Schema Case Studies and Data Analytics Lifecycle

Schema Case Studies and Data Analytics Lifecycle

1. Successful companies in the use of big data

   • Amazon
     • Well-known e-commerce platform
     • Personalized Recommendations
     • Inventory and Logistics Management
     • Dynamic Pricing
     • Improving User Experience
     • Continuous Innovation and Identification of Market Trends
   • The Marriott hotels
     • Personalization of the customer experience
     • Operational efficiency
     • Targeted Marketing
     • Inventory management
   • Netflix
     • Personalized Recommendations
     • Original Content Production
     • User Experience Optimization
     • Strategic Decisions
   • Uber Eats
     • Demand forecasting
     • Dynamic pricing
     • Personalized recommendations
     • Route optimization
     • Satisfaction analysis
   • McDonald’s
     • Smart Pricing
     • Personalized Menus
     • Inventory Optimization
     • Customer Experience
   • Bavaria
     • Product Innovation
     • Process Optimization
     • Targeted Marketing
     • Sustainability

2. Review the Big Data architecture for data processing

   • Data Sources: Collect data from IoT devices, social networks, applications, databases
   • Collection and storage: Data is collected and stored in distributed systems
   • Collection and storage: Data is collected and stored in distributed systems or NoSQL databases
     • - Hadoop Distributed File System (HDFS)
     • MongoDB
     • Cassandra
   • Data processing: Tools are used to process data in real time or in batches. This allows for complex analysis and the extraction of useful information.
     • Apache Spark
     • MapReduce
   • Analysis and visualization: Processed data is analyzed using advanced techniques and presented in dashboards or graphs to facilitate decision-making.
   • Security and privacy: Data protection is achieved through encryption, authentication, and other security measures.

3. Implications of Big Data architecture for data processing

   • It allows you to manage large volumes of information efficiently and strategically
   • Main implications
     • Scalability
     • Real-time data processing
     • Integration of diverse data
     • Resource optimization
     • Security and privacy
     • Cost reduction

4. Main definitions of Big Data architecture for data processing

   • Set of technologies and design patterns that enable the capture, storage, processing and analysis of large volumes of data
     • Distributed Systems
     • Data Ingestion
     • Batch and Stream Processing
     • Data Storage
     • Analytics and Machine Learning
     • Security and Governance