Big Data Processing & Analytics: Improving data insight.pdf
- 1. Big Data Processing & Analytics Group 12 Distributed Data Storage and Management Big Data Processing Frameworks Distributed Data Processing Paradigms Data Ingestion and Integration Distributed Machine Learning and AI Data Analytics and Visualization Scalable Data Processing Patterns Big Data Security and Privacy
- 2. Volume - Massive data scale (e.g., Facebook processes 4PB daily). Velocity - High-speed generation & processing (e.g., TikTok, Twitter). Variety - Structured (databases) & unstructured (videos, social media). What is Big Data? Defined by the 3Vs: Why Traditional Databases Fail: Single servers lack storage & processing power. Centralized systems create bottlenecks. Concurrent access is limited. Distributed Data Storage & Management
- 3. Key Principles of Distributed Storage: Horizontal Scaling - Add nodes instead of upgrading one server. Replication - Data copied across nodes for fault tolerance. Parallel Processing - Multiple nodes process data simultaneously. Data Ingestion Tools like Apache Kafka collect data. Splitting Data is divided into blocks (e.g., 128MB chunks in HDFS). Replication Copies are stored on multiple nodes. Querying Tools like Apache Hive retrieve data. Hadoop HDFS Stores data in blocks across multiple nodes. NoSQL Databases Handles unstructured data. (e.g., Cassandra, MongoDB) Cloud Object Storage Scalable and globally accessible. (e.g., Amazon S3) Big Data Processing Flow Technologies Used
- 4. Challenges, Trade-offs & Real-World Applications Complexity:Requiresclustermanagementexpertise. CAPTheoremTrade-offs:ConsistencyvsAvailabilityvsPartitionTolerance. Cost:Replicationincreasesstorageneeds. Security:Encryptingdistributeddataischallenging. ChallengesinDistributedStorage: Netflix:UsesAWSS3&Hadoopforvideostreamingstorage. CERN:Processes1PB/dayofparticlecollisiondata. Vodafone:UsesCassandraforreal-timecustomerdata. Real-WorldApplications:
- 6. Popular Big Data Frameworks & Use Cases Batch Processing: Processes data in batches, often used for offline processing. Stream Processing: Processes data in real-time, often used for online processing. Interactive Processing: Provides interactive querying and analysis capabilities. Framework Components Data Storage Resource Manager Task Executor Job Scheduler Processing Models
- 7. Popular Big Data Frameworks & Use Cases Apache Hadoop Ecosystem: Includes Hadoop, Hive, Pig, HBase, and others. Apache Spark Ecosystem: Includes Spark, Spark SQL, Spark Streaming, and others. NoSQL Databases: Includes MongoDB, Cassandra, Redis, and others. Use Cases: Data Integration: Integrating data from multiple sources. Data Analytics: Machine learning, data mining and pattern recognition. Real-time Processing: Social media, IoT sensor data. Data Warehousing: Storing and querying large datasets. Frameworks Ecosystem
- 8. Choosing & Implementing a Big Data Framework Start Small & Scale Gradually. Monitor & Optimize Performance. Ensure Security Measures. Stay Updated with Latest Features. Data Size & Complexity: Volume, variety, velocity. Processing Needs: Batch, stream, interactive. Scalability & Performance: Can it grow with data needs? Integration & Usability: Community support & ease of use. Key Considerations Best Practices
- 10. Core Distributed Processing Paradigms MapReduce is a programming model and software framework designed for processing and generating large datasets, making it particularly well-suited for big data. It works by dividing the processing into three main stages: Map, Shuffle, and Reduce Batch Processing (MapReduce) Map: Splits data and processes in parallel. Shuffle: Groups and sorts intermediate results. Reduce: Aggregates and finalizes results. How It Works: Best For: Large-scale data analysis, historical data processing. Limitations: High latency, not suitable for real-time tasks.
- 11. Core Distributed Processing Paradigms Stream processing is a paradigm in big data processing that focuses on analyzing data as it arrives, in real-time or near real-time. It's a fundamentally different approach from batch processing, which processes data in large chunks after it has been collected and stored. Stream Processing (Real-Time Analytics) Unbounded Data: Stream processing deals with data streams that are potentially infinite. There's no defined "end" to the data. Windowing Event vs Processing Time Key Characteristics: Best For: Fraud detection, real-time analysis, alerts, Log Processing. Challenges: Handling late-arriving data, state management, Fault tolerance,
- 12. Core Distributed Processing Paradigms Graph processing is a specialized area of big data processing that focuses on analyzing data represented as a graph. Unlike traditional data processing which often treats data as tables or documents, graph processing emphasizes the relationships between data points. Graph Processing (Network Analysis) Nodes (Vertices): Represent entities or objects (e.g., people, products, locations). Edges (Connections): Represent relationships between nodes (e.g., friendships, purchases, routes). Graph Algorithms: Specialized algorithms analyze graph structures and extract insights. These algorithms leverage the connections between nodes to discover patterns and relationships. Core Concepts: Best For: Social network Analysis, Recommendation systems, fraud detection, Knowledge Discovery, Logistics and Supply chain Optimization. Challenges: Scalability, Algorithm Selection, Distributed Computing.
