[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 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

本文由 Oscaner 创作, 采用 知识共享署名4.0 国际许可协议进行许可
本站文章除注明转载/出处外, 均为本站原创或翻译, 转载前请务必署名