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Learn Data Analytics With Hadoop Theory And Real Time Example

Download Data Analytics With Hadoop free in PDF. In this notes you will learn how to perform a wide range of techniques. This notes shows you why the Hadoop ecosystem is perfect for the job. You’ll focus on practical analysis you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce.

In this notes you’ll also learn about the analytical processes and data system available to build and empower data products that can handle and actually require huge amount of data.

You Learn These Topics From This Notes: 

1.The Age Of The Data Product

  • What is a Data Product?
  • Building Data Products at Scale with Hadoop
  • Leveraging Large Dataset
  • The Data Science Pipeline and The Hadoop Ecosystem
  • Big Data Workflows

2. An Operating System with Big Data

  • Basic Concept
  • Hadoop Architecture
  • Working with a Distributed File System
  • Basic File System Operations
  • Working with a Distributed Computation

3. A Framework for Python and Hadoop Streaming

  • Hadoop Streaming
  • A Framework for Map Reduce with Python
  • Counting Bigrams
  • Other Frameworks
  • Advance Map Reduce

4. In Memory Computing with Spark 

  • Spark Basics
  • The Spark Stack
  • Interacting Spark with PySpark
  • writing Spark Applications

5. Distributed Analysis And Patterns

  • Computing with Keys
  • Compound Keys
  • Key space Patterns
  • Design Patterns
  • Toward Last Mile Analytics

6. Data Mining And Warehousing

  • Structure Data Queries with Hive
  • The Hide Command Line Interface
  • Data Analysis with Hive
  • Hbase

7. Data Ingestion

  • Importing Relational Data with Sqoop
  • Importing from MySQL HDFS
  • Importing from MYSQL to Hive
  • Ingesting Streaming Data Flumes

8. Analytics With Higher Level APIs

  • Pig
  • Pig Latin
  • Data Types
  • Relational Operators
  • Spark’s Higher Level APIs

9. Machine Learning

  • Scalable Machine Learning with Spark
  • Collaborative Filtering
  • Classification
  • Clustering

10. Summary: Doing Distributive Data Science

  • Data Product Lifecycle
  • Data Lakes
  • Data Ingestion
  • Computational Data Stores
  • Machine Learning Lifecycle





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