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DPC2021: Beyond Relational: Data storage for modern applications - Mike Lehan
Explore the world of data storage beyond relational databases, discussing key-value stores, document storage, time series databases, and more, and learn how to choose the right database for your application's specific needs.
- Key-Value Stores are optimized for fast read operations and do not have relationships between data.
- They can be used for caching, and Redis is a popular option.
- Document Storage (e.g., MongoDB) is useful for storing JSON data and allows for efficient querying.
- Time Series Databases are optimized for storing a large amount of data with a timestamp.
- Indexes are important for efficient querying in databases.
- Transactions are important for ensuring data consistency and atomicity.
- Locks can be used to prevent concurrent updates, but can also cause deadlocks.
- Queues can be used to decouple producers and consumers, and SQS is a popular option.
- Webhooks can be used to send notifications to external services, but can be complex to implement.
- Exactly-once processing is important for ensuring data consistency in distributed systems.
- Tools like RabbitMQ and Redis can be used to implement message queues and caching.
- Choosing the right database for the job is important, and there is no one-size-fits-all solution.
- Considerations such as scalability, data consistency, and query performance should be taken into account when selecting a database.
- Relational databases may not be the best fit for all problems, and alternative databases like NoSQL databases may be more suitable.
- The choice of database will depend on the specific requirements of the application and the trade-offs that need to be made.