15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
This course is ideal for;
- Data Engineers
- Software Developers
- Data Analysts
- System Architects
- DevOps Engineers
- Big Data Professionals
- Anyone needing real-time data processing skills
Session Objectives
- Understand the fundamentals of Apache Kafka Streams and KSQL.
- Develop and deploy real-time data processing applications using Kafka Streams.
- Utilize KSQL for querying and transforming data streams.
- Implement stateful stream processing and windowing techniques.
- Perform real-time aggregations and data enrichment using Kafka Streams.
- Integrate Kafka Streams and KSQL with other data sources and sinks.
- Optimize Kafka Streams applications for performance and scalability.
- Troubleshoot and debug Kafka Streams and KSQL
- Implement fault-tolerant and resilient real-time data pipelines.
- Apply best practices for designing and developing Kafka Streams
- Understand how to monitor and manage Kafka Streams and KSQL
- Explore advanced features of KSQL for complex data transformations.
- Apply real world use cases for Kafka Streams and KSQL
About the Course
As enterprises shift from traditional batch operations to event-driven architectures, processing continuous, high-velocity data streams in real time has become essential for modern decision-making. Our Apache Kafka Streams and KSQL Training Course meets this engineering demand by providing technical teams with a comprehensive, hands-on deep dive into Kafka’s native stream processing capabilities. Guided by distributed systems specialists, you will master the mechanics of stateful stream processing, event time windowing, stream-table joins, and complex aggregations using the Kafka Streams API. Furthermore, you will gain practical experience leveraging KSQL/ksqlDB to query, transform, and manipulate live streams using intuitive SQL abstractions—equipping Data Engineers, Software Developers, and Analytics Engineers to build scalable, low-latency data pipelines and event-driven applications that respond instantaneously to business events.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of stream processing and Kafka Streams.
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Workshop 1.2:
Architecture and components of Kafka Streams.
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Workshop 1.3:
Developing basic Kafka Streams applications.
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Workshop 1.4:
Understanding key concepts like state stores and processors.
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Workshop 1.5:
Setting up development environment.
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Workshop 2.1:
Detailed exploration of the Kafka Streams API.
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Workshop 2.2:
Implementing data transformations and filters.
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Workshop 2.3:
Using key-value stores and state management.
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Workshop 2.4:
Handling different data formats and serialization.
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Workshop 2.5:
Implementing custom processors and transformers.
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Workshop 3.1:
Understanding stateful stream processing.
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Workshop 3.2:
Implementing windowing techniques (tumbling, hopping, sliding).
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Workshop 3.3:
Managing state stores and handling state migrations.
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Workshop 3.4:
Implementing aggregations and joins with state.
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Workshop 3.5:
Advanced state management techniques.
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Workshop 4.1:
Introduction to KSQL and its use cases.
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Workshop 4.2:
Querying and transforming data streams with KSQL.
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Workshop 4.3:
Creating streams and tables in KSQL.
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Workshop 4.4:
Performing data filtering and aggregations.
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Workshop 4.5:
Implementing joins and windowed aggregations.
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Workshop 5.1:
Advanced KSQL functions and operators.
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Workshop 5.2:
Implementing user-defined functions (UDFs) in KSQL.
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Workshop 5.3:
Using KSQL for data enrichment and transformations.
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Workshop 5.4:
Optimizing KSQL queries for performance.
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Workshop 5.5:
Implementing complex stream processing logic with KSQL.
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Workshop 6.1:
Integrating Kafka Streams and KSQL in real-time pipelines.
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Workshop 6.2:
Using KSQL for data preprocessing and transformations.
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Workshop 6.3:
Implementing real-time dashboards and monitoring.
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Workshop 6.4:
Handling data consistency and data quality.
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Workshop 6.5:
Architecting combined systems.
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Workshop 7.1:
Optimizing Kafka Streams applications for performance.
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Workshop 7.2:
Tuning Kafka Streams configurations.
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Workshop 7.3:
Monitoring and troubleshooting performance issues.
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Workshop 7.4:
Implementing efficient data partitioning strategies.
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Workshop 7.5:
Resource management for optimal performance.
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Workshop 8.1:
Implementing fault-tolerant Kafka Streams applications.
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Workshop 8.2:
Handling data recovery and state restoration.
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Workshop 8.3:
Designing resilient real-time data pipelines.
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Workshop 8.4:
Understanding and implementing retry mechanisms.
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Workshop 8.5:
Implementing dead letter queues and error handling.
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Workshop 9.1:
Integrating Kafka Streams with databases and data stores.
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Workshop 9.2:
Connecting to external data sources and sinks.
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Workshop 9.3:
Implementing data ingestion and extraction.
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Workshop 9.4:
Handling data formats and serialization.
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Workshop 9.5:
Advanced connector configurations.
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Workshop 10.1:
Implementing data governance policies with KSQL.
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Workshop 10.2:
Data lineage tracking and data quality management.
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Workshop 10.3:
Securing sensitive data in KSQL applications.
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Workshop 10.4:
Compliance considerations for KSQL deployments.
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Workshop 10.5:
Implementing auditing and reporting.
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Workshop 11.1:
Building real-time analytics dashboards.
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Workshop 11.2:
Implementing real-time alerting and monitoring.
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Workshop 11.3:
Utilizing Kafka Streams for anomaly detection.
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Workshop 11.4:
Implementing real-time decision-making systems.
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Workshop 11.5:
Developing real time scoring systems.
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Workshop 12.1:
Deploying Kafka Streams on cloud platforms.
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Workshop 12.2:
Managing cloud resources for Kafka Streams.
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Workshop 12.3:
Cloud-specific performance tuning.
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Workshop 12.4:
Security considerations for cloud deployments.
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Workshop 12.5:
Cost optimization for cloud based systems.
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Workshop 13.1:
Advanced windowing techniques in KSQL.
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Workshop 13.2:
Complex joins and aggregation in KSQL.
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Workshop 13.3:
Implementing complex data patterns.
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Workshop 13.4:
Advanced schema management.
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Workshop 13.5:
Complex event processing using KSQL.
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Workshop 14.1:
Integrating Kafka Streams with machine learning models.
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Workshop 14.2:
Implementing real-time feature engineering.
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Workshop 14.3:
Deploying machine learning models in Kafka Streams.
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Workshop 14.4:
Real time model scoring.
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Workshop 14.5:
Advanced techniques for streaming machine learning.
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Workshop 15.1:
Emerging trends in Kafka Streams and KSQL.
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Workshop 15.2:
Integrating Kafka Streams with AI and advanced analytics.
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Workshop 15.3:
Advanced techniques for real time data processing.
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Workshop 15.4:
Advanced techniques for large language models within Kafka streams.
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Workshop 15.5:
Future of real time data processing
