Module #1 Introduction to Real-Time Data Processing Overview of real-time data processing, its importance, and applications
Module #2 Characteristics of Real-Time Data Understanding the unique characteristics of real-time data, including speed, volume, and variability
Module #3 Real-Time Data Processing Architectures Introduction to different real-time data processing architectures, including streaming, micro-batching, and event-driven
Module #4 Streaming Architectures In-depth look at streaming architectures, including Apache Kafka, Apache Flink, and Apache Storm
Module #5 Micro-Batching Architectures In-depth look at micro-batching architectures, including Apache Spark, Apache Hadoop, and Apache Flume
Module #6 Event-Driven Architectures In-depth look at event-driven architectures, including AWS Lambda, Azure Functions, and Google Cloud Functions
Module #7 Data Ingestion and Integration Techniques for ingesting and integrating real-time data from various sources, including APIs, IoT devices, and social media
Module #8 Data Processing and Transformation Techniques for processing and transforming real-time data, including aggregation, filtering, and enrichment
Module #9 Data Storage and Retrieval Options for storing and retrieving real-time data, including NoSQL databases, time-series databases, and data warehouses
Module #10 Analytics and Visualization Techniques for analyzing and visualizing real-time data, including streaming analytics, machine learning, and data visualization tools
Module #11 Real-Time Data Quality and Governance Importance of data quality and governance in real-time data processing, including data validation, data cleansing, and data auditing
Module #12 Security and Compliance Security and compliance considerations for real-time data processing, including data encryption, access control, and regulatory compliance
Module #13 Scalability and Performance Designing real-time data processing architectures for scalability and performance, including horizontal scaling, load balancing, and caching
Module #14 Fault Tolerance and Resilience Designing real-time data processing architectures for fault tolerance and resilience, including error handling, retries, and duplicated data
Module #15 Real-Time Data Processing in the Cloud Overview of real-time data processing in the cloud, including cloud-native services and managed services
Module #16 Real-Time Data Processing Use Cases Real-world use cases for real-time data processing, including IoT, finance, healthcare, and retail
Module #17 Designing Real-Time Data Processing Pipelines Hands-on exercise designing real-time data processing pipelines using various architectures and tools
Module #18 Best Practices and Future Directions Best practices for real-time data processing and future directions in the field, including emerging trends and technologies
Module #19 Real-Time Data Processing with Apache Kafka In-depth look at using Apache Kafka for real-time data processing, including Kafka streams, Kafka Connect, and Kafka architecture
Module #20 Real-Time Data Processing with Apache Flink In-depth look at using Apache Flink for real-time data processing, including Flink architecture, Flink APIs, and Flink use cases
Module #21 Real-Time Data Processing with Apache Spark In-depth look at using Apache Spark for real-time data processing, including Spark Streaming, Spark Structured Streaming, and Spark SQL
Module #22 Real-Time Data Processing with AWS Services In-depth look at using AWS services for real-time data processing, including AWS Kinesis, AWS Lambda, and Amazon S3
Module #23 Real-Time Data Processing with Azure Services In-depth look at using Azure services for real-time data processing, including Azure Event Hubs, Azure Stream Analytics, and Azure Functions
Module #24 Real-Time Data Processing with Google Cloud Services In-depth look at using Google Cloud services for real-time data processing, including Google Cloud Pub/Sub, Google Cloud Dataflow, and Google Cloud Functions
Module #25 Case Study:Real-Time Data Processing in Finance Real-world case study of real-time data processing in finance, including trading platforms, risk management, and compliance
Module #26 Case Study:Real-Time Data Processing in IoT Real-world case study of real-time data processing in IoT, including sensor data processing, device management, and analytics
Module #27 Case Study:Real-Time Data Processing in Healthcare Real-world case study of real-time data processing in healthcare, including patient monitoring, medical imaging, and clinical decision support
Module #28 Real-Time Data Processing Project Hands-on project implementing real-time data processing pipelines using various architectures and tools
Module #29 Real-Time Data Processing in Edge Computing Overview of real-time data processing in edge computing, including edge computing architectures, edge AI, and edge analytics
Module #30 Course Wrap-Up & Conclusion Planning next steps in Real-Time Data Processing Architectures career
