#Databases

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Relational Databases (SQL)	Ideal choice when your data is structured and needs to be consistent Supports ACID transactions and complex relational queries Examples: MySQL, PostgreSQL, CockroachDB
Document Databases	Handles semi-structured data with the possibility of different fields for each document. Provides an amazing level of schema flexibility Examples: MongoDB, Couchbase
Key-Value Store	Use when the data model is based on key-value pairs. Ideal for fast data retrieval and high throughput. Examples: Redis, DynamoDB
Graph Databases	Excellent choice for data with complex relationships. Used in applications such as recommendation engines and navigation maps. Examples: Neo4j, Amazon Neptune.
Time-Series Databases	Perfect choice when dealing with time-series data like IoT sensor readings or server logs in DevOps. Provides efficient storage and retrieval of time-stamped data. Examples: InfluxDB, Prometheus.
Columnar Databases	Data is stored by columns instead of rows to optimize reading from a column. Great for applications that involve storing massive data sets and running analytical queries. Examples: Amazon Redshift, Snowflake
In-Memory Databases	Ideal for cases when speed is more important than persistence. Used for caching, real-time analytics, and high-frequency trading. Examples - Redis and Memcached
Search Engines	Great for situations when you need to support full-text search on your dataset. Essential for applications that require searching through large amounts of data Examples - Elasticsearch & Solr
Spatial Databases	Used for storing geographical and location-based data. Extended on top of traditional databases. Choose for applications that require Spatial indexing and geospatial analytics. Examples include PostGIS & Oracle Spatial
Blob Datastore	For applications that need to store large documents, images, audio and video files. They provide high availability, durability and cost-effective storage. Examples include Azure Blob Storage, Amazon S3
Ledger Databases	Specialized databases for recording and maintaining tamper-evident and immutable history of transactions. They use cryptographic techniques like hashing & chaining to ensure data integrity. Examples: Amazon QLDB, Azure SQL Ledger

Vector DB Vs. Traditional DB         Structured, Semi Structured, and Unstructured Data