Big Data Tools and Technologies
( 24 Modules )

Module #1
Introduction to Big Data
Overview of big data, its características, and importance in modern businesses

Module #2
Hadoop Fundamentals
Introduction to Hadoop, its ecosystem, and core components (HDFS, MapReduce, YARN)

Module #3
HDFS (Hadoop Distributed File System)
In-depth exploration of HDFS, its architecture, and data storage strategies

Module #4
MapReduce Programming
Programming with MapReduce, including writing mappers, reducers, and drivers

Module #5
YARN (Yet Another Resource Negotiator)
Understanding YARN, its components, and resource management in Hadoop

Module #6
Hadoop Ecosystem Tools
Introduction to Hadoop ecosystem tools such as Hive, Pig, and Sqoop

Module #7
Hive (Hadoop SQL)
Using Hive for data querying, data warehousing, and SQL-like operations

Module #8
Pig (Programming Language)
Using Pig for data processing, data flows, and procedural scripting

Module #9
Sqoop (SQL to Hadoop)
Using Sqoop for data ingestion, data transfer, and data integration

Module #10
NoSQL Databases
Introduction to NoSQL databases, their types (key-value, document, graph), and use cases

Module #11
HBase (Distributed NoSQL Database)
Using HBase for distributed, scalable, and fault-tolerant data storage

Module #12
Cassandra (Distributed NoSQL Database)
Using Cassandra for distributed, highly available, and scalable data storage

Module #13
MongoDB (Document-Oriented NoSQL Database)
Using MongoDB for flexible, document-based data storage and querying

Module #14
Spark Fundamentals
Introduction to Apache Spark, its ecosystem, and core components (Spark Core, Spark SQL, MLlib)

Module #15
Spark Programming
Programming with Spark, including writing Spark applications, RDDs, and DataFrames

Module #16
Spark SQL and DataFrames
Using Spark SQL and DataFrames for data querying, data transformation, and data analysis

Module #17
Spark MLlib (Machine Learning Library)
Using Spark MLlib for machine learning, data mining, and predictive analytics

Module #18
Real-time Data Processing with Spark Streaming
Using Spark Streaming for real-time data processing, event-time processing, and stream analytics

Module #19
Kafka (Distributed Streaming Platform)
Using Kafka for distributed streaming, event-driven architecture, and real-time data pipelines

Module #20
Kafka Architecture and Components
In-depth exploration of Kafka architecture, brokers, topics, partitions, and consumers

Module #21
Data Ingestion with Flume and NiFi
Using Flume and NiFi for data ingestion, data integration, and data flow management

Module #22
Big Data Security and Governance
Importance of big data security, governance, and compliance, including data encryption, access control, and auditing

Module #23
Big Data Use Cases and Industry Applications
Exploring big data use cases and industry applications, including IoT, healthcare, finance, and retail

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Big Data Tools and Technologies career

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Big Data Tools and Technologies ( 24 Modules )

Big Data Tools and Technologies
( 24 Modules )