 - It is a type of database that organizes data into tables. 

- Multiple entries with relational constraints and are typically designed to be ACID compliant. 

 - Designed to handle large volumes of structured, semi-structured, or unstructured data, and offer features such as high availability, horizontal scalability, and flexible data models. 

 - In this model, each piece of data (a value) is associated with a unique identifier (a key). 

 - Key-value stores are designed for high-speed retrieval and storage of data, making them suitable for use cases where fast access to data is crucial. 

🔹<strong>Column-family stores</strong>: 

 - stores data in columns rather than rows, enabling efficient retrieval of data subsets. 

 - Used to perform scan on entire key space, filtering etc. 

- Examples: Apache Cassandra, HBase, Amazon DynamoDB. 

🔹<strong>Document-oriented databases</strong>: 

 - These databases store data in flexible, semi-structured document formats like JSON or BSON. 

 - Schema definition is not required, addition of new fields is easier. 

- Used in inventory, content management etc. - Examples: MongoDB, Amazon DocumentDB. 

 - designed to represent and store data as graphs, consisting of nodes and edges. Nodes represent entities or data points, while edges represent the relationships between these entities. 

- Used in applications that involve complex relationships such as social networks, recommendation systems, fraud detection etc.

 - Several databases are specifically designed for search-related tasks, ranging from basic full-text search to more advanced search capabilities. 

 - Amazon CloudSearch: It offers features such as full-text search and real-time indexing. It is fully managed and scalable, making it easy to set up and operate for a variety of search applications. 

- Elasticsearch: It's widely used for full-text search, log analytics, and real-time analytics. It supports complex search queries and distributed indexing, making it suitable for search engines, e-commerce platforms etc. 

  - Type of database system designed to handle and process continuous streams of data in real-time. 

 - Optimized for handling high-throughput, time-sensitive data streams generated by various sources, such as devices, sensors etc. 

- Examples: Apache Kafka, Kinesis etc.&nbsp;&nbsp;

Data storage, retrieval and querying is a central piece in most of systems. Database choice clearly becomes a crucial decision while designing a system in terms of scalability, consistency etc.

Why database design choice matters?

Senior Software Engineer

Member Of Technical Staff 2

Member of Technical Staff 1

Shristi Katyayani



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