# Databases & Analytics

- [Databases & Analytics](#databases--analytics)
  - [Databases Intro](#databases-intro)
  - [Relational Databases](#relational-databases)
  - [NoSQL Databases](#nosql-databases)
    - [NoSQL data example: JSON](#nosql-data-example-json)
  - [Databases & Shared Responsibility on AWS](#databases--shared-responsibility-on-aws)
  - [AWS RDS Overview](#aws-rds-overview)
    - [Advantage over using RDS versus deploying DB on EC2](#advantage-over-using-rds-versus-deploying-db-on-ec2)
    - [RDS Deployments: Read Replicas, Multi-AZ](#rds-deployments-read-replicas-multi-az)
    - [RDS Deployments: Multi-Region](#rds-deployments-multi-region)
  - [Amazon Aurora](#amazon-aurora)
  - [Amazon ElastiCache Overview](#amazon-elasticache-overview)
  - [DynamoDB](#dynamodb)
    - [DynamoDB Accelerator - DAX](#dynamodb-accelerator---dax)
    - [DynamoDB - Global Tables](#dynamodb---global-tables)
  - [Redshift Overview](#redshift-overview)
  - [Amazon EMR](#amazon-emr)
  - [Amazon Athena](#amazon-athena)
  - [Amazon QuickSight](#amazon-quicksight)
  - [DocumentDB](#documentdb)
  - [Amazon Neptune](#amazon-neptune)
  - [Amazon QLDB](#amazon-qldb)
  - [Amazon Managed Blockchain](#amazon-managed-blockchain)
  - [AWS Glue](#aws-glue)
  - [DMS - Database Migration Service](#dms---database-migration-service)
  - [Databases & Analytics Summary](#databases--analytics-summary)

## Databases Intro

- Storing data on disk (EFS, EBS, EC2 Instance Store, S3) can have its limits
- Sometimes, you want to store data in a database…
- You can structure the data
- You build indexes to efficiently query / search through the data
- You define relationships between your datasets
- Databases are optimized for a purpose and come with different features, shapes and constraint

## Relational Databases

- Looks just like Excel spreadsheets, with links between them!
- Can use the SQL language to perform queries / lookups

## NoSQL Databases

- NoSQL = non-SQL = non relational databases
- NoSQL databases are purpose built for specific data models and have flexible schemas for building modern applications.
- Benefits:
  - Flexibility: easy to evolve data model
  - Scalability: designed to scale-out by using distributed clusters
  - High-performance: optimized for a specific data model
  - Highly functional: types optimized for the data model
- Examples: Key-value, document, graph, in-memory, search databases

### NoSQL data example: JSON

- JSON = JavaScript Object Notation
- JSON is a common form of data that fits into a NoSQL model
- Data can be nested
- Fields can change over time
- Support for new types: arrays, etc…

```json
{
  "name": "John",
  "age": 30,
  "cars": [
    "Ford",
    "BMW",
    "Fiat"
  ],
  "address": {
    "type": "house",
    "number": 23,
    "street": "Dream Road"
  }
}
```

## Databases & Shared Responsibility on AWS

- AWS offers use to manage different databases
- Benefits include:
  - Quick Provisioning, High Availability, Vertical and Horizontal Scaling
  - Automated Backup & Restore, Operations, Upgrades
  - Operating System Patching is handled by AWS
  - Monitoring, alerting
- Note: many databases technologies could be run on EC2, but you must handle yourself the resiliency, backup, patching, high availability, fault tolerance, scaling

## AWS RDS Overview

- RDS stands for Relational Database Service
- It’s a managed DB service for DB use SQL as a query language.
- It allows you to create databases in the cloud that are managed by AWS
  - Postgres
  - MySQL
  - MariaDB
  - Oracle
  - Microsoft SQL Server
  - **Aurora (AWS Proprietary database)**

