What are the most common NoSQL databases and how do they differ from SQL databases?

1 Key features of SQL databases

SQL databases are based on the relational model, which organizes data into tables with rows and columns. Each table has a predefined schema that specifies the attributes and constraints of the data. SQL databases use the Structured Query Language (SQL) to manipulate and query the data. SQL databases provide strong consistency, which means that all transactions are atomic, consistent, isolated, and durable (ACID). SQL databases are suitable for applications that require complex joins, transactions, and analytical queries.

2 Key features of NoSQL databases

NoSQL databases are based on the non-relational model, which allows data to be stored in various formats, such as key-value pairs, documents, graphs, or columns. Each format has its own advantages and disadvantages for different types of data and queries. NoSQL databases do not have a fixed schema, which means that the data can be dynamic and heterogeneous. NoSQL databases use different query languages or APIs to access and manipulate the data. NoSQL databases provide weak or eventual consistency, which means that not all transactions are ACID, but they can achieve higher availability and scalability. NoSQL databases are suitable for applications that handle large volumes of unstructured or semi-structured data, such as social media, e-commerce, or real-time analytics.

3 Types of NoSQL databases

NoSQL databases come in four main types: key-value, document, column, and graph. Each of these has its own characteristics and use cases. Key-value databases, for example, store data as pairs of keys and values, where the key is a unique identifier and the value can be any type of data. They are fast and simple, but do not support complex queries or relationships - examples include Redis, DynamoDB, and Memcached. On the other hand, document databases store data as documents, which are collections of fields and values. They are flexible and expressive but do not support joins or transactions - MongoDB, CouchDB, and Firestore are examples of document databases. Additionally, column databases store data as columns that share the same attribute - Cassandra, HBase, and Bigtable are some examples. Lastly, graph databases store data as nodes and edges that represent entities and relationships. They are powerful and intuitive but are not suitable for large or distributed data - Neo4j, OrientDB, and ArangoDB are some graph database examples.

4 How to choose between SQL and NoSQL databases

When deciding which type of database is best for a particular application, there is no one-size-fits-all answer. However, some general factors to consider include data structure, query complexity, scalability, and consistency. If the data is structured and consistent, SQL databases are a good choice; however, if the data is unstructured or semi-structured, NoSQL databases are more flexible and adaptable. Additionally, if the queries are complex and involve joins, transactions, or aggregations, SQL databases are more expressive and reliable; whereas if the queries are simple and involve key-based lookups or single-document operations, NoSQL databases are faster and simpler. Furthermore, if the data size and traffic are predictable and moderate, SQL databases are easier to manage and optimize; but if the data size and traffic are unpredictable and high, NoSQL databases are more scalable and resilient. Lastly, if the data integrity and accuracy are critical, SQL databases are more consistent and secure; on the other hand, if the data availability and performance are more important, NoSQL databases are more tolerant and responsive.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?