[Modify/Delete] Remove Notes for Some Problem
This commit is contained in:
Kanani Nirav
@@ -1,295 +0,0 @@
|
||||
# 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 |
|
||||
|
||||
|
||||
|
||||
### RDS Deployments: Multi-Region
|
||||
|
||||
- Multi-Region (Read Replicas)
|
||||
- Disaster recovery in case of region issue
|
||||
- Local performance for global reads
|
||||
- Replication cost
|
||||
|
||||
|
||||
|
||||
## 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
|
||||
|
||||
* * *
|
||||
|
||||
[<img align="center" src="../images/back-arrow.png" height="20" width="20"/> Amazon S3](./s3.md) [<img align="center" src="../images/list.png" height="30" width="30"/> List](../README.md) [Other Compute Section <img align="center" src="../images/forward-arrow.png" height="20" width="20"/>](./other_compute.md)
|
||||
Reference in New Issue
Block a user