Exam AWS MLS Machine Learning

[MLS-C01] [Exploratory Data Analysis] Introduction

Posted by Oscaner on June 16, 2022

Basic data analysis concepts and terminology
Kinesis Data Streams
Kinesis Data Firehose
Kinesis Video Streams
Kinesis Data Analytics
Visualize data for machine learning

Sources

Sources for streaming data

Kaggle.com
University of California Irvine
Open Data on AWS
Google big query also gives access to data sets

Kinesis Data Streams

Gets data from data producers such as IoT, social media
Uses shards to stream data to consumers such as EC2, Lambda, Kinesis Data Analytics, EMR clusters
Consumers then send data to a data repository such as S3, DynamoDB, Redshift, or Business Intelligence Tools

Kinesis Data Firehose

Receives data from producers such as IoT, social media
Uses Lambda functioning instead of shards to transmit producer data
Lambda function puts data to data stores such as S3, Redshift, ElasticSearch, or Splunk
Can transmit directly from producers through Firehose to the data store (don’t have to use Lambda intermediary)
S3 events to store to DynamoDB

Kinesis Video Streams

Build video processing applications such as machine learning models
Producers such as web cams, security cameras, audio feeds, images
Data Consumers - Kinesis Video Stream applications
Stores to S3

Kinesis Data Analytics

Use SQL to process streaming data
Sources: Kinesis Data Streams and Kinesis Data Firehose
SQL queries put to S3, Redshift, or visualization and Business Intelligence Tools

Visualizing Data for Machine Learning

Technique using static and interactive visuals to represent large amounts of data
Visualizes patterns, trends and correlations that may be difficult to discern
Data visualization helps monetize data as a product

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