• Overview of Big Data concepts
• Challenges of traditional data processing
• Big Data characteristics (5Vs)
• Industry use cases
• Big Data ecosystem overview
• Career opportunities
• Challenges of traditional data processing
• Big Data characteristics (5Vs)
• Industry use cases
• Big Data ecosystem overview
• Career opportunities
• What is Hadoop
• Hadoop architecture
• HDFS overview
• YARN overview
• MapReduce basics
• Hadoop distributions
• Hadoop architecture
• HDFS overview
• YARN overview
• MapReduce basics
• Hadoop distributions
• HDFS architecture
• NameNode and DataNode roles
• Block storage and replication
• HDFS read/write operations
• HDFS commands
• Fault tolerance mechanisms
• NameNode and DataNode roles
• Block storage and replication
• HDFS read/write operations
• HDFS commands
• Fault tolerance mechanisms
• Mapper and Reducer concepts
• Job lifecycle
• Shuffle and sort phase
• Writing basic MapReduce programs
• MapReduce limitations
• Performance tuning basics
• Job lifecycle
• Shuffle and sort phase
• Writing basic MapReduce programs
• MapReduce limitations
• Performance tuning basics
• Hive overview
• Pig overview
• HBase basics
• Sqoop overview
• Flume overview
• Oozie overview
• Pig overview
• HBase basics
• Sqoop overview
• Flume overview
• Oozie overview
• What is Apache Spark
• Spark architecture
• Spark vs Hadoop MapReduce
• Spark components overview
• Spark execution model
• Spark use cases
• Spark architecture
• Spark vs Hadoop MapReduce
• Spark components overview
• Spark execution model
• Spark use cases
• Resilient Distributed Datasets (RDDs)
• Transformations and actions
• Lazy evaluation
• Spark execution flow
• Caching and persistence
• Performance optimization basics
• Transformations and actions
• Lazy evaluation
• Spark execution flow
• Caching and persistence
• Performance optimization basics
• DataFrame concepts
• Creating DataFrames
• Data manipulation techniques
• SQL queries in Spark
• Dataset API overview
• Optimization techniques
• Creating DataFrames
• Data manipulation techniques
• SQL queries in Spark
• Dataset API overview
• Optimization techniques
• Spark SQL architecture
• Working with structured data
• Temporary views
• Hive integration
• Query optimization
• Real-world SQL use cases
• Working with structured data
• Temporary views
• Hive integration
• Query optimization
• Real-world SQL use cases
• Introduction to stream processing
• Spark Structured Streaming
• Micro-batch processing
• Real-time data pipelines
• Window operations
• Streaming use cases
• Spark Structured Streaming
• Micro-batch processing
• Real-time data pipelines
• Window operations
• Streaming use cases
• Machine learning basics in Spark
• Classification algorithms
• Regression algorithms
• Clustering techniques
• Model evaluation
• ML pipeline creation
• Classification algorithms
• Regression algorithms
• Clustering techniques
• Model evaluation
• ML pipeline creation
• Ingesting data from HDFS
• Integrating Spark with Hive
• Reading from relational databases
• Kafka integration basics
• Data pipeline design
• Batch vs real-time ingestion
• Integrating Spark with Hive
• Reading from relational databases
• Kafka integration basics
• Data pipeline design
• Batch vs real-time ingestion
• Partitioning strategies
• Memory management
• Spark configuration tuning
• Handling large datasets
• Performance monitoring
• Best practices
• Memory management
• Spark configuration tuning
• Handling large datasets
• Performance monitoring
• Best practices
• Spark standalone cluster
• Spark on YARN
• Resource allocation
• Monitoring jobs
• Handling failures
• Deployment best practices
• Spark on YARN
• Resource allocation
• Monitoring jobs
• Handling failures
• Deployment best practices
