Databases I should learn about beside traditional relational databases

Given your experience in web application development and familiarity with traditional relational databases, here are some other databases you might find valuable to know about:

1. NoSQL Databases

• MongoDB: A document-oriented database that stores data in flexible JSON-like formats. It’s great for unstructured data and is often used for applications requiring high scalability.
• Cassandra: A distributed wide-column store designed for high availability and scalability. It's ideal for handling large volumes of data across many servers without a single point of failure.
• Redis: An in-memory key-value store known for its speed. It’s commonly used for caching, session management, and real-time analytics.

2. Graph Databases

• Neo4j: A graph database designed to handle connected data and relationships efficiently. Useful for applications like social networks, recommendation systems, and fraud detection.
• ArangoDB: A multi-model database that supports graph, document, and key-value data models, allowing for flexibility in data representation.

3. Time-Series Databases

• InfluxDB: Optimized for time-series data, making it ideal for monitoring applications, IoT, and real-time analytics.
• Prometheus: Primarily used for monitoring and alerting, it’s also a time-series database that collects metrics from configured targets at specified intervals.

4. NewSQL Databases

• CockroachDB: A distributed SQL database that aims to provide the scalability of NoSQL databases while maintaining the familiarity of SQL.
• Google Spanner: A globally distributed, horizontally scalable database that combines the benefits of relational databases with NoSQL scalability.

5. Search Engines

• Elasticsearch: A distributed search and analytics engine based on Apache Lucene. It's commonly used for log and event data analysis, as well as full-text search.
• Apache Solr: Similar to Elasticsearch, Solr is another search platform built on Lucene, often used for enterprise search applications.

6. Data Warehousing Solutions

• Snowflake: A cloud-based data warehousing solution that allows for easy scaling and querying of large datasets.
• Amazon Redshift: A fully managed data warehouse service in the cloud that makes it easy to analyze large datasets using SQL.

7. Other Relational Databases

• SQLite: A lightweight, serverless database commonly used in mobile applications and small to medium-sized applications.
• Oracle Database: A powerful, enterprise-level relational database with robust features for transaction management, data warehousing, and analytics.

Conclusion

Familiarizing yourself with these databases can enhance your ability to choose the right tool for different scenarios, improve application performance, and broaden your understanding of modern data architectures. Each database has its strengths, so knowing a bit about their use cases and features can be highly beneficial in your development work.