Databases & Analytics

Databases & Analytics

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…

{
  "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

RDS Deployments: Multi-Region

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

Multi-Region

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

Amazon S3 List Other Compute Section

Download PDF : AWS Cloud Practitioner Study Notes (PDF)