# 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,…