Professional Training · Data & Analytics Track
Building Streaming Data Analytics on AWS
A one-day deep dive into streaming analytics on AWS using Amazon Kinesis and Amazon MSK. It covers where streaming services sit within a modern data architecture, then moves through designing and implementing a real-time solution: ingestion, transformation and storage options, stream and cluster sizing, sharding and partitioning, securing streaming data, monitoring workloads and managing cost.
Authorised Instructor
Hui Liang
Chief Digital Officer
- Data Engineer – Associate
- Security – Specialty
- Generative AI Developer – Professional
- Solutions Architect – Professional
- +5 more
This class is delivered by our AWS Authorised Instructor for this specialisation. Trainers confirmed on enrolment.
Who Should Attend
- Data Engineers: Engineers building real-time ingestion and processing pipelines.
- Data Architects: Architects designing streaming platforms on AWS.
- Developers: Developers building applications that consume live data streams.
Prerequisites
This is an intermediate course. To get the most from it, you should have:
At least one year of data analytics experience, or direct experience building real-time or streaming solutions.
Completion of Architecting on AWS or Data Analytics Fundamentals, or equivalent AWS knowledge.
Completion of Building Data Lakes on AWS, or equivalent experience of data lake design.
Learning Objectives
In this course, you will learn to:
Place streaming in context: Understand how AWS streaming services fit a modern data architecture.
Design a streaming solution: Build and implement real-time analytics on AWS.
Optimise throughput: Apply compression, sharding and partitioning techniques.
Choose the right scale: Select streams, clusters, topics and scaling approaches.
Secure streaming data: Protect data at rest and in transit.
Monitor and control cost: Remediate workload problems and apply cost management practices.
Certification
A MY Code Campus certificate of completion is awarded for Building Streaming Data Analytics on AWS.
In-person in Kuala Lumpur or live virtual. Team & enterprise rates available.
