15 Topics
10 Days
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
Dive into the world of real-time data processing with our comprehensive Apache Kafka Streams and KSQL Training Course. This program is meticulously designed to equip you with the essential skills to effectively utilize Kafka's powerful tools for real-time data streaming and processing. In today's fast-paced digital landscape, mastering Kafka Streams and KSQL is crucial for building scalable and responsive applications that can handle high-velocity data streams. Our Kafka training course provides hands-on experience and in-depth knowledge, enabling you to extract valuable insights and drive real-time decision-making.
This Kafka Streams training delves into the core concepts of stream processing, covering topics such as stateful stream processing, windowing, and aggregations. You'll gain proficiency in using KSQL for querying and transforming data streams, enabling you to build real-time data pipelines with ease. Whether you're a data engineer, developer, or analyst, this KSQL training will empower you to leverage the full potential of Kafka's powerful tools for real-time data streaming and processing.
Curriculum & Topics
15 Topics | 10 Days
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Subtopic 1.1:
Fundamentals of stream processing and Kafka Streams.
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Subtopic 1.2:
Architecture and components of Kafka Streams.
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Subtopic 1.3:
Developing basic Kafka Streams applications.
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Subtopic 1.4:
Understanding key concepts like state stores and processors.
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Subtopic 1.5:
Setting up development environment.
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Subtopic 2.1:
Detailed exploration of the Kafka Streams API.
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Subtopic 2.2:
Implementing data transformations and filters.
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Subtopic 2.3:
Using key-value stores and state management.
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Subtopic 2.4:
Handling different data formats and serialization.
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Subtopic 2.5:
Implementing custom processors and transformers.
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Subtopic 3.1:
Understanding stateful stream processing.
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Subtopic 3.2:
Implementing windowing techniques (tumbling, hopping, sliding).
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Subtopic 3.3:
Managing state stores and handling state migrations.
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Subtopic 3.4:
Implementing aggregations and joins with state.
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Subtopic 3.5:
Advanced state management techniques.
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Subtopic 4.1:
Introduction to KSQL and its use cases.
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Subtopic 4.2:
Querying and transforming data streams with KSQL.
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Subtopic 4.3:
Creating streams and tables in KSQL.
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Subtopic 4.4:
Performing data filtering and aggregations.
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Subtopic 4.5:
Implementing joins and windowed aggregations.
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Subtopic 5.1:
Advanced KSQL functions and operators.
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Subtopic 5.2:
Implementing user-defined functions (UDFs) in KSQL.
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Subtopic 5.3:
Using KSQL for data enrichment and transformations.
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Subtopic 5.4:
Optimizing KSQL queries for performance.
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Subtopic 5.5:
Implementing complex stream processing logic with KSQL.
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Subtopic 6.1:
Integrating Kafka Streams and KSQL in real-time pipelines.
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Subtopic 6.2:
Using KSQL for data preprocessing and transformations.
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Subtopic 6.3:
Implementing real-time dashboards and monitoring.
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Subtopic 6.4:
Handling data consistency and data quality.
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Subtopic 6.5:
Architecting combined systems.
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Subtopic 7.1:
Optimizing Kafka Streams applications for performance.
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Subtopic 7.2:
Tuning Kafka Streams configurations.
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Subtopic 7.3:
Monitoring and troubleshooting performance issues.
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Subtopic 7.4:
Implementing efficient data partitioning strategies.
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Subtopic 7.5:
Resource management for optimal performance.
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Subtopic 8.1:
Implementing fault-tolerant Kafka Streams applications.
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Subtopic 8.2:
Handling data recovery and state restoration.
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Subtopic 8.3:
Designing resilient real-time data pipelines.
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Subtopic 8.4:
Understanding and implementing retry mechanisms.
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Subtopic 8.5:
Implementing dead letter queues and error handling.
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Subtopic 9.1:
Integrating Kafka Streams with databases and data stores.
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Subtopic 9.2:
Connecting to external data sources and sinks.
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Subtopic 9.3:
Implementing data ingestion and extraction.
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Subtopic 9.4:
Handling data formats and serialization.
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Subtopic 9.5:
Advanced connector configurations.
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Subtopic 10.1:
Implementing data governance policies with KSQL.
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Subtopic 10.2:
Data lineage tracking and data quality management.
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Subtopic 10.3:
Securing sensitive data in KSQL applications.
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Subtopic 10.4:
Compliance considerations for KSQL deployments.
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Subtopic 10.5:
Implementing auditing and reporting.
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Subtopic 11.1:
Building real-time analytics dashboards.
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Subtopic 11.2:
Implementing real-time alerting and monitoring.
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Subtopic 11.3:
Utilizing Kafka Streams for anomaly detection.
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Subtopic 11.4:
Implementing real-time decision-making systems.
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Subtopic 11.5:
Developing real time scoring systems.
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Subtopic 12.1:
Deploying Kafka Streams on cloud platforms.
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Subtopic 12.2:
Managing cloud resources for Kafka Streams.
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Subtopic 12.3:
Cloud-specific performance tuning.
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Subtopic 12.4:
Security considerations for cloud deployments.
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Subtopic 12.5:
Cost optimization for cloud based systems.
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Subtopic 13.1:
Advanced windowing techniques in KSQL.
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Subtopic 13.2:
Complex joins and aggregation in KSQL.
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Subtopic 13.3:
Implementing complex data patterns.
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Subtopic 13.4:
Advanced schema management.
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Subtopic 13.5:
Complex event processing using KSQL.
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Subtopic 14.1:
Integrating Kafka Streams with machine learning models.
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Subtopic 14.2:
Implementing real-time feature engineering.
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Subtopic 14.3:
Deploying machine learning models in Kafka Streams.
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Subtopic 14.4:
Real time model scoring.
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Subtopic 14.5:
Advanced techniques for streaming machine learning.
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Subtopic 15.1:
Emerging trends in Kafka Streams and KSQL.
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Subtopic 15.2:
Integrating Kafka Streams with AI and advanced analytics.
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Subtopic 15.3:
Advanced techniques for real time data processing.
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Subtopic 15.4:
Advanced techniques for large language models within Kafka streams.
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Subtopic 15.5:
Future of real time data processing
