292 lines
12 KiB
Markdown
292 lines
12 KiB
Markdown
# Databases & Analytics
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- [Databases & Analytics](#databases--analytics)
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- [Databases Intro](#databases-intro)
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- [Relational Databases](#relational-databases)
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- [NoSQL Databases](#nosql-databases)
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- [NoSQL data example: JSON](#nosql-data-example-json)
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- [Databases & Shared Responsibility on AWS](#databases--shared-responsibility-on-aws)
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- [AWS RDS Overview](#aws-rds-overview)
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- [Advantage over using RDS versus deploying DB on EC2](#advantage-over-using-rds-versus-deploying-db-on-ec2)
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- [RDS Deployments: Read Replicas, Multi-AZ](#rds-deployments-read-replicas-multi-az)
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- [RDS Deployments: Multi-Region](#rds-deployments-multi-region)
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- [Amazon Aurora](#amazon-aurora)
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- [Amazon ElastiCache Overview](#amazon-elasticache-overview)
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- [DynamoDB](#dynamodb)
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- [DynamoDB Accelerator - DAX](#dynamodb-accelerator---dax)
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- [DynamoDB - Global Tables](#dynamodb---global-tables)
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- [Redshift Overview](#redshift-overview)
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- [Amazon EMR](#amazon-emr)
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- [Amazon Athena](#amazon-athena)
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- [Amazon QuickSight](#amazon-quicksight)
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- [DocumentDB](#documentdb)
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- [Amazon Neptune](#amazon-neptune)
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- [Amazon QLDB](#amazon-qldb)
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- [Amazon Managed Blockchain](#amazon-managed-blockchain)
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- [AWS Glue](#aws-glue)
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- [DMS - Database Migration Service](#dms---database-migration-service)
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- [Databases & Analytics Summary](#databases--analytics-summary)
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## Databases Intro
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- Storing data on disk (EFS, EBS, EC2 Instance Store, S3) can have its limits
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- Sometimes, you want to store data in a database…
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- You can structure the data
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- You build indexes to efficiently query / search through the data
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- You define relationships between your datasets
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- Databases are optimized for a purpose and come with different features, shapes and constraint
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## Relational Databases
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- Looks just like Excel spreadsheets, with links between them!
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- Can use the SQL language to perform queries / lookups
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## NoSQL Databases
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- NoSQL = non-SQL = non relational databases
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- NoSQL databases are purpose built for specific data models and have flexible schemas for building modern applications.
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- Benefits:
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- Flexibility: easy to evolve data model
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- Scalability: designed to scale-out by using distributed clusters
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- High-performance: optimized for a specific data model
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- Highly functional: types optimized for the data model
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- Examples: Key-value, document, graph, in-memory, search databases
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### NoSQL data example: JSON
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- JSON = JavaScript Object Notation
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- JSON is a common form of data that fits into a NoSQL model
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- Data can be nested
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- Fields can change over time
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- Support for new types: arrays, etc…
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```json
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{
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"name": "John",
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"age": 30,
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"cars": [
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"Ford",
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"BMW",
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"Fiat"
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],
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"address": {
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"type": "house",
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"number": 23,
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"street": "Dream Road"
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}
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}
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```
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## Databases & Shared Responsibility on AWS
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- AWS offers use to manage different databases
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- Benefits include:
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- Quick Provisioning, High Availability, Vertical and Horizontal Scaling
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- Automated Backup & Restore, Operations, Upgrades
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- Operating System Patching is handled by AWS
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- Monitoring, alerting
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- Note: many databases technologies could be run on EC2, but you must handle yourself the resiliency, backup, patching, high availability, fault tolerance, scaling
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## AWS RDS Overview
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- RDS stands for Relational Database Service
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- It’s a managed DB service for DB use SQL as a query language.
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- It allows you to create databases in the cloud that are managed by AWS
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- Postgres
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- MySQL
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- MariaDB
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- Oracle
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- Microsoft SQL Server
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- **Aurora (AWS Proprietary database)**
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### Advantage over using RDS versus deploying DB on EC2
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- RDS is a managed service:
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- Automated provisioning, OS patching
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- Continuous backups and restore to specific timestamp (Point in Time Restore)!
