719-298-4047info@force7training.com
Student Login
Force7 Training
FRCAWS-28AWS

Building Streaming Data Analytics Solutions

Duration · 1 dayVirtual + In-PersonInstructor-Led

$650

Course Description

This one-day instructor-led course teaches participants how to design, build, and operate real-time streaming data analytics solutions using modern event-driven architectures and scalable stream processing technologies. Learners gain practical experience ingesting, processing, analyzing, and visualizing streaming data while implementing operational best practices for low-latency analytics environments.

The course focuses on real-time data pipelines, event streaming, stream processing, analytics architectures, monitoring, and operational excellence.

— Be First in Line —

Register Your Interest

We're finalizing the schedule for Building Streaming Data Analytics Solutions. Add your details below and we'll notify you the moment a session opens for registration — no payment or commitment required.

Audience Profile

This course is intended for:

  • Data Engineers
  • Streaming Analytics Engineers
  • Cloud Engineers
  • Software Developers
  • Solutions Architects
  • Analytics Engineers
  • DevOps Engineers
  • IT Professionals working with real-time data systems

Prerequisites

Before enrolling, you should have:

  • Basic understanding of data processing concepts
  • Familiarity with databases and SQL
  • Basic knowledge of cloud computing concepts
  • Experience with Python or scripting is recommended
  • Understanding of distributed systems concepts is helpful

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1Understand streaming analytics architecture patterns
  • 2Design scalable real-time data pipelines
  • 3Ingest and process streaming data
  • 4Build event-driven analytics workflows
  • 5Implement stream processing and transformation logic
  • 6Analyze real-time data streams
  • 7Monitor and troubleshoot streaming systems
  • 8Apply operational and security best practices for streaming analytics

— Day-by-Day —

Course Outline

Module 1

Introduction to Streaming Data Analytics

Topics

  • Overview of streaming analytics
  • Batch vs. streaming processing
  • Event-driven architectures
  • Real-time analytics use cases
  • Streaming pipeline components
  • Data latency and throughput concepts
  • Designing scalable streaming systems
  • Key Concepts Covered
  • Event streams
  • Message brokers
  • Stream processing
  • Real-time analytics
  • Distributed streaming systems

Hands-On Lab

  • Explore streaming analytics architecture components
  • Review real-time analytics workflows

Module 2

Streaming Data Ingestion

Topics

  • Streaming ingestion fundamentals
  • Event producers and consumers
  • Managing high-throughput ingestion
  • Partitioning and scalability concepts
  • Handling streaming data formats
  • Data buffering and batching
  • Fault tolerance and durability
  • Key Technologies Covered
  • Streaming platforms
  • Event ingestion frameworks
  • Message queue architectures

Hands-On Lab

  • Configure a streaming ingestion pipeline
  • Ingest real-time event data into a streaming platform

Module 3

Stream Processing and Transformation

Topics

  • Stream processing fundamentals
  • Stateless vs. stateful processing
  • Windowing and aggregation concepts
  • Event filtering and enrichment
  • Real-time transformation workflows
  • Stream joins and correlation
  • Managing out-of-order events
  • Key Concepts Covered
  • Event windows
  • Real-time aggregations
  • Stateful stream processing
  • Event enrichment
  • Data transformation pipelines

Hands-On Lab

  • Build stream processing workflows
  • Transform and aggregate streaming events

Module 4

Real-Time Analytics and Visualization

Topics

  • Real-time query processing
  • Streaming dashboards and reporting
  • Operational analytics concepts
  • Alerting and anomaly detection
  • Low-latency analytics design
  • Time-series analytics fundamentals
  • Interactive analytics workflows
  • Key Concepts Covered
  • Streaming metrics
  • Dashboard architectures
  • Event-driven alerts
  • Time-series analysis
  • Operational reporting

Hands-On Lab

  • Analyze streaming datasets
  • Create dashboards and real-time analytics views

Module 5

Workflow Automation and Orchestration

Topics

  • Event-driven workflow orchestration
  • Trigger-based processing pipelines
  • Scheduling and automation concepts
  • Managing streaming dependencies
  • Workflow resilience strategies
  • Scaling stream processing workloads
  • Operational automation best practices
  • Key Concepts Covered
  • Event orchestration
  • Workflow automation
  • Scalable streaming pipelines
  • Fault-tolerant processing

Hands-On Lab

  • Configure automated event-driven workflows
  • Implement scalable processing orchestration

Module 6

Monitoring, Security, and Operational Excellence

Topics

  • Monitoring streaming systems
  • Logging and observability
  • Detecting pipeline bottlenecks
  • Troubleshooting streaming workloads
  • Security best practices for streaming platforms
  • Encryption and access controls
  • Cost optimization strategies
  • Key Concepts Covered
  • Streaming observability
  • Operational monitoring
  • Secure event processing
  • Performance tuning
  • Governance considerations

Hands-On Lab

  • Monitor and troubleshoot streaming workflows
  • Configure operational dashboards and alerts

Module 7

End-to-End Streaming Analytics Capstone Exercise

Topics

  • Designing production-ready streaming architectures
  • Integrating ingestion, processing, and analytics
  • Operationalizing real-time analytics pipelines
  • Scaling streaming systems
  • Applying architectural best practices
  • Capstone Hands-On Lab
  • Participants build an end-to-end streaming analytics solution that includes:
  • Streaming data ingestion
  • Event processing and transformation
  • Real-time analytics
  • Dashboard visualization
  • Workflow automation
  • Monitoring and alerting
  • Course Wrap-Up
  • Final Review
  • Review key streaming analytics concepts
  • Operational best practices
  • Common implementation patterns and challenges
  • Skills Validation
  • Practical lab reviews
  • Scenario-based discussions
  • Interactive Q&A
  • Next Steps
  • Advanced stream processing learning paths
  • Real-time data engineering training
  • Production streaming implementation guidance

Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

— Keep Exploring —

Need a different angle?

Browse the full AWS catalog or chat with an advisor about a custom training plan for your team.