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Module 8 Databases

Types of Databases

• Relational/SQL DB
• Non-relational/No-SQL
• Data Warehousing (Big data analytics)
• Enterprise SQL
• Manual DB (get a VM and install an SQL engine)

Amazon Relational Database Service (Amazon RDS)

• SQL-based

Unmanaged vs Managed Services

Challenges of Relational Databases

things you have to think about in the traditional/unmanaged sense

• server maintenance and energy footprint
• software installation and patches
• database backups and high availability
• limits on scalability
• data security
• operating system (OS) installation and patches

if you know how to do these and you can do them yourself it's gonna be cheaper

Amazon RDS

• a managed service that sets up and operates a relational database in the cloud

From on-premises databases to Amazon RDS

bold → managed by AWS

• traditional → you do everything
• Cloud VM + DB engine → they take care of physical (OS above is your responsibility)
• DB services → they take care of a lot of things and then you focus on your application optimization nalang

Managed Services Responsibilities

• YOU: application optimization
• AWS:
  • OS installation and patches
  • database software installation and patches
  • database backups
  • high availability
  • scaling
  • power and racking and stacking servers
  • server maintenance

Amazon RDS DB Instances

• columns and tables with foreign keys connecting one thing to another
• MySQL, Amazon Aurora, Microsoft SQL Server, PostgreSQL, MariaDB, Oracle
• DB engines

Amazon RDS in a VPC (virtual private cloud)

• put the database in a private subnet for security
• users from outside can access the VM

High Availability with Multi-AZ deployment

• VM and application are on the public subnet
• master and slave setup (master copies to slave) for backups
• so when the master isn't working, it will connect to the slave backup and the slave will be promoted to master

there are many architectural advantages you can setup in the cloud to ensure the data will not be lost

Amazon RDS Read Replicas

Read from a replica when there's high amount of load/traffic on one
Load balancing of transactions

Features

• offers asynchronous replication
• can be promoted to primary if needed
  Functionality
• use for read-heavy database workloads
• offload read queries

Use Cases

When to use Amazon RDS

Use when your application requires...

• complex transactions or complex queries
• a medium to high query to write rate - up to 30,000 IOPS (15k reads + 15k writes)
• no more than a single worker node or shard
• high durability
  Do not use when your application requires.
• massive read/write rates (150k write/sec) → use Enterprise SQL
• sharding due to high data size or throughput demands → use Data Warehousing
• simple GET or PUT requests and queries that a NoSQL database can handle → use a Non Relational DB
• relational database management system (RBDMS) customization → get a VM and install your DB engine

Module 8 Section 2: Amazon DynamoDB

Relational vs Non-relational

• non-SQL/non-relational: JSON, XML, etc. (horizontal scaling)
• while SQL/relational: fixed columns (vertical scaling - many entries, same column length)

	Relational (SQL)	Non-Relational
Data Storage	rows and columns	key-value, document, graph
Schemas	fixed	dynamic, flexible
Querying	uses SQL	focuses on collection of documents/data
example
	all the columns have fixed amounts or have a strict formatting	it's possible that some might not have an author or format (difference in varying cols)

What is Amazon DynamoDB

• fast and flexible NoSQL database service for any scale
• noSQL database tables
• virtually unlimited storage
• items can have differing attributes
• low-latency queries
• scalable read/write throughput

Amazon Redshift

• for data warehousing → for analytics, business intelligence

Parallel Processing Architecture

• kind of like distributed computing
• the leader node can distribute load to other nodes to perform
• 

What can it do

• automation and scaling
  • manage, monitor, scale
• compatibility
  • Amazon Redshift 🤝 SQL Clients & business intelligence (BI) tools

Amazon Redshift use cases

• Enterprise data warehouse (EDW)
  • migrate at a pace that customers are comfortable with
  • experiment without large upfront cost or commitment
  • respond faster to business needs
• Big Data
  • low price point for small customers
  • managed service for ease of deployment and maintenance
  • focus more on data and less on database management
• Software as a Service (SaaS)
  • scale the data warehouse capacity as demand grows
  • add analytic functionality to applications
  • reduce hardware and software costs

Amazon Aurora (Enterprise RDB/SQL)

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• enterprise-class relational database
• compatible with MySQL or PostgreSQL
• automate time-consuming tasks (like provisioning, patching, backup, recovery, failure detection, repair)
• Aurora itself is the engine, this is SaaS (the rest kanina are PaaS)

Amazon Aurora Service Benefits

• managed
• pay-as-you-go
• fast and available
• simple
• compatible
• high availability copies are saved in other availability zones
• resilient design as you perform transactions, it can do a master slave architecture to have copies on hand

Summary: The right tool for the right job