 2.1 Distributed Processing on a Cluster
  • 2.2 Storage: HDFS Architecture
  • 2.3 Storage: Using HDFS
  • 2.4 Resource Management: YARN Architecture
  • 2.5 Resource Management: Working with YARN
  • 3.1 Sqoop Overview
  • 3.2 Basic Imports and Exports
  • 3.3 Limiting Results
  • 3.4 Improving Sqoop’s Performance
  • 3.5 Sqoop 2
  • 4.1 Introduction to Impala and Hive
  • 4.2 Why Use Impala and Hive?
  • 4.3 Querying Data With Impala and Hive
  • 4.4 Comparing Hive and Impala to Traditional Databases
  • 5.1 Data Storage Overview
  • 5.2 Creating Databases and Tables
  • 5.3 Loading Data into Tables
  • 5.4 HCatalog
  • 5.5 Impala Metadata Caching
  • 6.1 Selecting a File Format
  • 6.2 Hadoop Tool Support for File Formats
  • 6.3 Avro Schemas
  • 6.4 Using Avro with Impala, Hive, and Sqoop
  • 6.5 Avro Schema Evolution
  • 6.6 Compression
  • 7.1 Partitioning Overview
  • 7.2 Partitioning in Impala and Hive
  • 8.1 What is Apache Flume?
  • 8.2 Basic Flume Architecture
  • 8.3 Flume Sources
  • 8.4 Flume Sinks
  • 8.5 Flume Channels
  • 8.6 Flume Configuration
  • 9.1 What is Apache Spark?
  • 9.2 Using the Spark Shell
  • 9.3 RDDs (Resilient Distributed Datasets)
  • 9.4 Functional Programming in Spark
  • 10.1 Creating RDDs
  • 10.2 Other General RDD Operations
  • 11.1 Spark Applications vs. Spark Shell
  • 11.2 Creating the SparkContext
  • 11.3 Building a Spark Application (Scala and Java)
  • 11.4 Running a Spark Application
  • 11.5 The Spark Application Web UI
  • 11.6 Configuring Spark Properties
  • 11.7 Logging
  • 12.1 Review: Spark on a Cluster
  • 12.2 RDD Partitions
  • 12.3 Partitioning of File-Based RDDs
  • 12.4 HDFS and Data Locality
  • 12.5 Executing Parallel Operations
  • 12.6 Stages and Tasks
  • 13.1 RDD Lineage
  • 13.2 RDD Persistence Overview
  • 13.3 Distributed Persistence
  • 14.1 Common Spark Use Cases
  • 14.2 Iterative Algorithms in Spark
  • 14.3 Graph Processing and Analysis
  • 14.4 Machine Learning
  • 14.5 Example: k-means
  • 15.1 Spark SQL and the SQL Context
  • 15.2 Creating DataFrames
  • 15.3 Transforming and Querying DataFrames
  • 15.4 Saving DataFrames
  • 15.5 DataFrames and RDDs
  • 15.6 Comparing Spark SQL, Impala, and Hive-on-Spark

Exam & Certification

Exam Name: CCA Spark and Hadoop Developer Exam
Exam Code: CCA175
Number of question: 10 – 12 Performance based
Exam duration: 120 Minutes
Passing marks: 70%
Language: English, Japanese

Hub4Tech.com

The fastest growing training company in the world.

1- Vijay Nagar, Indore
T. +91-90691 39140
T. +1-717 889 7087
E: info@hub4tech.com


Hub4Tech.com

The fastest growing training company in the world.

1- Vijay Nagar, Indore
T. +91-90691 39140
T. +1-717 889 7087
E: info@hub4tech.com



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Disclaimer

** The above course information is taken from Cloudera Inc., Apache Software Foundation

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