Big Data is a term used to describe vast and complicated collections of data
- 1. Big Data and Hadoop Management Information System Group F Summer 2025
- 2. Introduction • Big Data refers to extremely large and complex datasets. • Characterized by 5 V's: Volume, Velocity, Variety, Veracity, and Value. • Requires advanced tools like Hadoop for processing and analysis. • UAE uses Big Data to drive digital innovation and smart governance.
- 3. Big Data: Concepts and Characteristics • Volume: Massive data generation from multiple sources. • Velocity: Real-time data streams require fast processing. • Variety: Data comes in many formats (text, image, video, etc.). • Veracity and Value: Data quality and usefulness are critical.
- 4. Big Data Analytics • Uses ML and statistical models to analyze large datasets. • Includes descriptive, diagnostic, predictive, and prescriptive analytics. • Applied in sectors like healthcare, finance, and urban planning. • Visualizations help decision-makers interpret complex data.
- 5. Applications of Big Data • Healthcare: Patient monitoring, genomics, predictive risk scoring. • Education: Personalized learning and dropout prediction. • Commerce: Personalized marketing, inventory optimization. • UAE Example: Malaffi unifies healthcare data across Abu Dhabi.
- 6. Big Data in the UAE • UAE AI Strategy 2031 drives national Big Data adoption. • Smart Dubai and Abu Dhabi Digital Strategy use AI-powered platforms. • Dubai Pulse and Bayanat platforms support data sharing and analytics. • Digital Twin projects improve resource and infrastructure planning.
- 7. Benefits of Big Data • Improves decision-making and operational efficiency. • Enables real-time insights and organizational agility. • Supports personalized services (e.g., health, education). • Drives innovation and predictive maintenance in industries.
- 8. Challenges of Big Data • Lack of skilled professionals in data engineering and science. • High infrastructure demands and data integration complexity. • Data quality issues and compliance with privacy laws. • Security vulnerabilities due to scale and complexity.
- 9. Introduction to Hadoop • Open-source framework for distributed storage and processing. • Core components: HDFS (storage) and MapReduce (processing). • Developed by Apache based on Google’s architecture. • Supports massive scalability and fault-tolerance.
- 10. Key Features of Hadoop • Distributed, fault-tolerant storage (HDFS). • Scalable to thousands of nodes. • Data locality improves efficiency by moving computation to data. • Open-source and cost-effective using commodity hardware.
- 11. Hadoop Architecture • Composed of HDFS, YARN, and MapReduce. • NameNode manages metadata; DataNodes store blocks. • YARN handles resource scheduling and job management. • MapReduce splits jobs into parallel map and reduce tasks.
- 12. Setting Up a Hadoop Cluster • Involves configuring nodes with Hadoop and Java. • Core files (e.g., core-site.xml) define paths and replication. • Services like NameNode and ResourceManager must be launched. • Cloud platforms simplify setup via managed services (e.g., EMR).
- 13. Hadoop Ecosystem Components • Hive: SQL-like query engine. • Pig: Scripting platform for data transformation. • HBase: NoSQL database for real-time access. • Others: Flume, Sqoop, Oozie, ZooKeeper.
- 14. Apache Spark’s Role • In-memory processing engine that complements Hadoop. • Supports batch, streaming, machine learning, and graph processing. • Much faster than MapReduce for iterative tasks. • APIs available in Java, Scala, Python, and R.
- 15. Data Analytics & Visualization with Hadoop • Tools like Hive and Spark SQL enable large-scale analysis. • Dashboards (e.g., Tableau, SAS) help visualize insights. • Real-time processing possible via Spark Streaming, Flink, Kafka. • Visualization democratizes data across the organization.
- 16. Hadoop on the Cloud • Elastic scaling and cost-efficiency via pay-as-you-go models. • Managed services reduce setup and maintenance complexity. • Integration with cloud AI/ML and data services. • Challenges: Latency, operational complexity, vendor lock-in.
- 17. UAE Case Studies • Malaffi: Health data integration platform in Abu Dhabi. • Dubai Pulse: Centralized data sharing and analytics hub. • TAMM: AI-driven personalized government services in Abu Dhabi. • Digital Twin: Infrastructure simulation and planning.
- 18. Conclusion • Big Data and Hadoop enable scalable, data-driven solutions. • UAE is a regional leader in implementing these technologies. • Smart governance and AI strategies rely on Big Data foundations. • Future innovation will depend on integrating cloud, AI, and analytics.