- 13. Core Distributed Processing Paradigms Iterative processing is a computational paradigm where the same data is processed repeatedly until a specific condition is met. This approach is fundamental to many machine learning algorithms and other data-intensive tasks. Iterative Processing Paradigms Repetitive Computation: The same operations are applied multiple times to refine results. Convergence: The process stops when a satisfactory solution is reached. State Updates: Each iteration updates the system's state based on previous computations. Core Concepts: Importance: Machine Learning, Optimization, Data Analysis, Challenges: Computational Cost, Convergence Issues and Parameter Tuning
- 14. Core Distributed Processing Paradigms Hybrid architectures in big data processing refer to architectural patterns that combine different data processing paradigms, typically batch and stream processing, to leverage the strengths of each. Hybrid Architectures (Lambda & Kappa) Batch Layer: Stores & processes historical data. Speed Layer: Processes real-time updates. Serving Layer: Merges batch & real-time results. Challenge: Maintaining two separate data pipelines. Lambda Architecture: Kappa Architecture: Treats all data as streams (simplifies processing). Challenge: Requires reprocessing historical data.
- 16. KeyTechniques&Challenges BatchProcessing:Collectsandprocessesdatain chunks(e.g.,ETL–Extract,Transform,Load). StreamProcessing:Ingestsreal-timedatacontinuously (e.g.,ApacheKafka,ApacheFlink). DataIngestionTechniques: DataIntegrationTechniques: ETL(Extract,Transform,Load):Traditionalmethodforstructureddata. ELT(Extract,Load,Transform):Moderncloud-basedapproach. DataVirtualization:Real-timeaccesswithoutmovingdata. API-basedIntegration:UsesAPIsforreal-timedataexchange. ManualDataIntegration:Manuallywritinglinesofcodetoperform integrationtechniques Handling large, heterogeneous data sources. Ensuring data quality, consistency, and security. Balancing real-time and batchdata needs. Challenges:
- 19. Key Distributed Learning Paradigms & Architectures Parameter Server Model: The central server manages model updates. All-Reduce & Ring-Reduce: Efficient aggregation of updates across nodes. Federated Learning: Models train on local devices, only sharing updates (e.g., mobile AI). The model is replicated across nodes; each node processes different data. Learning Paradigms: Architectures: Data Parallelism Large models are split across multiple machines. Model Parallelism Different model stages are processed in sequence across nodes. Pipeline Parallelism
- 20. Tools,Challenges&FutureTrends TensorFlowDistributed:Scalabletrainingwithtf.distributeAPI. PyTorchDistributed:Supportsdata&modelparallelism. Horovod(byUber):Optimizedforlarge-scaledeeplearning. PopularTools&Frameworks: FutureTrends: EdgeAI&5GIntegration–TrainingAIclosertodata sources. EnhancedPrivacy–Securefederatedlearning& differentialprivacy. HybridModels–Combiningdata,model,and pipelineparallelism. Edge AI & 5G Integration – Training AI closer to data sources. Enhanced Privacy – Secure federated learning & differential privacy. Hybrid Models – Combining data, model, and pipeline parallelism. Challenges:
- 25. Real-World Applications & Best Practices Industries Using Big Data Analytics: Healthcare: Covid-19 patient data analysis. Finance: Fraud detection, risk analysis. Entertainment: Netflix & Spotify recommendation engines. Business: Optimizing operations for profitability
- 30. Big Data Security & Privacy Big Data Security and Privacy are paramount concerns due to the large-scale collection and processing of sensitive information. Ensuring data protection and compliance with regulations is crucial for maintaining trust and preventing breaches. Big Data Risks Large-scaledatacollectionincreases exposuretobreaches. Sensitiveinformation(e.g.,financial,health, biometricdata)mustbeprotected.
- 31. Anonymization & Pseudonymization: Removes or replaces identifiable information. Differential Privacy: Introduces noise to datasets to prevent individual identification. Homomorphic Encryption: Enables computation on encrypted data without decryption. Secure Multi-Party Computation (SMC): Allows analysis without sharing raw data. Privacy Concerns Unchecked data collection may lead to unauthorized use. Cross-referencing datasets can re-identify individuals. Security Challenges: Distributed data storage increases attack surfaces. Compliance with global regulations (GDPR, CCPA) is essential. Key Privacy & Security Techniques
- 32. Data Encryption: Protects data at rest and in transit. Access Controls & Authentication: Restricts unauthorized access (e.g., role-based access). Intrusion Detection Systems: Identifies threats in real-time. Data Governance: Implements policies for compliance and auditing. Regulatory Frameworks: Enforce data access policies and retention limits. Maintain audit logs for compliance and security tracking. Regularly assess vulnerabilities and update security measures. Best Security Practices GDPR (Europe): Requires explicit consent and data access rights. CCPA (California): Grants consumers control over their data. HIPAA (Healthcare, USA): Ensures patient data confidentiality. Compliance & Governance in Big Data Data Governance Strategies:
- 33. ThankYou Group12 Group 12 Big Data Processing and Analytics 21/02/2025