### Advantage over using RDS versus deploying DB on EC2

- RDS is a managed service:
  - Automated provisioning, OS patching
  - Continuous backups and restore to specific timestamp (Point in Time Restore)!
  - Monitoring dashboards
  - Read replicas for improved read performance
  - Multi AZ setup for DR (Disaster Recovery)
  - Maintenance windows for upgrades
  - Scaling capability (vertical and horizontal)
  - Storage backed by EBS (gp2 or io1)
- BUT you can’t SSH into your instances

### RDS Deployments: Read Replicas, Multi-AZ

| Read Replicas                       | Multi-AZ                                          |
| ----------------------------------- | ------------------------------------------------- |
| Scale the read workload of your DB  | Failover in case of AZ outage (high availability) |
| Can create up to 5 Read Replicas    | Data is only read/written to the main database    |
| Data is only written to the main DB | Can only have 1 other AZ as failover              |

![Read Replicas Multi-AZ](../images/read_replicas_multi_AZ.png)

### RDS Deployments: Multi-Region

- Multi-Region (Read Replicas)
  - Disaster recovery in case of region issue
  - Local performance for global reads
  - Replication cost

![Multi-Region](../images/multi_region.png)

## Amazon Aurora

- Aurora is a proprietary technology from AWS (not open sourced)
- PostgreSQL and MySQL are both supported as Aurora DB
- Aurora is “AWS cloud optimized” and claims 5x performance improvement over MySQL on RDS, over 3x the performance of Postgres on RDS
- Aurora storage automatically grows in increments of 10GB, up to 64 TB.
- Aurora costs more than RDS (20% more) – but is more efficient
- Not in the free tier

## Amazon ElastiCache Overview

- The same way RDS is to get managed Relational Databases…
- ElastiCache is to get managed Redis or Memcached
- Caches are in-memory databases with high performance, low latency
- Helps reduce load off databases for read intensive workloads
- AWS takes care of OS maintenance / patching, optimizations, setup, configuration, monitoring, failure recovery and backup

## DynamoDB

- Fully Managed Highly available with replication across 3 AZ
- NoSQL database - not a relational database
- Scales to massive workloads, distributed “serverless” database
- Millions of requests per seconds, trillions of row, 100s of TB of storage
- Fast and consistent in performance
- Single-digit millisecond latency – low latency retrieval
- Integrated with IAM for security, authorization and administration
- Low cost and auto scaling capabilities
- Standard & Infrequent Access (IA) Table Class

### DynamoDB Accelerator - DAX

- Fully Managed in-memory cache for DynamoDB
- 10x performance improvement – single- digit millisecond latency to microseconds latency – when accessing your DynamoDB tables
- Secure, highly scalable & highly available
- Difference with ElastiCache at the CCP level: DAX is only used for and is integrated with DynamoDB, while ElastiCache can be used for other databases

### DynamoDB - Global Tables

- Make a DynamoDB table accessible with low latency in multiple-regions
- Active-Active replication (read/write to any AWS Region)

## Redshift Overview

- Redshift is based on PostgreSQL, but it’s not used for OLTP (Online Transactional Processing)
- It’s OLAP – online analytical processing (analytics and data warehousing)
- Load data once every hour, not every second
- 10x better performance than other data warehouses, scale to PBs of data
- Columnar storage of data (instead of row based)
- Massively Parallel Query Execution (MPP), highly available
- Pay as you go based on the instances provisioned
- Has a SQL interface for performing the queries
- BI tools such as AWS Quicksight or Tableau integrate with it

## Amazon EMR

- EMR stands for “Elastic MapReduce”
- EMR helps creating Hadoop clusters (Big Data) to analyze and process vast amount of data
- The clusters can be made of hundreds of EC2 instances
- Also supports Apache Spark, HBase, Presto, Flink
- EMR takes care of all the provisioning and configuration
- Auto-scaling and integrated with Spot instances
- Use cases: data processing, machine learning, web indexing, big data

