Data Science and Big Data

1. Big Data
   1. Definition and Characteristics
      1. Volume
         1. Large scale of data generation
         2. Data size measurement in petabytes and exabytes
         3. Growth challenges
      2. Velocity
         1. Speed of data in motion
         2. Streaming data and real-time processing
         3. Challenges in processing speed
      3. Variety
         1. Structured vs. unstructured data
         2. Sources of diverse data types
            1. Text
            2. Images
            3. Videos
            4. Sensor data
      4. Veracity
         1. Trustworthiness and accuracy of data
         2. Sources of noise and error
         3. Data quality management
      5. Value
         1. Extracting meaningful insights
         2. Improving business processes
         3. Innovation and competitive advantages
   2. Storage Solutions
      1. Distributed File Systems
         1. Hadoop Distributed File System (HDFS)
            1. Architecture and components
            2. NameNode and DataNode roles
            3. Block storage advantages
         2. Amazon S3
            1. Scalability and durability
            2. Integration with other AWS services
            3. Cost-effective storage options
      2. Data Lakes
         1. Concept and purpose
         2. Scalability and flexibility
         3. Contents of raw, semi-structured, and structured data
      3. Data Warehouses
         1. Traditional vs. modern architectures
         2. ETL (Extract, Transform, Load) processes
         3. Data querying and analysis efficiency
   3. Processing Frameworks
      1. Hadoop Ecosystem
         1. MapReduce
            1. Processing large-scale data sets
            2. Batch processing model
            3. Fault-tolerant data loops
         2. Apache Hive
            1. SQL-like query languages for big data
            2. Optimization techniques
            3. Integration with Hadoop and Spark
         3. Apache Pig
            1. Data flow scripting language
            2. Handling complex data transformations
            3. Batch processing optimization
      2. Apache Spark
         1. In-memory processing capabilities
         2. Speed and efficiency over Hadoop
         3. Spark components (Spark SQL, Spark Streaming, MLlib)
         4. Use cases and applications
      3. Real-Time Processing
         1. Streaming analytics
         2. Tools and technologies (e.g., Apache Kafka, Flink)
         3. Industry applications (e.g., fraud detection, IoT monitoring)
   4. NoSQL Databases
      1. MongoDB
         1. Flexible document schema
         2. Use cases in web applications
         3. Sharding and replication mechanisms
      2. Cassandra
         1. High availability and reliability
         2. Suitable for large-scale deployments
         3. Support for structured and unstructured data
      3. Couchbase
         1. Multi-model database capabilities
         2. Real-time analytics and data sync
         3. Integrations and scalability features
   5. Cloud Platforms
      1. Amazon Web Services (AWS)
         1. Elastic MapReduce (EMR) for big data processing
         2. S3, Redshift, and other data services
         3. AI and ML integration options
      2. Microsoft Azure
         1. Azure HDInsight for Hadoop and Spark
         2. Azure Data Lake for storage and analytics
         3. Integration with Power BI for visualization
      3. Google Cloud Platform (GCP)
         1. BigQuery for fast SQL queries on large datasets
         2. Dataflow for stream and batch data processing
         3. Machine learning with TensorFlow on GCP
   6. Future Trends
      1. Edge Computing
         1. Decentralized processing near data sources
         2. Reducing latency and bandwidth usage
         3. Applications in IoT and smart devices
      2. Real-time Data Processing
         1. Advances in stream processing technologies
         2. Real-time analytics and insights
         3. Facilitation of immediate business decisions
      3. Data-as-a-Service (DaaS)
         1. Business models centered on data monetization
         2. Cloud-based data offerings
         3. Customizable data solutions for enterprises