In this first chapter, you will learn about the differences between batch and stream processing, create your first stream, manage its permissions, write to it and read from it.

What is streaming and why does it matter?

Batch vs stream

Producers and destinations

Managing Firehose delivery streams

Getting ready for the first stream

Creating roles

IAM users vs. roles

Permissions practice

S3 bucket creation

Working with the Firehose delivery stream

Create your first Firehose stream

Writing to a Firehose stream

Reading Firehose data

Streaming in the cloud

The next step in your streaming data journey is learning how to use transformational lambda functions to go serverless. Through hands-on exercises, you will add lambda layers and trigger lambda functions on specific conditions.

Going serverless

Creating and running Lambda functions

Serverless vs servers

Lambda function components

Reading data from S3 based on an event

Your first live lambda!

Adding a lambda layer

Lambda flow

Using environment variables

Serverless data workflow

Fast vs slow lambdas

A time-triggered lambda

Time-triggered lambda code

Serverless APIs

Create an API lambda

A serverless API

You're now ready to encode and decode streaming data and analyze data directly in the stream. You will even use multiple streams to get daily vehicle top speeds.

A transformational Lambda

Transforming data inside a stream

Encoding and decoding base64

Create a transformational lambda

A barebones transformational lambda

Analyzing data in the stream

Creating a Kinesis data analytics application

Kinesis data analytics vs transformational Lambda

Building a Kinesis data analytics application

Kinesis data analytics SQL components

Using multiple streams

Delivering data from Kinesis Analytics

Get the daily top speed

Using Kinesis data analytics for alerts

Analyzing streaming data

In this final chapter, you will discover how to monitor your stream's performance using logs, metrics, alarms and dashboards. You will use Elasticsearch and build your own Kibana dashboard.

Streaming data case study

Creating an Elasticsearch cluster

Options

Kinesis Data Analytics

Elastic Search

Amazon RedShift

RedShift vs Elasticsearch

Monitoring performance

Cloudwatch dasbhoards and alarms

CloudWatch Metrics

CloudWatch Alarms

CloudWatch Dashboards

Cloudwatch components

Monitoring Lambda

Cloudwatch monitoring flow

Visualizing streaming data

Working with ElasticSearch using Kibana

Amazon CloudWatch

Cloudwatch vs Elasticsearch

Elasticsearch visualization flow

Alerts after Kinesis Lambda Transform

An alternative approach

Another alternative approach

Monitoring and visualizing streaming data

What powers the systems that we use every day without realizing, like fraud detection to keep our transactions secure or traffic signals that keep traffic flowing smoothly? The answer is streaming data (data that is continuously generated by different sources) and serverless technologies—like Amazon Kinesis and AWS Lambda.
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In this course, you’ll learn how to leverage these powerful technologies by helping a fictional data engineer named Cody. Your goal is to help her to collect real-time streaming data from city-owned vehicles, analyze the data, and send relevant alerts like speed warnings to drivers. Using Amazon Kinesis and Firehose, you’ll learn how to ingest data from millions of sources before using Kinesis Analytics to analyze data as it moves through the stream. You’ll also spin up serverless functions in AWS Lambda that will conditionally trigger actions based on the data received. By the end of this training you’ll know how to create live ElasticSearch dashboards with AWS QuickSight and CloudWatch—and hopefully helped Cody complete her ambitious project.


Introduction to AWS Boto in Python

Introduction to Shell

Streaming Concepts

Learn how to work with streaming data using serverless technologies on AWS.

Streaming Data with AWS Kinesis and Lambda

Navigate to the Elasticsearch domain in AWS and open Kibana.

Create an index pattern that matches the one you specified in Firehose for the vehicle data, then head to discover and verify the data is coming in correctly.

Create two visualizations and then add visualizations to the dashboard.

Create a destination to tell Elasticsearch how to notify alarm receivers.

Create a monitor to check for speeders, then set the speeder threshold to trigger the alarm if anyone speeds more than 2x in an hour.

Elasticsearch visualization flow

Streaming Data with AWS Kinesis and Lambda

Hands-on interactive exercise