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This modules provides an overview of the big data, understanding Big data ecosystem, setting up the environment like cloudera vm setup, GCP Cluster Fixes and Cluster Setup on Google Cloud.

This module explores the concepts of 4V's ​Volume, Variety, Velocity and Veracity and concepts lo HDFS and Hadoop commands and overview of the Yarn ecosystem.

This module provides Sqoop introduction, Managing Target Directories, Working with Parquet file format, Working with Avro File Format, Working with Different Compressions, Conditional Imports, Split-by and Boundary Queries, Field delimeters, Incremental Appends, Sqoop-Hive Cluster Fix, Sqoop Hive Import and Sqoop List Tables/Database

This module explores Hadoop Distributed File System (HDFS), HDFS Architecture and Components and Case Study Analyzing Uber Datasets using Hadoop Framework

This module provides a knowledge about Distributed Processing MapReduce Framework, Distributed Processing in MapReduce, Case Study Flipkart Dodged WannaCry, Ransomwar, MapReduce Terminologies, Map Execution Phases, MapReduce Jobs, Building a MapReduce Program and finally Creating a New Project

This module presents the idea of Hive SQL Over Hadoop Map reduce, Hive Case study, Hive Architecture, Hive Meta Store, Hive DDL and DML, Hive Data types, File Format Types, Hive Data Serialization, Hive Optimization Partitioning Bucketing Skewing, Hive Analytics UDF and UDAF, Assisted Practice Working with Hive Quer Editor and conepts of Apache Pig and Components of Pig.

This module explains the topics of NoSQL, HBase Overview, HBase Architecture, HBase Data Model, Connecting to HBase and Assisted Practice Data Upload from HDFS to HBase

This model presents the concepts of data ingestion into Big data using an ETL, Data Ingestion Overview, Apache Kafka, Kafka Data Model, Apache Kafka Architecture, Apache Flume, Apache Flume Model and Components in Flume’s Architecture.

This model presents the python concepts like Modes of Python, Applications of Python, Variables in Python, Operators in Python, Control Statements in Python, Loop Statements in Python, List Operations, Swap Two Strings , Merge Two Dictionaries, Python Functions, Object-Oriented Programming in Python, Access Modifiers, Object - Oriented Programming Concepts and Modules in Python.

This module covers the topics like types of Big data, Challenges is in Traditional Data Solution, Data Processing in Big Data, Distributed Computing and Its Challenges, MapReduce, Apache Storm and Its Limitations and General Purpose Solution Apache Spark.

This module explains the Spark Components, Spark Architecture, Spark Cluster in Real World, Intoduction to PySpark Shell, Submitting PySpark Job, Spark Web UI and  Deployment of PySpark Job.

This module covers Spark SQL Spark SQL Architecture, Spark - Context, User - defined Functions, User - defined Aggregate, Functions, Apache Spark DataFrames, Spark DataFrames – Catalyst Optimizer, Interoperating with RDDs, PySpark DataFrames, Spark - Hive Integration, Create DataFrame Using PySpark to Process Records and UDF with DataFrame.

This module presents Traditional Computing Methods and Its Drawbacks, Spark Streaming Introduction, Real Time Processing of Big Data, Data Processing Architectures, Spark Streaming, Introduction to DStreams, Checkpointing, State Operations, Windowing Operation, Spark Streaming Source and Apache Spark Streaming

This module provides the knowledge of the Spark Structured Streaming, Batch vs Streaming, Structured Streaming Architecture, Use Case Banking, Transactions, Structured Streaming APIs, Usecase Spark Structured Streaming and Working with Spark Strutured Application.

This module presents the idea about the Graphs, Use Cases of GraphX, Spark GraphX, GraphX Operators, Graph Parallel System, Algorithms in Spark, Pregel API and Graph Frames