Course Code: bigdataconcepts
Duration: 21 hours
Overview:

___ is ___.

This instructor-led, live training (online or onsite) is aimed at beginner-level / intermediate-level / advanced-level ___ who wish to use ___ to ___.

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

  • Install and configure ___.
  • ___. 
  • ___. 
  • ___. 

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Introduction to Big Data
 Definition of Big Data
 Characteristics:
o Volume, Velocity, Variety, Veracity, and Value
 Big Data vs Traditional Data Management
Big Data Use Cases
 Real-world examples
 Benefits and challenges
Big Data Architecture Overview
 Components:
o Data Sources, Ingestion, Storage, Processing, and Analysis
 Overview of Lambda and Kappa architectures
Data Ingestion Techniques
 Batch vs Real-time data ingestion
Big Data Storage Solutions
 Distributed file systems:
o HDFS
 NoSQL Databases:
o Cassandra, HBase, MongoDB
 Data lakes vs Data warehouses
Data Processing in Big Data
 Batch Processing
 Real-time Processing
Introduction to Apache Hadoop & Spark
 Hadoop architecture and components
 Spark architecture, RDD, DataFrames
 Processing data using Apache Spark
Big Data Analytics

 Introduction to Machine Learning in Big Data (MLlib)
Data Security and Governance in Big Data
 Challenges in securing Big Data
 Tools for data security
Big Data Ecosystem: Tools and Technologies
 Emerging trends in Big Data
 Analyzing large datasets using Hive and Spark SQL
Future of Big Data Technologies