AWS Databases And Key differences

This section provides an overview of AWS Databases And Key differences.

Type: Relational Database (SQL)
Supported Engines: MySQL, PostgreSQL, MariaDB, Oracle, Microsoft SQL Server, and Amazon Aurora.
Use Case: Traditional relational databases with structured data and transactional requirements.
Key Features:
- Managed service with automated backups, patching, and replication.
- Supports Multi-AZ deployments for high availability.
- Read replicas for scaling read operations.
Performance: Suitable for OLTP (Online Transaction Processing) workloads.
Scalability: Vertical scaling (instance resizing) and horizontal read scaling via read replicas.
Cost: Pay-as-you-go pricing with Reserved Instances for cost savings.

Type: Relational Database (SQL)
Supported Engines: MySQL and PostgreSQL-compatible.
Use Case: High-performance relational databases with scalability and availability.
Key Features:
- Up to 5x faster than standard MySQL databases and 3x faster than PostgreSQL.
- Distributed, fault-tolerant, self-healing storage system.
- Automated backups, snapshots, and point-in-time recovery.
- Global database for multi-region, low-latency reads.
Performance: Optimized for both OLTP and OLAP (Online Analytical Processing) workloads.
Scalability: Auto-scaling for storage up to 128 TB and horizontal read scaling with Aurora replicas.
Cost: Slightly higher than standard RDS due to enhanced performance and features.

Type: NoSQL (Key-Value and Document)
Use Case: High-performance, low-latency applications requiring flexible schema and scalability.
Key Features:
- Fully managed, serverless with automatic scaling.
- Millisecond response times at any scale.
- Global tables for multi-region, active-active replication.
- Supports transactions for ACID compliance.
Performance: Optimized for low-latency read/write operations.
Scalability: Horizontal scaling with no limits on data size or throughput.
Cost: Pay-per-request pricing model with provisioned or on-demand capacity modes.

Type: Data Warehouse (SQL)
Use Case: Large-scale data analytics and business intelligence workloads.
Key Features:
- Columnar storage for efficient data compression and retrieval.
- Massively parallel processing (MPP) for high performance.
- Integration with BI tools and machine learning services.
- Spectrum allows querying data directly in S3.
Performance: Optimized for complex queries and large data sets.
Scalability: Scales up to petabytes of data across multiple nodes.
Cost: Pay-as-you-go with reserved node pricing options.

Type: In-Memory Data Store (Redis and Memcached)
Use Case: Caching, session storage, real-time analytics, and gaming leaderboards.
Key Features:
- Fully managed with support for Redis and Memcached.
- Sub-millisecond latency with high throughput.
- Supports data persistence (Redis) and replication for high availability.
Performance: Optimized for high-speed data access with low latency.
Scalability: Horizontal scaling with clustering (Redis) and sharding.
Cost: Pay-as-you-go with costs depending on node type and cluster configuration.

Type: Graph Database (Property Graph and RDF)
Use Case: Applications requiring complex relationships and graph traversal, such as social networks, recommendation engines, and fraud detection.
Key Features:
- Supports popular graph models: Property Graph and RDF.
- Fully managed with automated backups, patching, and replication.
- ACID-compliant with fast graph queries.
- Multi-AZ deployments for high availability.
Performance: Optimized for graph traversal queries.
Scalability: Scales up to billions of relationships and handles up to hundreds of thousands of queries per second.
Cost: Pay-as-you-go with pricing based on instance type and storage.

Type: Time Series Database
Use Case: Applications that require storage and real-time analysis of time series data, such as IoT, telemetry, and event tracking.
Key Features:
- Fully managed and serverless with built-in time series analytics functions.
- Automatically scales up or down based on data volume.
- Ingestion, retention, and querying optimized for time series data.
Performance: Optimized for real-time data ingestion and complex time series queries.
Scalability: Automatically scales based on workload demands.
Cost: Pay only for the data ingested, stored, and queried.

Type: Ledger Database
Use Case: Applications requiring an immutable, transparent, and cryptographically verifiable transaction log, such as financial systems, supply chains, and regulatory compliance.
Key Features:
- Fully managed ledger database with ACID transactions.
- Cryptographically verifiable transaction history.
- Append-only journal that stores every change to application data.
Performance: Optimized for high-throughput and low-latency transactions.
Scalability: Automatically scales to accommodate growing transaction volumes.
Cost: Pay-as-you-go with costs based on read/write IOPS, data storage, and journal storage.

Type: NoSQL Document Database (MongoDB Compatible)
Use Case: Applications requiring a scalable, highly available, and fully managed document database service compatible with MongoDB workloads.
Key Features:
- Compatible with MongoDB drivers and tools.
- Fully managed with automated backups, replication, and scaling.
- ACID transactions and high availability with multi-AZ support.
Performance: Optimized for handling semi-structured data and complex queries.
Scalability: Vertical scaling by adjusting instance size and horizontal scaling with replica sets.
Cost: Pay-as-you-go with pricing based on instance type, IOPS, and storage.

Type: NoSQL Columnar Database
Use Case: Applications requiring a scalable, highly available, and fully managed Cassandra-compatible database.
Key Features:
- Compatible with Apache Cassandra APIs.
- Serverless with automatic scaling based on workload.
- Managed service with built-in security, backups, and replication.
Performance: Optimized for low-latency reads and writes with columnar data storage.
Scalability: Seamless scaling with on-demand capacity mode.
Cost: Pay-as-you-go pricing based on read/write requests, data storage, and throughput.

RDS and Aurora are best for traditional relational database needs, with Aurora providing higher performance and scalability.
DynamoDB is ideal for NoSQL workloads that require high performance and scalability, especially in key-value and document data models.
Redshift is designed for big data analytics and data warehousing.
ElastiCache provides high-speed, in-memory data storage for caching and real-time applications.
Neptune is optimized for graph database use cases where relationships and traversals are key.
Timestream is specialized for time series data, commonly used in IoT and telemetry applications.
QLDB offers a cryptographically verifiable ledger for immutable records.
DocumentDB provides a managed MongoDB-compatible environment.
Keyspaces offers managed Cassandra-compatible database service for columnar data.

VPC Interview Questions