Choosing AWS Databases - RDS vs DynamoDB vs Redshift - AWS Certification Cheat Sheet

Let’s get a quick overview of how you can choose between different AWS Databases. How do you choose RDS vs DynamoDB vs Redshift?

You will learn

• What are the different types of databases?
• How can you choose the right AWS database for your scenario?
• What are the differences between RDS vs DynamoDB vs Redshift?
• When do you use RDS vs DynamoDB vs Redshift?

Database Categories

• There are several categories of databases:
  • Relational (OLTP and OLAP), Document, Key Value, Graph, In Memory among others
• Choosing type of database for your use case is not easy. A few factors:
  • Do you want a fixed schema?
    • Do you want flexibility in defining and changing your schema? (schemaless)
  • What level of transaction properties do you need? (atomicity and consistency)
  • What kind of latency do you want? (seconds, milliseconds or microseconds)
  • How many transactions do you expect? (hundreds or thousands or millions of transactions per second)
  • How much data will be stored? (MBs or GBs or TBs or PBs)
  • and a lot more…

Relational Databases

• This was the only option until a decade back!
• Most popular (or unpopular) type of databases
• Predefined schema with tables and relationships
• Very strong transactional capabilities
• Used for
  • OLTP (Online Transaction Processing) use cases and
  • OLAP (Online Analytics Processing) use cases

Relational Database - OLTP (Online Transaction Processing)

• Applications where large number of users make large number of small transactions
  • small data reads, updates and deletes
• Use cases:
  • Most traditional applications, ERP, CRM, e-commerce, banking applications
• Popular databases:
  • MySQL, Oracle, SQL Server etc
• Recommended AWS Managed Service:
  • Amazon RDS
  • Supports Amazon Aurora, PostgreSQL, MySQL, MariaDB (Enhanced MySQL), Oracle Database, and SQL Server

Relational Database - OLAP (Online Analytics Processing)

• Applications allowing users to analyze petabytes of data
  • Examples : Reporting applications, Data ware houses, Business intelligence applications, Analytics systems
  • Sample application : Decide insurance premiums analyzing data from last hundred years
  • Data is consolidated from multiple (transactional) databases
• Recommended AWS Managed Service
  • Amazon Redshift
  • Petabyte-scale distributed data ware house based on PostgreSQL

Relational Databases - OLAP vs OLTP

• OLAP and OLTP use similar data structures
• BUT very different approach in how data is stored
• OLTP databases use row storage
  • Each table row is stored together
  • Efficient for processing small transactions
• OLAP databases use columnar storage
  • Each table column is stored together
  • High compression - store petabytes of data efficiently
  • Distribute data - one table in multiple cluster nodes
  • Execute single query across multiple nodes - Complex queries can be executed efficiently

Document Database

• Structure data the way your application needs it
• Create one table instead of dozens!
• Quickly evolving semi structured data (schema-less)
• Use cases : Content management, catalogs, user profiles
• Advantages : (Horizontally) Scalable to terabytes of data with millisecond responses upto millions of transactions per second
• Recommended AWS Managed Service
  • Amazon DynamoDB

Key-value

• Use a simple key-value pair to store data. Key is a unique identifier.
• Values can be objects, compound objects or simple data values
• Advantages : (Horizontally) Scalable to terabytes of data with millisecond responses upto millions of transactions per second
• Recommended AWS Managed Service - Amazon DynamoDB again
• Use cases : shopping carts, session stores, gaming applications and very high traffic web apps

Graph

• Store and navigate data with complex relationships
• Use cases : Social Networking Data (Twitter, Facebook), Fraud Detection
• Recommended AWS Managed Service - Amazon Neptune

In-memory Databases

• Retrieving data from memory is much faster from retrieving data from disk
• In-memory databases like Redis deliver microsecond latency by storing persistent data in memory
• Recommended AWS Managed Service
  • Amazon ElastiCache
  • Supports Redis and Memcached
    • Redis is recommended for persistent data
    • Memcached is recommended for simple caches
• Use cases : Caching, session management, gaming leader boards, geospatial applications

AWS Databases - Summary

AWS Databases - Interview and Certification Questions