Implementation_Big_Data_Presentation.pptx
- 1. Implementation • A Comprehensive Overview with a Focus on Big Data
- 2. Introduction • • What is 'Implementation'? • • Why implementation matters in tech projects • • Focus of this presentation: Big Data implementation
- 3. Objectives • • Understand 'implementation' in Big Data • • Explore process, tools, and architecture • • Real-world case studies • • Challenges and best practices
- 4. What is Big Data? • • Definition: Massive volume of structured & unstructured data • • Key sources: Social Media, IoT, Banking, Healthcare
- 5. Need for Big Data Implementation • • Manage high-volume, high-velocity data • • Derive insights for decisions • • Enable real-time processing and automation
- 6. Phases in Big Data Implementation • 1. Planning and Requirement Analysis • 2. Infrastructure Setup • 3. Data Ingestion • 4. Storage & Processing • 5. Analytics & Reporting • 6. Deployment • 7. Maintenance
- 7. Big Data Architecture • • Source Systems → Ingestion Layer → Storage → Processing → Visualization • • Tools used in each layer
- 8. Data Ingestion Tools • • Apache Kafka • • Apache NiFi • • Apache Flume • • Apache Sqoop
- 9. Storage Technologies • • Hadoop HDFS • • MongoDB • • Cassandra • • Amazon S3
- 10. Processing Engines • • Batch: Hadoop MapReduce • • Real-time: Apache Spark, Flink • • Interactive: Presto, Druid
- 11. Big Data Implementation on Cloud • • AWS: EMR, S3, Glue • • Google Cloud: BigQuery, Dataflow • • Azure: HDInsight, Data Lake
- 12. Security in Implementation • • Data encryption • • Role-based access control • • Audit logging and compliance
- 13. Challenges in Implementation • • Infrastructure cost • • Data quality issues • • Skill gaps • • Legacy system integration
- 14. Best Practices • • Begin with a clear business use case • • Use scalable tools • • Ensure governance • • Adopt agile methodology
- 15. Case Study – Netflix • • Personalized recommendations • • Real-time tracking • • Tools: Spark, Kafka, AWS
- 16. Case Study – Healthcare • • Patient analytics via wearable devices • • Predictive alerts • • Tools: Hadoop, Python, Tableau
- 17. Future of Big Data Implementation • • AI & Machine Learning • • Edge Computing • • Federated Data Processing
- 18. Summary • • Big Data implementation: challenging but rewarding • • Requires tools, planning, execution • • Evolving with trends
- 19. Q&A • Thank you! • Open to questions.