Skip to main content
Level Up; Mastering Data Streaming Architecture

Level Up; Mastering Data Streaming Architecture

$199.00
When you get access:
Course access is prepared after purchase and delivered via email
How you learn:
Self-paced • Lifetime updates
Your guarantee:
30-day money-back guarantee — no questions asked
Who trusts this:
Trusted by professionals in 160+ countries
Toolkit Included:
Includes a practical, ready-to-use toolkit with implementation templates, worksheets, checklists, and decision-support materials so you can apply what you learn immediately - no additional setup required.
Adding to cart… The item has been added

Level Up: Mastering Data Streaming Architecture

Level Up: Mastering Data Streaming Architecture

Transform your career and become a sought-after expert in the rapidly growing field of data streaming! This comprehensive course, Level Up: Mastering Data Streaming Architecture, is meticulously designed to take you from foundational concepts to advanced techniques, empowering you to design, build, and maintain robust, scalable, and real-time data streaming solutions. Gain the skills demanded by top companies and position yourself for success in the data-driven era.

This course features interactive exercises, hands-on projects, real-world case studies, and access to a vibrant community of fellow learners. Learn from expert instructors with years of experience in building and deploying cutting-edge data streaming systems. Enjoy a flexible learning environment accessible from any device, allowing you to learn at your own pace.

Upon successful completion of the course, you will receive a prestigious CERTIFICATE issued by The Art of Service, validating your expertise in Data Streaming Architecture!



Course Curriculum: Your Path to Data Streaming Mastery

Module 1: Foundations of Data Streaming

  • Introduction to Data Streaming: Understanding the landscape and evolution of data streaming.
  • Real-time vs. Batch Processing: Exploring the differences, trade-offs, and use cases for each.
  • Key Concepts and Terminology: Familiarizing yourself with essential data streaming terms (e.g., latency, throughput, fault tolerance).
  • The Data Streaming Ecosystem: An overview of the major players and technologies involved in data streaming.
  • Understanding Data Velocity, Volume, and Variety (The 3 V's): Applying these concepts to data streaming.
  • Common Use Cases for Data Streaming: Exploring practical applications across various industries (e.g., finance, e-commerce, IoT).
  • Building Blocks of a Data Streaming Architecture: Source, ingestion, processing, storage, and consumption.
  • Introduction to Message Queues and Brokers: Understanding the role of queues and brokers in decoupling systems.
  • Hands-on Exercise: Setting up a simple data pipeline with basic logging.
  • Quiz: Testing your understanding of fundamental data streaming concepts.

Module 2: Message Brokers and Queues (Kafka Deep Dive)

  • Introduction to Apache Kafka: History, architecture, and core concepts of Kafka.
  • Kafka Architecture: Brokers, Topics, Partitions, and Consumers: Understanding the key components of a Kafka cluster.
  • Kafka Producers and Consumers: Developing applications to produce and consume data from Kafka topics.
  • Kafka Connect: Building data pipelines to connect Kafka to external systems.
  • Kafka Streams: Developing real-time stream processing applications with Kafka Streams.
  • Kafka Cluster Setup and Configuration: Setting up a local Kafka cluster (Docker-based).
  • Scaling and Performance Tuning Kafka: Optimizing Kafka for high throughput and low latency.
  • Kafka Security: Authentication, Authorization, and Encryption: Securing your Kafka cluster.
  • Kafka Monitoring and Management: Utilizing tools to monitor Kafka health and performance.
  • Hands-on Project: Building a real-time data pipeline using Kafka to ingest and process sensor data.
  • Quiz: Assessing your knowledge of Kafka architecture and usage.

Module 3: Alternatives to Kafka (RabbitMQ, Pulsar, AWS Kinesis)

  • Introduction to RabbitMQ: Understanding RabbitMQ's features and use cases.
  • RabbitMQ vs. Kafka: Comparing and contrasting their strengths and weaknesses.
  • Introduction to Apache Pulsar: Exploring Pulsar's unique features and architectural design.
  • Pulsar vs. Kafka: Analyzing their differences in terms of performance, scalability, and features.
  • AWS Kinesis Data Streams: Leveraging AWS Kinesis for real-time data streaming in the cloud.
  • Choosing the Right Message Broker: Evaluating factors such as scalability, latency, and cost.
  • Hands-on Exercise: Implementing a simple message queue using RabbitMQ.
  • Comparative Analysis: Deep dive into the pros and cons of each technology.
  • Considerations for Deployment and Maintenance: Understanding the operational aspects of each message broker.
  • Real-world Examples: Exploring use cases where each message broker excels.
  • Quiz: Testing your comprehension of various message brokers.

