Cursos de Big Data - Data Science

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Código del Curso

bigd_LBG

Duración

14 horas (usualmente 2 días, incluidas las pausas)

Requerimientos

Delegates should have an awareness and some experience of storgage tools and an awreness of handling large data sets

Descripción General

Esta sesión de capacitación basada en el aula explorará Big Data . Los delegados tendrán ejemplos basados en computadora y ejercicios de estudio de casos para llevar a cabo con herramientas relevantes de big data

Machine Translated

Programa del Curso

  1. Big data fundamentals
    • Big Data and its role in the corporate world
    • The phases of development of a Big Data strategy within a corporation
    • Explain the rationale underlying a holistic approach to Big Data
    • Components needed in a Big Data Platform
    • Big data storage solution
    • Limits of Traditional Technologies
    • Overview of database types
    • The four dimensions of Big Data
  2. Big data impact on business
    • Business importance of Big Data
    • Challenges of extracting useful data
    • Integrating Big data with traditional data
  3. Big data storage technologies
    • Overview of big data technologies
      • Data storage models
      • Hadoop
      • Hive
      • Cassandra
      • MongoDB
    • Choosing the right big data technology
  4. Processing big data
    • Connecting and extracting data from database
    • Transforming and preparation data for processing
    • Using Hadoop MapReduce for processing distributed data
    • Monitoring and executing Hadoop MapReduce jobs
    • Hadoop distributed file system building blocks
    • Mapreduce and Yarn
    • Handling streaming data with Spark
  5. Big data analysis tools and technologies
    • Programming Hadoop with Pig Latin language
    • Querying big data with Hive
    • Mining data with Mahout
    • Visualizing and reporting tools
  6. Big data in business
    • Managing and establishing Big Data needs
    • Business importance of Big Data
    • Selecting the right big data tools for the problem

 

Data Warehousing Concepts

  • What is Data Ware House?
  • Difference between OLTP and Data Ware Housing
  • Data Acquisition
  • Data Extraction
  • Data Transformation.
  • Data Loading
  • Data Marts
  • Dependent vs Independent data Mart
  • Data Base design

ETL Testing Concepts:

  • Introduction.
  • Software development life cycle.
  • Testing methodologies.
  • ETL Testing Work Flow Process.
  • ETL Testing Responsibilities in Data stage.       

Big data Fundamentals

  • Big Data and its role in the corporate world
  • The phases of development of a Big Data strategy within a corporation
  • Explain the rationale underlying a holistic approach to Big Data
  • Components needed in a Big Data Platform
  • Big data storage solution
  • Limits of Traditional Technologies
  • Overview of database types

NoSQL Databases

Hadoop

Map Reduce

Apache Spark

 

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