Big Data

Module 1: Introduction to Big Data

• What is Big Data?
• Characteristics of big data (volume, velocity, variety, veracity)
• Challenges posed by big data

• Big Data Analytics:
• Benefits of big data analytics
• Use cases and applications

• Big Data Technologies:
• Overview of popular big data technologies (Hadoop, Spark, NoSQL databases)

Module 2: Hadoop Ecosystem

• Hadoop Distributed File System (HDFS):
• Understanding HDFS
• Components of HDFS (NameNode, DataNode)
• Reading and writing data to HDFS

• MapReduce:
• MapReduce programming model
• Writing MapReduce jobs
• Understanding the MapReduce execution process

• YARN:
• Introduction to YARN
• Resource Management in YARN
• Submitting applications to YARN

Module 3: Apache Spark

• Introduction to Spark:
• Spark architecture and components
• Advantages of Spark over MapReduce

• Spark Core:
• RDDs (Resilient Distributed Datasets)
• Transformations and actions
• Spark SQL

• Spark Streaming:
• Processing real-time data streams
• DStreams and Receivers

• Spark MLlib:
• Machine learning algorithms in Spark
• Building and training models

Module 4: NoSQL Databases

• Introduction to NoSQL:
• Differences between NoSQL and relational databases
• Types of NoSQL databases (document, key-value, graph, wide-column)

• MongoDB:
• MongoDB architecture and features
• Creating and querying collections
• Indexing and aggregation

• Cassandra:
• Cassandra architecture and features
• Data modeling and querying
• Distributed data management

Module 5: Big Data Tools and Frameworks

• Apache Hive:
• SQL-like interface for querying big data
• Creating and managing tables
• HiveQL syntax

• Apache Pig:
• Pig Latin scripting language
• Data analysis and transformation

• Apache Kafka:
• Distributed streaming platform
• Producing and consuming messages
• Building real-time data pipelines

Module 6: Big Data Analytics Use Cases

• • Social Media Analytics:
• o Analyzing social media data for insights

• Customer Analytics:
• Understanding customer behavior and preferences

• Fraud Detection:
• Detecting fraudulent activities using big data

• Predictive Analytics:
• Making predictions based on historical data

Module 7: Big Data Cloud Platforms

• • Azure HDInsight:
• o Overview of Azure HDInsight
• o Deploying Hadoop clusters on Azure

Module 9: Big Data projects

• Hands-on Projects:
• Creating and running pipelines
• Building Big Data flows
• Integrating with various data sources and sinks
• Implementing ETL and ELT patterns
• Troubleshooting and optimizing Big Data workflows

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