All of Amazon Web Services (AWS) Data Analytics Services – Table of Contents

Extracting Insights and Actionable Information from Data

Extracting Insights and Actionable Information from Data

Amazon Web Services (AWS) Data Analytics Processing

Learn the comprehensive set of data and analytical services to handle every step of the analytics process chain, ideal usage patterns and anti-patterns on AWS.

PURPOSE OF THIS BLOG SERIES:
Data is not only getting bigger (in “Volume”) and in ever-increasing different formats (the “Variety”) faster (the “Velocity”), but the need to derive “Value” quickly through analytics to provide actionable insights for businesses is indeed a differentiating factor between successful businesses that can mitigate risk and respond to customer actions in near real-time vs. other businesses that will fall behind in the day and age of data deluge.

In addition to the metaphorical “V’s” mentioned above to describe big data, there is one more: “Veracity” – being sure your data is clean prior to any analytics performed whatsoever. Garbage in, garbage out. There’s no time to waste making improper, mal-informed decisions based on dirty data. This is paramount. Using solutions in the AWS Marketplace make this crucial and difficult step easy.

Analyzing extensive data sets require noteworthy compute capacity that normally exceeds processing capabilities available on-premises. In addition, that compute capacity can fluctuate in size based on the data inputs and the type of analytics being performed. This characteristic of scaling workloads is perfectly suited to AWS and the AWS Marketplace’s pay-as-you-go cloud model, where applications can scale up and down (horizontally and vertically) based on demand. Extracting insights and actionable information from data can be done within minutes rather than months, and you only pay for what you use.

The vast majority of big data use cases deployed in the cloud today run on AWS, with unique customer references for big data analytics, of which 67 are enterprise, household names. You can build virtually any big data analytics application and support any workload regardless of the volume, velocity, and variety of data. With 50+ services and hundreds of features added every year, AWS provides everything you need to collect, store, process, analyze, and visualize big data on the cloud.

Let’s dive into these services and learn the ideal usage patterns of each so you can improve the way you build business analytics solutions and run your business.

Without Data Analytics, You're Guessing at Business Decisions!

Without Data Analytics, You’re Guessing at Business Decisions!

Each item in the Table of Contents below will become a link when live.

TABLE OF CONTENTS:

  • HOW ANALYTICS WILL CHANGE YOUR BUSINESS, FOR THE BETTER, FOREVER
    1. We’re Living in a Metrics-Driven World
    2. Key Enablers  to Big Data Analytics for Everyone
    3. Faster Time to Actionable Insights & ROI
    4. Data Mixology and the End of Data Silos
  • THE CURRENT STATE OF DATA & ANALYTICS
    1. The “old” buzz word: “Big Data”
    2. The “new” buzz word: “Digital Transformation”
    3. 2017: The Year of Intelligence
  • OVERCOMING KEY CHALLENGES IN DATA ANALYTICS
    1. Capacity Guessing
    2. Scaling Workloads
    3. Processing Power
    4. Cloud & Data Security
    5. Traditional Data Warehouses
    6. Traditional Relational Database Management Systems
    7. Discover, Migrate & Deploy Pre-Configured Big Data BI & Advanced Analytic Solutions in Minutes – and Pay Only for What You Use by the Hour!!!
  • WHY USE AWS FOR DATA ANALYTICS?
    1. AWS Big Data Analytics Advantages
    2. Some Specific Advantages of AWS Big Data Analytics
    3. AWS Marketplace for Big Data Analytics Advantages
    4. Some Specific Advantages of AWS Marketplace in Big Data Analytics
    5. Self-Managed Big Data Analytics Solutions on AWS Marketplace Examples
    6. AWS Marketplace “Hidden” Helpful Sites to Find Solutions
  • DATA COLLECTION ON AWS
    1. AWS Direct Connect
    2. AWS Snowball
    3. AWS Storage Gateway
    4. Amazon Kinesis Firehose
    5. Amazon S3 Transfer Acceleration
    6. AWS Database Migration Service
    7. AWS Marketplace Solutions for Data Collection
  • OVERVIEW OF DATA ORCHESTRATION ON AWS
    1. Amazon SWF
    2. AWS Data Pipeline
    3. AWS Lambda
  • OVERVIEW OF COMMONLY USED DATA STORES FOR ANALYTICAL WORKFLOWS ON AWS
    1. Amazon S3
    2. Amazon DynamoDB
    3. Amazon Aurora
  • OTHER IMPORTANT KNOWLEDGE NECESSITIES FOR AWS ANALYTICS
    1. Understanding the Need for Cloud Tools
    2. Where to Discover Pricing for Individual and Bundled AWS Services
    3. Where to Price Out a Production Cloud Solution vs On-Prem
    4. Monitoring Analytical Processes with Amazon CloudWatch
    5. Ensuring Clean Data Prior to Analyzing
    6. Batch Processing vs. Real-Time Processing
    7. Where to Find AWS Sample Data for Practice
  • AMAZON REDSHIFT DATA WAREHOUSE
    1. Amazon Redshift Overview
    2. Amazon Redshift vs. Traditional Data Warehouses
    3. Amazon Redshift Benefits
    4. Amazon Redshift Interfaces
    5. Amazon Redshift Integration with Other AWS Services
    6. Amazon Redshift Data Integration, Loading & Cluster Configuration
    7. Amazon Redshift Ideal Usage Patterns and Anti-Patterns
    8. AWS Marketplace Solution Matillion ETL for Redshift
  • THE AMAZON KINESIS FAMILY
    1. Amazon Kinesis Streams
    2. Amazon Kinesis Applications Using Amazon Kinesis Client Library
    3. Real-Time Streaming Data Producers
    4. Amazon Kinesis Data Stores
    5. Amazon Kinesis Integration with Other AWS Services
    6. Amazon Kinesis Stream Pre-Requisites: Identity & Access Management (IAM) Policies and Users
    7. Creating & Implementing Amazon Kinesis Streams
    8. The Role of Amazon Kinesis Firehose & Amazon Kinesis Analytics
    9. Amazon Kinesis Streams Ideal Usage Patterns & Anti-Patterns
  • AMAZON ELASTIC MAP REDUCE (EMR)
    1. Amazon EMR Overview & Benefits vs. Traditional Hadoop MapReduce
    2. Open Source Frameworks: EMR Interfaces
    3. Amazon EC2 Instance Configurations for Use with Amazon EMR
    4. Amazon EMR Data Stores
    5. Amazon EMR Cluster Pre-Requisites
    6. Launching an Amazon EMR Cluster
    7. Processing Data with Amazon EMR
    8. Amazon EMR Integration with Other AWS Services
    9. A Custom Framework of AWS Services for Data Lake Ingestion
    10. Amazon EMR Ideal Usage Patterns & Anti-Patterns
  • AMAZON ATHENA
    1. Amazon Athena Overview
    2. Amazon Athena Data Formats, Data Types & Compression Formats
    3. Defining Queries, Schemas & SerDe
    4. Amazon Athena Interfaces
    5. Amazon Athena Pre-Requisites
    6. Creating a Database & Table in Amazon Athena & Querying Data
    7. Amazon Athena Integration with Other AWS Services
    8. Amazon Athena Use Case Samples
    9. Analysis & Visualization Integrations with Amazon Athena
  • AMAZON ELASTICSEARCH SERVICE (ESS)
    1. Amazon Elasticsearch Service Overview vs. Open Source Elasticsearch
    2. Amazon ESS Benefits
    3. Amazon ESS Integration with Logstash & Kibana
