SQL Vs NoSQL | Data Models | AWS RDS vs Dynamo DB

#awsrds #dynamodb #aws #sql #nosql #redis #keyvalue #graphbased #columnbased

SQL (Structured Query Language) and NoSQL (Not Only SQL) are two different types of database management systems, each with their own strengths and weaknesses.

SQL databases are relational databases, which means that they organize data into tables, with each table consisting of rows and columns. SQL databases use a schema, which is a blueprint that defines the structure of the database, including the data types and relationships between the tables. SQL databases are known for their ability to handle complex queries and their strong support for transactions, making them a good choice for applications that require data integrity, such as financial or e-commerce applications.

NoSQL databases, on the other hand, are non-relational databases that store data in a more flexible, schema-less manner. NoSQL databases use a variety of data models, such as document-oriented, key-value, column-family, or graph, depending on the needs of the application. NoSQL databases are known for their ability to scale horizontally, meaning that they can handle large amounts of data and traffic by adding more servers to the system. NoSQL databases are a good choice for applications that require high scalability and performance, such as social networks, gaming, or big data analytics.

In summary, SQL databases are ideal for applications that require a high degree of data consistency and support for complex queries, while NoSQL databases are ideal for applications that require high scalability and performance with flexible data models.

Data Model: Amazon RDS is a relational database service, which uses SQL and organizes data into tables with columns and rows. DynamoDB is a NoSQL database service that stores data in the form of key-value pairs or as a document-oriented data model.

Scalability: Amazon RDS provides vertical scaling, which means that you can increase the power of a single database instance. DynamoDB, on the other hand, provides horizontal scaling, which means that you can add more servers to increase throughput and capacity.

Pricing: Amazon RDS pricing is based on the instance type and the storage capacity, while DynamoDB pricing is based on the provisioned throughput capacity, storage capacity, and data transfer.

Data Durability: DynamoDB is designed to provide 99.999% durability for all data stored, while RDS durability is based on the underlying storage service used.

Data Consistency: RDS provides strong consistency, which ensures that all data is up-to-date across all nodes. DynamoDB, however, provides eventually consistent reads by default, which means that data may not be immediately up-to-date on all nodes.

Query Language: RDS uses SQL for querying data, while DynamoDB uses a proprietary query language called AWS Query API.

Transactions: RDS supports ACID (Atomicity, Consistency, Isolation, Durability) transactions, while DynamoDB does not provide multi-table transactions.

Secondary Indexes: DynamoDB supports global and local secondary indexes, while RDS supports only local secondary indexes.

Use Cases: Amazon RDS is suitable for applications that require a relational database schema, while DynamoDB is suitable for applications that require a flexible schema and high performance at scale.

Managed Service: Amazon RDS is a fully managed service that handles database administration tasks, while DynamoDB is a fully managed service that handles both database administration and scaling.