Module 4: Stream Processing Frameworks (Apache Flink)

  • Introduction to Apache Flink: Understanding Flink's architecture and capabilities.
  • Flink's Distributed Architecture: JobManager, TaskManagers, and state management.
  • Flink DataStream API: Building real-time data processing pipelines with the DataStream API.
  • Flink Table API and SQL: Utilizing SQL-like syntax for stream processing.
  • Windowing Techniques in Flink: Time-based and count-based windowing for data aggregation.
  • State Management in Flink: Maintaining stateful computations for complex stream processing.
  • Fault Tolerance in Flink: Ensuring data consistency and reliability in the face of failures.
  • Deploying Flink Applications: Deploying Flink applications to a cluster or cloud environment.
  • Flink Performance Tuning and Optimization: Optimizing Flink applications for high throughput and low latency.
  • Hands-on Project: Building a real-time fraud detection system using Flink.
  • Quiz: Verifying your understanding of Apache Flink and its application.

Module 5: Stream Processing Frameworks (Spark Streaming and Structured Streaming)

  • Introduction to Apache Spark Streaming: Understanding Spark Streaming's micro-batch architecture.
  • Spark Streaming Transformations: Applying transformations to DStreams for real-time data processing.
  • Introduction to Spark Structured Streaming: A unified API for batch and stream processing.
  • Structured Streaming Concepts: Triggers, Watermarks, and State Management: Mastering the key concepts.
  • Real-time ETL with Spark Structured Streaming: Building pipelines for data transformation and loading.
  • Spark Streaming vs. Flink: Comparing and contrasting their strengths and weaknesses.
  • Hands-on Exercise: Building a simple real-time analytics application with Spark Streaming.
  • Advanced Topics: Exploring advanced features of Spark Streaming and Structured Streaming.
  • Best Practices: Following best practices for developing and deploying Spark Streaming applications.
  • Real-world Scenarios: Examining practical examples of Spark Streaming in action.
  • Quiz: Confirming your knowledge of Spark Streaming and Structured Streaming.

Module 6: Data Serialization and Deserialization

  • Introduction to Data Serialization: Understanding the importance of efficient data serialization.
  • Common Serialization Formats: JSON, Avro, Protocol Buffers: Exploring the pros and cons of each format.
  • Choosing the Right Serialization Format: Evaluating factors such as performance, schema evolution, and compatibility.
  • Avro: Schema Evolution and Compatibility: Leveraging Avro for schema management in evolving systems.
  • Protocol Buffers: Defining and Using Protocol Buffers: Defining and using Protocol Buffers for efficient data serialization.
  • Implementing Serialization and Deserialization in Java, Python, and Scala: Hands-on implementation examples.
  • Performance Comparison of Serialization Formats: Benchmarking the performance of different serialization formats.
  • Hands-on Project: Implementing a data pipeline that uses Avro for data serialization.
  • Integration with Data Streaming Frameworks: Utilizing serialization formats with Kafka, Flink, and Spark.
  • Addressing Common Issues: Troubleshooting common serialization and deserialization problems.
  • Quiz: Testing your understanding of data serialization concepts and formats.

Module 7: Data Ingestion and Integration

  • Data Ingestion Strategies: Push vs. Pull architectures for data ingestion.
  • Change Data Capture (CDC): Capturing and streaming changes from databases.
  • CDC Tools and Techniques: Debezium, Maxwell, and Kafka Connect CDC: Implementing CDC with various tools.
  • Integrating with External Data Sources: Connecting to databases, APIs, and other systems.
  • Data Transformation and Enrichment: Transforming and enriching data during ingestion.
  • Data Validation and Quality Checks: Ensuring data quality during ingestion.
  • Handling Different Data Formats and Schemas: Processing data from various sources with different formats.
  • Hands-on Project: Building a data ingestion pipeline using Kafka Connect and Debezium.
  • Real-time Data Warehousing: Building a real-time data warehouse using data streaming technologies.
  • Best Practices for Data Ingestion: Following best practices for building robust and scalable ingestion pipelines.
  • Quiz: Assessing your knowledge of data ingestion strategies and tools.