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- Monitoring dashboards
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- Read replicas for improved read performance
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- Multi AZ setup for DR (Disaster Recovery)
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- Maintenance windows for upgrades
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- Scaling capability (vertical and horizontal)
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- Storage backed by EBS (gp2 or io1)
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- BUT you can’t SSH into your instances
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### RDS Deployments: Read Replicas, Multi-AZ
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| Read Replicas | Multi-AZ |
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| ----------------------------------- | ------------------------------------------------- |
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| Scale the read workload of your DB | Failover in case of AZ outage (high availability) |
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| Can create up to 5 Read Replicas | Data is only read/written to the main database |
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| Data is only written to the main DB | Can only have 1 other AZ as failover |
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### RDS Deployments: Multi-Region
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- Multi-Region (Read Replicas)
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- Disaster recovery in case of region issue
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- Local performance for global reads
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- Replication cost
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## Amazon Aurora
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- Aurora is a proprietary technology from AWS (not open sourced)
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- PostgreSQL and MySQL are both supported as Aurora DB
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- Aurora is “AWS cloud optimized” and claims 5x performance improvement over MySQL on RDS, over 3x the performance of Postgres on RDS
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- Aurora storage automatically grows in increments of 10GB, up to 64 TB.
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- Aurora costs more than RDS (20% more) – but is more efficient
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- Not in the free tier
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## Amazon ElastiCache Overview
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- The same way RDS is to get managed Relational Databases…
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- ElastiCache is to get managed Redis or Memcached
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- Caches are in-memory databases with high performance, low latency
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- Helps reduce load off databases for read intensive workloads
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- AWS takes care of OS maintenance / patching, optimizations, setup, configuration, monitoring, failure recovery and backup
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## DynamoDB
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- Fully Managed Highly available with replication across 3 AZ
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- NoSQL database - not a relational database
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- Scales to massive workloads, distributed “serverless” database
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- Millions of requests per seconds, trillions of row, 100s of TB of storage
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- Fast and consistent in performance
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- Single-digit millisecond latency – low latency retrieval
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- Integrated with IAM for security, authorization and administration
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- Low cost and auto scaling capabilities
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- Standard & Infrequent Access (IA) Table Class
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### DynamoDB Accelerator - DAX
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- Fully Managed in-memory cache for DynamoDB
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- 10x performance improvement – single- digit millisecond latency to microseconds latency – when accessing your DynamoDB tables
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- Secure, highly scalable & highly available
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- 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
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### DynamoDB - Global Tables
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- Make a DynamoDB table accessible with low latency in multiple-regions
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- Active-Active replication (read/write to any AWS Region)
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## Redshift Overview
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- Redshift is based on PostgreSQL, but it’s not used for OLTP (Online Transactional Processing)
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- It’s OLAP – online analytical processing (analytics and data warehousing)
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- Load data once every hour, not every second
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- 10x better performance than other data warehouses, scale to PBs of data
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- Columnar storage of data (instead of row based)
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- Massively Parallel Query Execution (MPP), highly available
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- Pay as you go based on the instances provisioned
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- Has a SQL interface for performing the queries
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- BI tools such as AWS Quicksight or Tableau integrate with it
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## Amazon EMR
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- EMR stands for “Elastic MapReduce”
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- EMR helps creating Hadoop clusters (Big Data) to analyze and process vast amount of data
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- The clusters can be made of hundreds of EC2 instances
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- Also supports Apache Spark, HBase, Presto, Flink
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- EMR takes care of all the provisioning and configuration
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- Auto-scaling and integrated with Spot instances
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- Use cases: data processing, machine learning, web indexing, big data
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## Amazon Athena
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- Serverless query service to analyze data stored in Amazon S3
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- Uses standard SQL language to query the files
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- Supports CSV, JSON, ORC, Avro, and Parquet (built on Presto)
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- Pricing: $5.00 per TB of data scanned
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- Use compressed or columnar data for cost-savings (less scan)
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- Use cases: Business intelligence / analytics / reporting, analyze & query VPC Flow Logs, ELB Logs, CloudTrail trails, etc...
