Real-Time Data Processing Architectures
( 30 Modules )

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

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Real-Time Data Processing Architectures ( 30 Modules )

Real-Time Data Processing Architectures
( 30 Modules )