SAA - C03 Certification: Database in AWS

RDS

• Managed PostgreSQL, MySQL, Oracle, SQL Server, DB2, MariaDB, Custom

• Provisioned RDS Instance Size and EBS Volume Type & Size

• Auto-scaling capability for Storage

• Support for Read Replicas and Multi-AZ (for HA and have a standby database)

• Security through IAM, Securities Groups, KMS, SSL in transit

• Automated Backup with Point in time restore feature (up to 35 days)

• Manual DB Snapshot for longer-term recovery

• Managed and Scheduled maintenance (with downtime)

• Support for IAM Authentication, integration with Secrets Manager

• RDS Custom for access to and customize the underlying instance (Oracle & SQL Server)

Use cases: store relational datasets (RDMBS/OLTP), perform SQL queries, transactions

Aurora

• Compatible API for PostgreSQL / MySQL, separation of storage and compute

• Storage: data is stored in 6 replicas, across 3 AZ - HA, self-healing, auto-scaling

• Compute: Cluster of DB Instance across multiple AZ, auto-scaling of Read Replicas

• Cluster: Custom endpoints for writer and reader DB instances

• Same security/monitoring/maintenance features as RDS

• Know the backup & restore options for Aurora

• Aurora Serverless - for unpredictable workloads

• Aurora Global: up to 16 DB Read Instances in reach region, < 1 second storage replication

• Aurora Machine Learning: perform ML using SageMaker & Comprehend on Aurora

• Aurora Database Cloning: new cluster from existing one, faster than restoring a snapshot

Use cases: same as RDS, but with less maintenance, more flexibility, more performance, more features

ElastiCache

• Managed Redis / Memcached

• In-memory data store, sub-millisecond latency

• Support for Clustering (Redis) and Multi-AZ, Read Replicas (sharding)

• Security through IAM, Security Groups, KMS, Redis Auth

• Backup / Snapshot, Point in time restore feature

• Manage and Schedule maintenance

• Requires some application code changes to be leveraged

Use cases: Key/Value store, frequent reads, less writes, cache results for DB queries, store session data for websites, cannot use SQL

DynamoDB

• AWS proprietary technology, managed serverless NoSQL db, millisecond latency

• Capacity modes: provisioned capacity with optional auto-scaling or on-demand capacity

• Can replace ElastiCache as a key/value store

• HA, multi-AZ by default, Read and Writes are decoupled, transaction capability

• DAX cluster for read cache, microsecond read latency

• Security, authentication/author is done through IAM

• Event Processing: DynamoDB Streams to integrate with Lambda or Kinesis Data Streams

• Global Table feature: active-active setup

• Automated backups up to 35 days with PITR (point-in-time recovery), or on-demand backups

• Export to S3 without using RCU in the PITR window, import from S3 without using WCU

• It is great to evolve schemas rapidly

• Use case: serverless applications development (small document 100s KM), distributed serverless cache

S3

• Great for bigger objects, not so great for many small objects

• Serverless, scales infinitely, max object size is 5 TB, version capability

• Tiers: S3 Standard, S3 IA, S3 Intelligent, S3 Glacier + lifecycle policy

• Features: versioning, encryption, replication, MFA-Delete, Access Logs,…

• Security: IAM, bucket policies, ACL, Access Points, Object Lambda, CORD, Object/Vault Lock

• Encryption: SSE-S3, SSE-KMS,…

• Batch operations on objects using S3 Batch, listing files using S3 Inventory

• Performance: Multi-part upload, S3 Transfer Acceleration, S3 Select

• Automation: S3 Event notifications (SNS, SQS, Lambda, EventBridge)

Use cases: static files, key valuie store for big files, website hosting

DocumentDB

• Is the same for MongoDB (like Aurora for MongoDB)

• Similar deployment concepts to Aurora

• Fully managed, HA with replication across 3 AZ

• DocumentDB storage automatically grows in increments of 10 GB

• Automatically scales to workloads with millions of requests per second

Neptune

• Full-managed graph database

• A popular graph database would be a social network

• HA across 3 AZ, 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

• Great for knowledge graphs, fraud detection, recommendation engines, social networking

• Support for Streams (like Dynamo Data Streams)

  • Send notifications when certain changes are made

  • Maintain graph data synchronized in another data store (S3, OpenSearch,…)

  • Replicate data across regions in Neptune

Keyspaces (for Apache Cassandra)

• Cassandra is an open-source NoSQL distributed database

• A managed Apache Cassandra-compatible database service

• Serverless, Scalable, HA, fully managed by AWS

• Automatically scale tables up/down based on the application’s traffic

• Tables are replicated 3 times across multiple AZ

• Using the Cassandra Query Language (CQL)

• 100s of requests per second

• Capacity: on-demand mode or provisioned mode with auto-scaling

• Encryption, backup, Point-In-Time recovery up to 35 days

Use cases: store IOT devices info, time-series data,…

Quantum Ledger Database

• A ledger is a book recording financial transactions

• Fully managed, serverless, HA, replication across 3 AZ

• Used to review the history of all the changes made to 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

TimeStream

• Full-managed, fast, scalable, serverless time series database

• Automatically scales up/down to adjust capacity

• Store and analyze trillions of events per day

• 100s time faster & 1/10 the cost of relational databases

• Scheduled queries, multi-measure records, SQL compatibility

Use cases: IoT apps, operatinal applications, real-time analytics,…