Databases

1. New Trends in Databases
   1. Big Data Technologies
      1. Hadoop Ecosystem
         1. Hadoop Distributed File System (HDFS)
         2. MapReduce Programming Model
         3. Hadoop YARN (Yet Another Resource Negotiator)
         4. Apache Hive (Data Warehousing)
         5. Apache Pig (Data Processing)
         6. HBase (Hadoop Database)
         7. Apache Mahout (Machine Learning Library)
      2. Apache Spark
         1. Resilient Distributed Datasets (RDDs)
         2. Spark SQL
         3. Spark Streaming
         4. MLlib (Machine Learning Library)
         5. GraphX (Graph Processing)
         6. Performance and Scalability Features
   2. Artificial Intelligence and Machine Learning Applications
      1. Integration with Databases
         1. Automated Data Cleaning and Preparation
         2. Enhanced Query Processing
         3. Predictive Analytics
         4. Machine Learning Model Storage and Management
         5. Natural Language Processing for Query Interfaces
      2. AI-Driven Database Automation
         1. Self-Tuning and Optimization
         2. Anomaly Detection and Alert Systems
         3. Automated Backup and Recovery Processes
   3. Real-Time Databases
      1. Definition and Importance
      2. Features and Requirements
         1. Low-Latency Transactions
         2. High-Throughput Processing
      3. Examples and Use Cases
         1. Financial Transactions
         2. IoT Data Streams
         3. Real-Time Analytics and Monitoring
      4. Technologies and Tools
         1. Apache Kafka
         2. Amazon Kinesis
         3. Google Cloud Bigtable
   4. Blockchain Databases
      1. Concepts and Differences from Traditional Databases
         1. Decentralization
         2. Consistency Models in Blockchain
         3. Immutability and Append-Only Ledger
         4. Smart Contracts and Automated Transactions
      2. Use Cases and Applications
         1. Supply Chain Management
         2. Digital Identity Verification
         3. Decentralized Finance (DeFi)
      3. Challenges and Considerations
         1. Scalability Issues
         2. Regulatory and Compliance Concerns
         3. Integration with Existing Systems
   5. Multi-Model Databases
      1. Definition and Benefits
      2. Support for Different Data Models
         1. Relational and Document Model
         2. Graph and Key-Value Model
         3. Flexibility in Querying and Indexing
      3. Use Cases
         1. Hybrid Applications
         2. Unified Data Management
   6. Cloud-Native and Serverless Databases
      1. Characteristics and Advantages
         1. Auto-Scaling and High Availability
         2. Pay-as-You-Go Pricing Model
         3. Minimal Operational Overhead
      2. Popular Platforms
         1. Amazon Aurora
         2. Google Cloud Firestore
         3. Azure Cosmos DB
      3. Storage and Compute Separation Techniques
   7. Data Lakes and Lakehouses
      1. Definition and Architecture
      2. Differences from Traditional Data Warehouses
      3. Technologies
         1. Apache Delta Lake
         2. Google Cloud Storage
         3. AWS Lake Formation
      4. Management and Governance
         1. Metadata Management
         2. Data Lineage and Cataloging
   8. Edge Computing and Database Integration
      1. Definition and Benefits of Edge Databases
      2. Distributed Database Processing at the Edge
      3. Key Applications
         1. Real-Time IoT Applications
         2. Autonomous Vehicle Data Processing
      4. Challenges in Edge Database Management
         1. Data Consistency and Synchronization
         2. Security and Privacy Concerns