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| Section | Weight | Objectives |
|---|---|---|
| ETL/ELT with Hadoop and Spark | 18% | - ETL vs ELT concepts and scheduling tools - Hadoop ecosystem, HDFS and ingestion tools - Apache Spark architecture and processing |
| Data Warehousing with SQL and NoSQL | 17% | - Relational databases, schemas and normalization - NoSQL database types, features and use cases - SQL querying and performance considerations |
| Building Data Pipelines with Python | 20% | - Python libraries for data processing - Pipeline monitoring and error handling - Designing and automating data pipelines |
| The Role of the Data Engineer | 5% | - Role in analytics and data science projects - Skills and responsibilities of a data engineer |
| Processing Streaming and IoT Data | 20% | - Streaming data concepts and IoT use cases - Apache Kafka architecture and messaging - Apache Storm and real-time processing |
| Data Governance, Security and Privacy | 20% | - Big data security: Apache Atlas, Ranger, Knox - Governance frameworks, roles and policies - Data privacy regulations and ethics - Metadata management and MDM |
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