Choose the suitable database to map the right workload
A comprehensive database selection guide across cloud providers and use cases. Let me break down its key components:
The image is organized into three main data categories:
1. Structured Data
2. Semi-Structured Data
3. Unstructured Data
For each category, it shows database options across four platforms:
- AWS (Amazon Web Services)
- Azure (Microsoft)
- GCP (Google Cloud Platform)
- Cloud Agnostic solutions
For Structured Data, it's divided into:
- ACID Transactions (OLTP)
- ACID + Scalability
- Analytics (OLAP)
For Semi-Structured Data, it shows use cases for:
- Dictionary (Key-Value stores)
- Cache (In-memory)
- Column-Oriented
- Time Series
- Embedding (Vector)
- Location & Geo-entities
- Entity Relationships
- Nested Objects (XML, JSON)
For Unstructured Data, it covers:
- Full Text Search
- Raw Text, Image, Video (Blob storage)
Some key database options highlighted include:
- RDS, Aurora, RedShift (AWS)
- Azure SQL Database, Cosmos DB, Azure Synapse (Azure)
- Cloud SQL, BigQuery, Bigtable (Google Cloud)
- Various cloud-agnostic options like MongoDB, Redis, Elasticsearch
The diagram is color-coded:
- Yellow section for structured data solutions
- Blue section for semi-structured data solutions
- Green section for unstructured data solutions
This is a valuable reference for architects and developers who want to choose the appropriate database solution based on their specific use case and preferred cloud platform.