    4. ESS ELK Stack on AWS Marketplace
    5. ESS Integration with Other AWS Services
    6. ESS Interfaces
    7. ESS Ideal Usage Patterns & Anti-Patterns
  • AMAZON MACHINE LEARNING (ML)
    1. Amazon ML Overview
    2. Amazon ML Benefits
    3. Amazon ML Key Features
    4. Generating Predictions with Amazon ML
    5. Amazon ML Integration with Other AWS Services
    6. Amazon ML Interfaces and SDKs
    7. Amazon ML Ideal Usage Patterns & Anti-Patterns
  • AMAZON ARTIFICIAL INTELLIGENCE (AI)
    1. Amazon AI Overview
    2. Amazon Lex
    3. Amazon Rekognition
    4. Amazon Polly
    5. AWS Deep Learning AMI’s
    6. Amazon Deep Learning CloudFormation Template
  • AMAZON QUICKSIGHT
    1. Amazon QuickSight Overview
    2. Amazon QuickSight Benefits
    3. What is SPICE?
    4. Amazon QuickSight Data Sources
    5. Amazon QuickSight Data Cleansing
    6. Amazon QuickSight Data Interfaces
    7. Amazon QuickSight Sample Data
    8. Amazon QuickSight Supported Visualization Types
    9. Building a Data Visualization with Amazon QuickSight
  • AWS GLUE
    1. AWS Glue Overview
    2. Integration with Other AWS Services
    3. Register Data Sources with AWS Glue
    4. Select a Data Source & Target: Watch the Magic!
    5. Schedule & Run AWS Glue Jobs
    6. Running AWS Glue: A Fully Managed ETL Service to Prepare Data for Analytics
  • AMAZON MOBILE ANALYTICS
    1. Amazon Mobile Analytics Overview
    2. Amazon Mobile Analytics Benefits
    3. Amazon Mobile Analytics Supported Platforms
    4. Amazon Mobile Analytics: More Details
    5. Amazon Mobile Analytics Integration with Other AWS Services
    6. Amazon Mobile Analytics Pre-Requisites
    7. Using Amazon Mobile Analytics to Understand User Engagement
  • AMAZON PINPOINT
    1. Amazon Pinpoint Overview
    2. Amazon Pinpoint Benefits
    3. Amazon Pinpoint Supported Platforms
    4. Amazon Pinpoint Interfaces
    5. Amazon Pinpoint Analytics
    6. Creating an Amazon Pinpoint Campaign
  • AWS DATA PIPELINE IN DETAIL
    1. AWS Data Pipeline Overview
    2. AWS Data Pipeline Supported Databases
    3. AWS Data Pipeline Data Stores
    4. AWS Data Pipeline Compute Services to Transform Data
    5. AWS Data Pipeline Data Nodes, Activities, Preconditions & Schedules
    6. AWS Data Pipeline Interfaces & Task Runner
    7. Steps to Complete Before You Create an AWS Data Pipeline
    8. Copy CSV Data Between Amazon S3 Buckets Using AWS Data Pipeline
  • AMAZON SIMPLE STORAGE SERVICE (S3) IN DETAIL
    1. Amazon S3 Overview
    2. Amazon S3 “Flavors”
    3. Amazon S3 Lifecycle
    4. Examples of Amazon S3’s Benefits in Large-Scale Analytics
  • AMAZON RDS AURORA DATABASE IN DETAIL
    1. Amazon RDS Aurora Overview vs. Traditional RDBMS
    2. Amazon Aurora Benefits
    3. Amazon Aurora’s Architecture
    4. Amazon Aurora Interfaces
    5. Amazon Aurora’s Integration with Other AWS Services
    6. Create an Amazon Aurora Cluster
    7. Amazon Aurora’s PostgreSQL Support
  • AMAZON DYNAMODB DATABASE IN DETAIL
    1. Amazon DynamoDB Overview
    2. Amazon DynamoDB Benefits in Analytical Processing
    3. Amazon DynamoDB Integration with Other AWS Services
    4. Amazon DynamoDB Interfaces
    5. Creating & Querying a DynamoDB Database

First Chapter can be found here.

#gottaluvAWS! #gottaluvAWSMarketplace!

This entry was posted in Amazon Artificial Intelligence, Amazon Athena, Amazon Aurora, Amazon DynamoDB, Amazon EC2 On-Demand Instances, Amazon Elastic MapReduce, Amazon Elasticsearch Service, Amazon IAM, Amazon Kinesis Family, Amazon Machine Learning, Amazon Mobile Analytics, Amazon Pinpoint, Amazon QuickSight, Amazon Redshift Data Warehouse, Amazon Web Services, Amazon Web Services Analytic Services, AWS Analytic Services, AWS Analytics, AWS BI, AWS Data Collection, AWS Data Orchestration, AWS Data Pipeline, AWS Glue, AWS Marketplace, AWS S2. Bookmark the permalink.

6 Responses to All of Amazon Web Services (AWS) Data Analytics Services – Table of Contents

  1. I am very lost with big data and machine learning. I want to show visionary impressions hoping to discover the kids personal quest for STEM career choices. Then expose them to other high technology tools of industry.

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  3. Vee says:

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