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Building Streaming Data Analytics Solutions on AWS

Last Updated: 08-03-2025

The Building Streaming Data Analytics Solutions on AWS course is designed for professionals who want to learn how to build and manage scalable, real-time data analytics solutions using AWS services. This hands-on course will guide you through the key AWS tools and architectures for handling streaming data, including Amazon Kinesis, AWS Lambda, and Amazon Redshift. You’ll gain practical experience in building data pipelines, processing real-time streams, and performing analytics on large-scale data streams to generate actionable insights.

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Course Prerequisites

  • Basic understanding of AWS services like Amazon EC2, S3, and IAM.
  • Familiarity with cloud computing concepts, data processing, and analytics.
  • Recommended: Experience with programming languages like Python or Java, and knowledge of SQL for querying data.

Learning Objectives

By the end of this course, you will be able to:

  1. Design and implement a real-time data streaming architecture using Amazon Kinesis and AWS Lambda.
  2. Set up and manage Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics for stream processing.
  3. Integrate Kinesis with AWS services like S3, Redshift, and Elasticsearch for analytics and storage.
  4. Process and analyze streaming data in real-time to enable quick decision-making.
  5. Build scalable and fault-tolerant systems for continuous data ingestion and analytics.
  6. Use AWS Lambda to process data streams and trigger real-time actions based on incoming data.
  7. Optimize cost and performance for streaming analytics solutions.
  8. Implement best practices for security, data governance, and monitoring in streaming analytics environments.

Target Audience

This course is ideal for:

  • Data engineers, architects, and developers responsible for implementing real-time data processing and analytics solutions.
  • IT professionals and system architects looking to leverage AWS services for streaming data analytics.
  • Machine learning practitioners and data scientists interested in integrating real-time data streams with analytics.
  • Cloud developers and engineers seeking hands-on experience with AWS streaming tools like Kinesis and Lambda.

Course Modules

  • Introduction to Modern Data Architectures:

    • Understanding the features and benefits of data warehouses, data lakes, and modern data architectures.
    • Exploring how AWS streaming services fit into these architectures.
  • Designing Streaming Data Analytics Solutions:

    • Architecting solutions using AWS services such as Amazon Kinesis and Amazon Managed Streaming for Apache Kafka (Amazon MSK).
    • Integrating streaming data solutions with AWS services like AWS Glue and AWS Lambda.
  • Optimizing Data Storage:

    • Applying techniques such as compression, sharding, and partitioning to optimize data storage.
  • Data Ingestion, Transformation, and Storage:

    • Selecting and deploying appropriate options for ingesting, transforming, and storing streaming data.
  • Security, Performance, and Cost Management:

    • Implementing best practices for security, performance optimization, and cost management in streaming data solutions.

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