AWS Streaming Data Analytics Training Course

 AWS Streaming Data Analytics Training Course 

Introduction 

AWS Streaming Data Analytics Training Course provides comprehensive practical knowledge and technical expertise in real-time data processing, streaming analytics, cloud computing, and big data engineering using Amazon Web Services (AWS). The course explores modern data streaming architectures, real-time data ingestion, event-driven processing, data transformation, and distributed analytics using services such as Amazon Kinesis Data Streams, Amazon Kinesis Data Firehose, Amazon Managed Service for Apache Flink, Amazon MSK, AWS Lambda, and Amazon S3. Participants will develop the skills required to design scalable, secure, highly available, and cost-effective streaming data pipelines that support real-time business intelligence, operational analytics, predictive modelling, and data-driven decision-making. 

The training emphasizes hands-on implementation of cloud-native streaming solutions, real-time dashboards, event processing, machine learning integration, and automated data workflows. Participants will explore advanced streaming data architecture, stream processing optimization, fault tolerance, data governance, monitoring, and performance management through practical exercises and international case studies. Drawing on global applications across financial services, telecommunications, healthcare, retail, logistics, and smart cities, the course equips professionals with industry-relevant capabilities to implement enterprise-grade AWS streaming analytics solutions and support digital transformation initiatives. 

Course Objectives 

1. Understand AWS streaming analytics architecture and real-time data engineering principles.
2. Develop scalable data ingestion pipelines using Amazon Kinesis.
3. Implement real-time stream processing using Apache Flink.
4. Configure event-driven architectures using AWS Lambda.
5. Integrate streaming data with Amazon S3 and AWS analytics services.
6. Apply real-time data transformation and enrichment techniques.
7. Design fault-tolerant and highly available streaming pipelines.
8. Implement data security, encryption, and access management.
9. Develop real-time dashboards and business intelligence solutions.
10. Optimize streaming performance, scalability, and processing latency.
11. Apply machine learning techniques to streaming data analytics.
12. Implement monitoring and observability using Amazon CloudWatch.
13. Evaluate cost optimization and enterprise streaming data governance strategies.

Organizational Benefits
 

1. Improved real-time business intelligence and decision-making.
2. Faster processing of high-volume organizational data.
3. Enhanced operational efficiency and automation.
4. Reduced data processing latency and infrastructure costs.
5. Improved customer experience through real-time insights.
6. Strengthened data security and regulatory compliance.
7. Scalable enterprise data engineering capabilities.
8. Enhanced predictive analytics and fraud detection.
9. Improved integration across organizational information systems.
10. Increased competitiveness through data-driven innovation.

Target Audiences
 

1. Data engineers and big data professionals.
2. Cloud architects and AWS solutions architects.
3. Business intelligence and analytics specialists.
4. Software developers and application engineers.
5. Database administrators and data architects.
6. Machine learning and artificial intelligence engineers.
7. IT managers and cloud infrastructure specialists.
8. Data scientists and technology consultants.

Course Duration: 5 days

Course Modules

Module 1: Introduction to AWS Streaming Data Analytics
 

• Fundamentals of real-time analytics and streaming data architecture.
• Batch processing versus stream processing.
• AWS streaming ecosystem and service integration.
• Distributed data processing and event-driven architectures.
• Streaming applications in financial services and telecommunications.
• Case study: Real-time transaction monitoring in global banking.

Module 2: Real-Time Data Ingestion with Amazon Kinesis
 

• Introduction to Amazon Kinesis Data Streams.
• Configuring producers, consumers, shards, and partition keys.
• Implementing real-time data ingestion pipelines.
• Data buffering, retention, and throughput management.
• Integrating Kinesis with AWS Lambda and Amazon S3.
• Case study: Real-time customer activity tracking in global e-commerce.

Module 3: Stream Processing with Amazon Managed Service for Apache Flink
 

• Introduction to Apache Flink and stream processing.
• Implementing real-time transformations and aggregations.
• Event-time processing, windows, and watermarks.
• Managing stateful computations and fault tolerance.
• Developing continuous streaming analytics applications.
• Case study: Real-time fraud detection in international financial institutions.

Module 4: Event-Driven Data Processing and AWS Lambda
 

• Designing serverless streaming architectures.
• Configuring AWS Lambda event source mappings.
• Implementing automated event processing workflows.
• Data filtering, enrichment, and transformation.
• Managing retries, error handling, and dead-letter queues.
• Case study: Real-time order processing in global retail operations.

Module 5: Streaming Data Storage and Integration
 

• Integrating streaming pipelines with Amazon S3.
• Configuring Amazon Kinesis Data Firehose.
• Connecting streaming systems with Amazon Redshift.
• Implementing data lakes and analytical storage architectures.
• Managing data formats, partitioning, and compression.
• Case study: Real-time logistics data integration for international supply chains.

Module 6: Real-Time Visualization and Business Intelligence
 

• Developing streaming analytics dashboards.
• Integrating Amazon QuickSight with analytical datasets.
• Implementing real-time performance indicators.
• Building operational monitoring and reporting solutions.
• Applying predictive analytics to streaming datasets.
• Case study: Real-time patient monitoring and healthcare analytics.

Module 7: Security, Monitoring, and Performance Optimization
 

• Implementing AWS Identity and Access Management policies.
• Applying encryption and secure data transmission.
• Monitoring streaming applications using Amazon CloudWatch.
• Optimizing throughput, latency, and processing capacity.
• Implementing fault tolerance and cost management strategies.
• Case study: Securing large-scale streaming infrastructure in telecommunications.

Module 8: Enterprise Streaming Architecture and Capstone Project
 

• Designing scalable enterprise streaming architectures.
• Integrating Amazon MSK and Apache Kafka.
• Implementing streaming data governance and quality controls.
• Developing end-to-end real-time analytics pipelines.
• Evaluating deployment, scalability, and operational readiness.
• Case study: Smart city traffic analytics using real-time transportation data.

Training Methodology
 

• Instructor-led presentations and interactive technical discussions.
• Practical AWS cloud laboratory exercises and demonstrations.
• Hands-on streaming pipeline development and configuration.
• International industry case studies and group assignments.
• Real-time analytics simulations and capstone project presentations.
• Knowledge assessments, technical evaluations, and feedback sessions.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.
 
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
 
c. Course duration is flexible and the contents can be modified to fit any number of days.
 
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
 
e. One-year post-training support Consultation and Coaching provided after the course.
 
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.