AI and Data in Business
Real-Time Streaming Analytics with Apache Kafka
Turn Real-Time Streaming Analytics with Apache Kafka into controlled practice by examining Kafka topics partitions and schemas, security reliability and observability, and completing a practical streaming solution lab.
Overview
Practical learning for workplace transfer.
Effective Real-Time Streaming Analytics with Apache Kafka requires technical choices to survive operational scrutiny. The course moves from event streaming foundations through security reliability and observability, using peer review and scenario work to produce a feasible next-step plan.
Prerequisites
Relevant experience with Real-Time Streaming Analytics with Apache Kafka is useful; technical depth is adapted to the cohort.
Objectives
- Frame event streaming foundations for a defensible business decision.
- Diagnose Kafka topics partitions and schemas against technical and operational evidence.
- Select and justify an approach to stream processing and state under realistic constraints.
- Establish ownership, controls, and measures for security reliability and observability.
- Deliver the streaming solution lab output and defend it in a stakeholder review.
Target audience
- AI, data, analytics, and digital leaders
- Data scientists, engineers, architects, and product teams
- Transformation, innovation, and business-analysis professionals
- Risk and operational owners of AI-enabled services responsible for Real-Time Streaming Analytics with Apache Kafka
Program outline
A clear structure for the learning journey.
Program outline
Outline points are grouped in one designed block instead of being treated as separate module cards.
Module 1: event streaming foundations
Establish acceptance criteria and evidence requirements before approving event streaming foundations.
Peer-review the proposed event streaming foundations approach for unintended effects and operational fit.
Resolve the scenario through a documented recommendation and escalation path.
Module 2: Kafka topics partitions and schemas
Establish acceptance criteria and evidence requirements before approving Kafka topics partitions and schemas.
Peer-review the proposed Kafka topics partitions and schemas approach for unintended effects and operational fit.
Resolve the scenario through a documented recommendation and escalation path.
Module 3: stream processing and state
Establish acceptance criteria and evidence requirements before approving stream processing and state.
Peer-review the proposed stream processing and state approach for unintended effects and operational fit.
Resolve the scenario through a documented recommendation and escalation path.
Module 4: security reliability and observability
Establish acceptance criteria and evidence requirements before approving security reliability and observability.
Peer-review the proposed security reliability and observability approach for unintended effects and operational fit.
Resolve the scenario through a documented recommendation and escalation path.
Module 5: streaming solution lab
Establish acceptance criteria and evidence requirements before approving streaming solution lab.
Peer-review the proposed streaming solution lab approach for unintended effects and operational fit.
Resolve the scenario through a documented recommendation and escalation path.
Materials provided
- Course workbook and specialist reference guide
- Applied case pack and decision worksheets
- Implementation checklist and action-plan canvas
- 4D Certificate of Completion
Training Options
Programs can be delivered in-house, online, or in a blended format depending on your team's schedule, location, and learning objectives. When an external certificate or exam is included, certification rules and fees remain under the relevant awarding body's policies, while 4D provides the training and preparation support.
Why choose 4D
The Real-Time Streaming Analytics with Apache Kafka cases are adapted to the client sector and conclude with a reviewable output, without claiming certification.
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