Training Course: Big Data Management

IT235039 9 - 13 Mar 2025 Cost : 2275 € Euro
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Introduction:

The world today is producing vast amounts of data, and the ability to manage and analyze this data effectively is becoming increasingly important for businesses of all sizes. This training program is designed to provide participants with an overview of the principles and practices of big data management.

 

Objectives:

By the end of this training program, participants will be able to:

  • Understand the basics of big data and its applications in different industries
  • Identify different types of data and their sources
  • Understand the importance of data governance and security
  • Develop skills for managing and processing large datasets
  • Apply different analytical techniques to extract insights from data

 

Target Audience:

This training program is ideal for professionals who work with data or are responsible for data management within their organization. It is also suitable for business analysts, data scientists, IT managers, and executives who want to understand the basics of big data management.

 

Outline:

Day 1:

Introduction to Big Data

  • Definition of big data
  • Characteristics of big data
  • Applications of big data in different industries
  • Challenges of managing big data

 

Day 2:

Types of Data and Data Sources

  • Structured, semi-structured, and unstructured data
  • Internal and external data sources
  • Data quality and data profiling
  • Data integration and data mapping

 

Day 3:

Data Governance and Security

  • Principles of data governance
  • Data ownership and data stewardship
  • Compliance and regulatory requirements
  • Security and privacy considerations

 

Day 4:

Big Data Processing and Management

  • Hadoop and MapReduce
  • Distributed computing and storage
  • Data warehousing and data lakes
  • Big data architecture and infrastructure

 

Day 5:

Big Data Analytics and Insights

  • Introduction to data analytics
  • Data visualization and reporting
  • Predictive analytics and machine learning
  • Big data case studies and best practices
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