Ultimate Guide to Data Streaming with AWS Kinesis

Learn to build event driven systems and insights based on real-time analytics

Real-time streaming technologies are growing in popularity among the many technological drivers of business innovation because users are increasingly demanding personalized experiences which adapt and respond to them based on their journey through digital products and services. The AWS Kinesis suite of stream persistence and processing services have come to be recognized as first class choice for achieving the kinds of event driven architectures feeding into real-time analytics.

What you’ll learn

  • Collect, Process and, Analyze Data in Real-Time with AWS Kinesis.
  • Real-Time Data Analytics.
  • Kinesis Data Streams.
  • Kinesis Data Firehose.
  • Kinesis Data Analytics for SQL.
  • Kinesis Data Analytics for Apache Flink.
  • Pub/Sub Message Processing.
  • Harness the Power of Kinesis Stream Processing in both Java and Python.

Course Content

  • Introduction –> 5 lectures • 9min.
  • Kinesis Data Streams –> 31 lectures • 4hr 58min.
  • Kinesis Data Firehose –> 6 lectures • 41min.
  • Kinesis Data Analytics –> 13 lectures • 1hr 20min.

Ultimate Guide to Data Streaming with AWS Kinesis

Requirements

  • Basic understanding of AWS Cloud (AWS CLI, S3, CloudWatch, IAM).
  • How to program in either Java or Python.

Real-time streaming technologies are growing in popularity among the many technological drivers of business innovation because users are increasingly demanding personalized experiences which adapt and respond to them based on their journey through digital products and services. The AWS Kinesis suite of stream persistence and processing services have come to be recognized as first class choice for achieving the kinds of event driven architectures feeding into real-time analytics.

 

In this course students learn to harness the power of Kinesis Data Streams (KDS) and Kinesis Data Firehose (KDF) to construct high-throughput, low latency, pipelines of data across a variety of architectural components leading to scalable and loosely coupled systems. Additional focus is placed on how these stream persistence technologies are used in conjunction with Kinesis Data Analytics to perform advanced, real-time, computations which drive informed business actions and insights.

 

The course goes beyond the theory of what these services are, making heavy use of demonstrations and code walkthroughs to give examples of how these technologies are used in practice. Most code examples are demonstrated in parallel using both the Python and Java programming languages in an effort to reach the largest audience of developers. However, some examples are presented only in one language in cases where either one language doesn’t support a particular functionality or is significantly less complex to demonstrate.