Big Data Analytics Tutorial

Big Data Analytics focuses on analyzing massive and complex datasets to discover meaningful insights. It helps organizations make faster, smarter and data-driven decisions using scalable technologies.

• Handles large, fast and diverse data
• Uses distributed systems for processing
• Powers business intelligence and machine learning

Foundations of Big Data Analytics

This section introduces the core principles that form the base of Big Data systems and analytics. It explains how large-scale data is stored, processed and analyzed using distributed technologies.

• What is Big Data
• Types of Big Data
• 6V's of Big Data
• Introduction to Big Data Analytics
• Types of Big Data Analytics
• Traditional Data vs. Big Data
• Big Data Examples in Real Life
• Big Data vs. Data Science

Big Data Architecture

This section explains the structural components required to handle Big Data efficiently. It covers how data flows from collection to storage, processing, analysis and visualization.

• Data Ingestion
• Batch Processing and Stream Processing
• Data Lakes
• Data Warehouses
• Data Processing
• Data Analysis
• Big Data Visualization

Big Data Technologies

This section introduces the major tools and technologies used for storing, processing and analyzing Big Data.

• Storage Technologies:HDFS, HBase, MongoDB, Cassandra
• Processing Frameworks:Hadoop, Spark, Flink
• Data Ingestion Tools:Kafka, Sqoop
• Coordination Tool: Zookeeper
• Query & Analytics Tools:Hive, Pig, Presto

Distributed Computing Concepts

This section explains the core principles behind distributed systems that power Big Data platforms.

• Introduction
• Parallel data processing
• Fault Tolerance in Distributed System
• Replication in Distributed System

Hadoop Ecosystem

The Apache Hadoop ecosystem provides distributed storage and processing for Big Data. It solves scalability issues by dividing data across multiple machines and processing it in parallel.

• Introduction
• Hadoop Architecture
• Map Reduce in Hadoop
• MapReduce Architecture
• Mapper In Map Reduce
• Reducer in Map Reduce
• Hadoop 2.x vs Hadoop 3.x
• Ecosystem

Hive & Apache Pig

Apache Hive and Apache Pig simplify Big Data processing on Hadoop. They provide higher-level abstractions over MapReduce for easier querying and data transformation.

• Introduction to Apache Hive
• Database Options in Hive
• Features And Limitations in Hive
• HQL Database Creation & Drop Database in Hive.
• Introduction to Apache Pig
• MapReduce vs. Pig
• Pig vs. Hive

Machine Learning for Big Data Analytics

This section explains how machine learning is applied to large-scale datasets using distributed frameworks.

• Big Data ML Frameworks: Spark MLlib, TensorFlow / PyTorch
• Scalable ML Algorithms:Linear regression, Logistic Regression, Random Forest, K-Means Clustering
• Model Evaluation & Tuning:Cross-validation, Hyperparameter tuning

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