## Amazon Athena

- Serverless query service to analyze data stored in Amazon S3
- Uses standard SQL language to query the files
- Supports CSV, JSON, ORC, Avro, and Parquet (built on Presto)
- Pricing: $5.00 per TB of data scanned
- Use compressed or columnar data for cost-savings (less scan)
- Use cases: Business intelligence / analytics / reporting, analyze & query VPC Flow Logs, ELB Logs, CloudTrail trails, etc...
- **analyze data in S3 using serverless SQL, use Athena**

## Amazon QuickSight

- Serverless machine learning-powered business intelligence service to create interactive dashboards
- Fast, automatically scalable, embeddable, with per-session pricing
- Use cases:
  - Business analytics
  - Building visualizations
  - Perform ad-hoc analysis
  - Get business insights using data
- Integrated with RDS, Aurora, Athena, Redshift, S3…

## DocumentDB

- Aurora is an “AWS-implementation” of PostgreSQL / MySQL …
- DocumentDB is the same for MongoDB (which is a NoSQL database)
- MongoDB is used to store, query, and index JSON data
- Similar “deployment concepts” as Aurora
- Fully Managed, highly available with replication across 3 AZ
- Aurora storage automatically grows in increments of 10GB, up to 64 TB.
- Automatically scales to workloads with millions of requests per seconds

## Amazon Neptune

- Fully managed graph database
- A popular graph dataset would be a social network
  - Users have friends
  - Posts have comments
  - Comments have likes from users
  - Users share and like posts…
- Highly available across 3 AZ, with up to 15 read replicas
- Build and run applications working with highly connected datasets – optimized for these complex and hard queries
- Can store up to billions of relations and query the graph with milliseconds latency
- Highly available with replications across multiple AZs
- Great for knowledge graphs (Wikipedia), fraud detection, recommendation engines, social networking

## Amazon QLDB

- QLDB stands for ”Quantum Ledger Database”
- A ledger is a book **recording financial transactions**
- Fully Managed, Serverless, High available, Replication across 3 AZ
- Used to **review history of all the changes made to your application data** over time
- **Immutable** system: no entry can be removed or modified, cryptographically verifiable
- 2-3x better performance than common ledger blockchain frameworks, manipulate data using SQL
- Difference with Amazon Managed Blockchain: no decentralization component, in accordance with financial regulation rules

## Amazon Managed Blockchain

- Blockchain makes it possible to build applications where multiple parties can execute transactions without the need for a trusted, central authority.
- Amazon Managed Blockchain is a managed service to:
  - Join public blockchain networks
  - Or create your own scalable private network
- Compatible with the frameworks Hyperledger Fabric & Ethereum

## AWS Glue

- Managed extract, transform, and load (ETL) service
- Useful to prepare and transform data for analytics
- Fully serverless service
- Glue Data Catalog: catalog of datasets
  - can be used by Athena, Redshift, EMR

## DMS - Database Migration Service

- Quickly and securely migrate databases to AWS, resilient, self healing
- The source database remains available during the migration
- Supports:
  - Homogeneous migrations: ex Oracle to Oracle
  - Heterogeneous migrations: ex Microsoft SQL Server to Aurora

## Databases & Analytics Summary

- Relational Databases - OLTP: RDS & Aurora (SQL)
- Differences between Multi-AZ, Read Replicas, Multi-Region
- In-memory Database: ElastiCache
- Key/Value Database: DynamoDB (serverless) & DAX (cache for DynamoDB)
- Warehouse - OLAP: Redshift (SQL)
- Hadoop Cluster: EMR
- Athena: query data on Amazon S3 (serverless & SQL)
- QuickSight: dashboards on your data (serverless)
- DocumentDB: “Aurora for MongoDB” (JSON – NoSQL database)
- Amazon QLDB: Financial Transactions Ledger (immutable journal, cryptographically verifiable)
- Amazon Managed Blockchain: managed Hyperledger Fabric & Ethereum blockchains
- Glue: Managed ETL (Extract Transform Load) and Data Catalog service
- Database Migration: DMS
- Neptune: graph database

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