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- **analyze data in S3 using serverless SQL, use Athena**
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## Amazon QuickSight
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- Serverless machine learning-powered business intelligence service to create interactive dashboards
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- Fast, automatically scalable, embeddable, with per-session pricing
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- Use cases:
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- Business analytics
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- Building visualizations
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- Perform ad-hoc analysis
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- Get business insights using data
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- Integrated with RDS, Aurora, Athena, Redshift, S3…
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## DocumentDB
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- Aurora is an “AWS-implementation” of PostgreSQL / MySQL …
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- DocumentDB is the same for MongoDB (which is a NoSQL database)
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- MongoDB is used to store, query, and index JSON data
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- Similar “deployment concepts” as Aurora
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- Fully Managed, highly available with replication across 3 AZ
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- Aurora storage automatically grows in increments of 10GB, up to 64 TB.
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- Automatically scales to workloads with millions of requests per seconds
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## Amazon Neptune
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- Fully managed graph database
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- A popular graph dataset would be a social network
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- Users have friends
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- Posts have comments
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- Comments have likes from users
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- Users share and like posts…
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- Highly available across 3 AZ, with up to 15 read replicas
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- Build and run applications working with highly connected datasets – optimized for these complex and hard queries
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- Can store up to billions of relations and query the graph with milliseconds latency
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- Highly available with replications across multiple AZs
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- Great for knowledge graphs (Wikipedia), fraud detection, recommendation engines, social networking
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## Amazon QLDB
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- QLDB stands for ”Quantum Ledger Database”
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- A ledger is a book **recording financial transactions**
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- Fully Managed, Serverless, High available, Replication across 3 AZ
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- Used to **review history of all the changes made to your application data** over time
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- **Immutable** system: no entry can be removed or modified, cryptographically verifiable
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- 2-3x better performance than common ledger blockchain frameworks, manipulate data using SQL
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- Difference with Amazon Managed Blockchain: no decentralization component, in accordance with financial regulation rules
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## Amazon Managed Blockchain
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- Blockchain makes it possible to build applications where multiple parties can execute transactions without the need for a trusted, central authority.
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- Amazon Managed Blockchain is a managed service to:
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- Join public blockchain networks
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- Or create your own scalable private network
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- Compatible with the frameworks Hyperledger Fabric & Ethereum
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## AWS Glue
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- Managed extract, transform, and load (ETL) service
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- Useful to prepare and transform data for analytics
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- Fully serverless service
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- Glue Data Catalog: catalog of datasets
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- can be used by Athena, Redshift, EMR
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## DMS - Database Migration Service
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- Quickly and securely migrate databases to AWS, resilient, self healing
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- The source database remains available during the migration
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- Supports:
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- Homogeneous migrations: ex Oracle to Oracle
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- Heterogeneous migrations: ex Microsoft SQL Server to Aurora
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## Databases & Analytics Summary
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- Relational Databases - OLTP: RDS & Aurora (SQL)
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- Differences between Multi-AZ, Read Replicas, Multi-Region
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- In-memory Database: ElastiCache
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- Key/Value Database: DynamoDB (serverless) & DAX (cache for DynamoDB)
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- Warehouse - OLAP: Redshift (SQL)
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- Hadoop Cluster: EMR
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- Athena: query data on Amazon S3 (serverless & SQL)
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- QuickSight: dashboards on your data (serverless)
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- DocumentDB: “Aurora for MongoDB” (JSON – NoSQL database)
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- Amazon QLDB: Financial Transactions Ledger (immutable journal, cryptographically verifiable)
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- Amazon Managed Blockchain: managed Hyperledger Fabric & Ethereum blockchains
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- Glue: Managed ETL (Extract Transform Load) and Data Catalog service
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- Database Migration: DMS
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- Neptune: graph database
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