Data Warehousing
  • Data Warehousing
  • Readme
  • Fundamentals
    • Terms to Know
    • Jobs
    • Skills needed for DW developer
    • Application Tiers
    • Operational Database
    • What is a Data Warehouse
      • Typical Data Architecture
      • Problem Statement
      • Features of Data Warehouse
      • Need for Data Warehouse
      • Current State of the Art
    • Activities of Data Science
    • Types of Data
    • Data Storage Systems
    • Data Warehouse 1980 - Current
    • Data Warehouse vs Data Mart
    • Data Warehouse Architecture
      • Top-Down Approach
      • Bottom-Up Approach
    • Data Warehouse Characteristic
      • Subject Oriented
      • Integrated
      • Time Variant
      • Non Volatile
    • Tools
    • Cloud vs On-Premise
    • Steps to design a Data Warehouse
      • Gather Requirements
      • Environment
      • Data Modeling
      • Choosing ETL / ELT Solution
      • Online Analytic Processing
      • Front End
      • Query Optimization
    • Dataset Examples
    • Thoughts on some data
  • RDBMS
    • Data Model
      • Entity Relationship Model
      • Attributes
      • Keys
      • Transaction
      • ACID
    • Online vs Batch
    • DSL vs GPL
    • Connect to Elvis
    • SQL Concepts
      • Basic Select - 1
      • Basic Select - 2
      • UNION Operators
      • Wild Cards & Distinct
      • Group By & Having
      • Sub Queries
      • Derived Tables
      • Views
    • Practice using SQLBolt
  • Cloud
    • Overview
    • Types of Cloud Services
    • Challenges of Cloud Computing
    • AWS
      • AWS Global Infrastructure
      • EC2
      • S3
      • IAM
    • Terraform
  • Spark - Databricks
    • Storage Formats
    • File Formats
    • Medallion Architecture
    • Delta
  • Data Warehousing Concepts
    • Dimensional Modelling
      • Star Schema
      • Galaxy Schema
      • Snowflake Schema
      • Starflake Schema
      • Star vs Snowflake
      • GRAIN
      • Multi-Fact Star Schema
      • Vertabelo Tool
    • Dimension - Fact
    • Sample Excercise
    • Keys
      • Why Surrogate Keys are Important
    • More Examples
    • Master Data Management
    • Steps of Dimensional Modeling
    • Types of Dimensions
      • Date Dimension Table
      • Degenerate Dimension
      • Junk Dimension
      • Static Dimension
      • Conformed Dimensions
      • Slowly Changing Dimensions
        • SCD - Type 0
        • SCD - Type 1
        • SCD - Type 2
        • SCD - Type 3
        • SCD - Type 4
        • SCD - Type 6
        • SCD - Type 5 - Fun Fact
      • Role Playing Dimension
      • Conformed vs Role Playing
      • Shrunken Dimension
      • Swappable Dimension
      • Step Dimension
    • Types of Facts
      • Factless Fact Table
      • Transaction Fact
      • Periodic Fact
      • Accumulating Snapshot Fact Table
      • Transaction vs Periodic vs Accumulating
      • Additive, Semi-Additive, Non-Additive
      • Periodic Snapshot vs Additive
      • Conformed Fact
    • Sample Data Architecture Diagram
    • Data Pipeline Models
    • New DW Concepts
Powered by GitBook
On this page
  1. Cloud

Types of Cloud Services

SaaS

Software as a Service

Cloud-based service providers offer end-user applications. Google Apps, DropBox, Slack, etc.

  • Web access to Software (primarily commercial).

  • Software is managed from a central location.

  • Delivery 1 - many models.

  • No patches, No upgrades

When not to use

  • Hardware integration is needed. (Price Scanner)

  • Faster processing is required.

  • Cannot host data outside the premise.

PaaS

Platform as a Service

Software tools are available over the internet. AWS RDS, Heroku, Salesforce

  • Scalable

  • Built on Virtualization Technology

  • No User needed to maintain software. (DB upgrades, patches by cloud team)

When not to use PaaS

  • Propriety tools don't allow moving to diff providers. (AWS-specific tools)

  • Using new software that is not part of the PaaS toolset.

IaaS

Infrastructure as a Service

Cloud-based hardware services. Pay-as-you-go services for Storage, Networking, and Servers.

Amazon EC2, Google Compute Engine, S3.

  • Highly flexible and scalable.

  • Accessible by more than one user.

  • Cost-effective (if used right).

Serverless computing

Focuses on building apps without spending time managing servers/infrastructure.

Feature automatic scaling, built-in high availability, and pay-per-use.

Use of resources when a specific function or event occurs.

Cloud providers handle the deployment, and capacity, and manage the servers.

Example: AWS Lambda, AWS Step Functions.

Easy way to remember SaaS, PaaS, IaaS

PreviousOverviewNextChallenges of Cloud Computing

Last updated 2 years ago

bigcommerce.com