Module 8: Stream Processing Patterns and Best Practices

  • Common Stream Processing Patterns: Aggregation, filtering, joining, and enrichment.
  • Stateful vs. Stateless Stream Processing: Understanding the differences and use cases.
  • Exactly-Once Processing: Ensuring data consistency in stream processing applications.
  • Lambda Architecture: Combining batch and stream processing for comprehensive data analysis.
  • Kappa Architecture: A simplified architecture for stream processing only.
  • Choosing the Right Architecture: Lambda vs. Kappa: Evaluating the trade-offs between Lambda and Kappa architectures.
  • Handling Late-Arriving Data: Managing data that arrives out of order or with delays.
  • Windowing Techniques: Time-based and count-based windowing for data aggregation.
  • Real-time Anomaly Detection: Detecting anomalies and outliers in real-time data streams.
  • Best Practices for Stream Processing Application Design: Designing robust and scalable stream processing applications.
  • Quiz: Verifying your understanding of stream processing patterns and best practices.

Module 9: Monitoring and Observability

  • Importance of Monitoring and Observability: Understanding the need for monitoring in data streaming systems.
  • Key Metrics to Monitor: Latency, throughput, error rate, and resource utilization.
  • Monitoring Tools and Technologies: Prometheus, Grafana, and ELK Stack: Utilizing monitoring tools for data streaming applications.
  • Setting Up Alerts and Notifications: Configuring alerts for critical events and performance issues.
  • Log Aggregation and Analysis: Collecting and analyzing logs for troubleshooting and performance optimization.
  • Distributed Tracing: Tracing requests across distributed systems to identify bottlenecks.
  • Monitoring Kafka Clusters: Monitoring the health and performance of Kafka clusters.
  • Monitoring Flink and Spark Applications: Monitoring the performance of Flink and Spark stream processing applications.
  • Hands-on Project: Setting up a monitoring dashboard for a data streaming application.
  • Best Practices for Monitoring and Observability: Following best practices for monitoring data streaming systems.
  • Quiz: Testing your knowledge of monitoring and observability concepts.

Module 10: Security in Data Streaming

  • Security Considerations for Data Streaming: Addressing security risks in data streaming systems.
  • Authentication and Authorization: Securing access to data streaming components.
  • Encryption: Encrypting data in transit and at rest.
  • Data Masking and Anonymization: Protecting sensitive data in data streams.
  • Secure Data Ingestion: Ensuring secure data ingestion from external sources.
  • Secure Stream Processing: Protecting data during stream processing operations.
  • Secure Data Storage: Securing data stored in data lakes and data warehouses.
  • Compliance and Regulations: Meeting compliance requirements such as GDPR and HIPAA.
  • Hands-on Exercise: Implementing security measures for a data streaming pipeline.
  • Best Practices for Security in Data Streaming: Following best practices for securing data streaming systems.
  • Quiz: Confirming your comprehension of security principles in data streaming.

Module 11: Cloud-Based Data Streaming

  • Data Streaming in the Cloud: Leveraging cloud platforms for data streaming.
  • AWS Kinesis Data Streams and Data Firehose: Using AWS Kinesis for real-time data streaming and ingestion.
  • Google Cloud Pub/Sub and Dataflow: Utilizing Google Cloud for data streaming and stream processing.
  • Azure Event Hubs and Stream Analytics: Leveraging Azure for data streaming and real-time analytics.
  • Choosing the Right Cloud Platform: Evaluating cloud platforms for data streaming based on requirements.
  • Deploying Data Streaming Applications to the Cloud: Deploying Flink and Spark applications to cloud environments.
  • Scaling and Managing Data Streaming Infrastructure in the Cloud: Scaling data streaming infrastructure in the cloud.
  • Cost Optimization in the Cloud: Optimizing cost for data streaming in the cloud.
  • Hands-on Project: Building a data streaming pipeline using AWS Kinesis.
  • Best Practices for Cloud-Based Data Streaming: Following best practices for data streaming in the cloud.
  • Quiz: Assessing your understanding of cloud-based data streaming services.

