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| Section | Objectives |
|---|---|
| Topic 1: Data Mining and Machine Learning | - Classification and clustering algorithms - Feature engineering and model evaluation - Association rule mining |
| Topic 2: Distributed Storage and Computing | - HDFS architecture and principles - MapReduce execution model - Resource management and cluster scheduling |
| Topic 3: Operation and Maintenance | - Performance optimization - Cluster monitoring and troubleshooting |
| Topic 4: Data Collection and Integration | - Kafka messaging and streaming ingestion - ETL processes and data pipelines |
| Topic 5: Big Data Security and Governance | - Data quality management - Data security mechanisms and access control - Compliance and data lifecycle management |
| Topic 6: Data Processing Frameworks | - Hive data warehouse and SQL processing - Real-time computing frameworks (Flink/Storm concepts) - Spark architecture and execution |
| Topic 7: Big Data Fundamentals | - Big data concepts and ecosystem overview - Data characteristics and processing models |
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