Module 12: Data Streaming for IoT

  • Data Streaming for IoT: Applying data streaming to Internet of Things (IoT) applications.
  • IoT Device Data Ingestion: Ingesting data from IoT devices using MQTT, CoAP, and other protocols.
  • Real-time IoT Data Processing: Processing IoT data in real-time for analytics and decision-making.
  • Edge Computing: Processing data at the edge to reduce latency and bandwidth usage.
  • IoT Analytics: Analyzing IoT data to gain insights and improve operations.
  • IoT Security: Securing IoT devices and data.
  • Hands-on Project: Building an IoT data streaming pipeline for sensor data analysis.
  • Real-world IoT Use Cases: Exploring practical applications of data streaming in IoT.
  • Best Practices for Data Streaming in IoT: Following best practices for data streaming in IoT.
  • Challenges and Opportunities: Discussing the challenges and opportunities in the field of IoT data streaming.
  • Quiz: Verifying your knowledge of data streaming applications for IoT.

Module 13: Data Streaming for Real-time Analytics

  • Real-time Analytics: Exploring the concepts and benefits of real-time analytics.
  • Real-time Data Warehousing: Building a real-time data warehouse using data streaming technologies.
  • Real-time Business Intelligence (BI): Visualizing real-time data for business insights.
  • Real-time Machine Learning (ML): Training and deploying machine learning models in real-time.
  • Building Real-time Dashboards: Creating dashboards to monitor real-time data.
  • Integrating with Real-time Visualization Tools: Using tools like Tableau and Grafana for real-time visualization.
  • Hands-on Project: Building a real-time analytics dashboard for website traffic.
  • Real-world Use Cases: Examining practical examples of real-time analytics in action.
  • Best Practices for Real-time Analytics: Following best practices for building real-time analytics systems.
  • Tools for Real-time Analytics: Overview of relevant software and platforms
  • Quiz: Confirming your understanding of real-time analytics principles.

Module 14: Advanced Data Streaming Topics

  • Complex Event Processing (CEP): Detecting patterns and events in real-time data streams.
  • Stream Joins: Joining multiple data streams based on common attributes.
  • Windowing Strategies: Advanced windowing techniques for complex data aggregation.
  • Custom Partitioning: Implementing custom partitioning strategies for optimal performance.
  • Back Pressure Handling: Managing back pressure in data streaming systems.
  • Dynamic Scaling: Scaling data streaming infrastructure dynamically based on load.
  • Schema Registry: Managing schemas for data streaming applications.
  • Multi-Tenancy: Building multi-tenant data streaming platforms.
  • Hands-on Exercise: Implementing complex event processing using Flink CEP.
  • Emerging Trends in Data Streaming: Exploring new technologies and trends in the field.
  • Quiz: Testing your knowledge of advanced data streaming concepts.

Module 15: Data Streaming with Apache Beam

  • Introduction to Apache Beam: Understanding the Beam programming model.
  • Beam Pipelines: Constructing data processing pipelines in Beam.
  • Beam Transforms: Applying transformations to data using Beam.
  • Beam Runners: Executing Beam pipelines on different execution engines (e.g., Flink, Spark).
  • Building Batch and Streaming Pipelines with Beam: Creating unified pipelines for both batch and stream processing.
  • Hands-on Project: Building a data processing pipeline using Apache Beam.
  • Use Cases for Apache Beam: Exploring practical applications of Apache Beam in data processing.
  • Best Practices for Apache Beam: Following best practices for using Apache Beam effectively.
  • Advantages of Using Apache Beam: Unified batch and stream processing, portability, and extensibility.
  • Quiz: Assessing your knowledge of Apache Beam concepts and usage.

Module 16: Capstone Project: Building a Complete Data Streaming Solution

  • Project Overview: Designing and building a complete data streaming solution for a real-world use case.
  • Requirements Gathering: Defining the requirements for the data streaming solution.
  • Architecture Design: Designing the architecture for the data streaming solution.
  • Implementation: Implementing the data streaming solution using the technologies learned in the course.
  • Testing and Validation: Testing and validating the data streaming solution.
  • Deployment: Deploying the data streaming solution to a production environment.
  • Monitoring and Maintenance: Monitoring and maintaining the data streaming solution.
  • Project Presentation: Presenting the data streaming solution to the class.
  • Feedback and Review: Receiving feedback and review on the data streaming solution.
  • Wrap-up and Future Directions: Discussing future directions and opportunities in the field of data streaming.
This course is constantly updated to reflect the latest advancements in data streaming technology. We are committed to providing you with the most current and relevant knowledge to succeed in this exciting field.

Enroll today and take your data skills to the next level! Earn your CERTIFICATE from The Art of Service and demonstrate your mastery of Data Streaming Architecture.

!