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By Casey Rosenthal

What is the best data storage option for your application? We have an abundance of conventional wisdom when it comes to building applications on top of a relational database in the Ruby world. Building an application on top of a NoSQL database is a different story. I will present a conceptual framework for understanding Access Patterns that jives with properties of databases, then review common NoSQL databases and propose considerations for choosing one over another. I will also review some uncommon NoSQL databases that address common use cases, and suggest that perhaps some of these should be used more often. Most importantly, I will describe the different state of mind that you should have when building applications on top of these NoSQL options, and provide visualization of non-relational concerns like: fault tolerance, availability, consistency, capacity planning, and horizontal vs vertical scaling. Whether or not you choose a NoSQL option for a future project, you won't look at data storage options in the same way after this presentation. ;-)

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In the presentation titled "Data Storage: NoSQL Toasters and a Cloud of Kitchen Sinks," Casey Rosenthal discusses the evolving landscape of data storage, particularly emphasizing NoSQL databases. Starting with a humorous analogy involving toasters, he highlights the oversimplification of the term 'NoSQL' and invites the audience to reframe it as an opportunity for diverse choices in data storage.

**Key Points Discussed:**  
- **NoSQL Overview:** Rosenthal outlines the historical context of data storage, contrasting traditional relational databases with the new options provided by NoSQL technology, which have emerged due to the limitations of SQL databases in certain use cases.  
- **Common Participants:** He reviews popular NoSQL databases, including:  
  - **MongoDB:** A document database that stores data in tree-structure documents.  
  - **Redis:** An in-memory key-value store that prioritizes speed.  
  - **Cassandra & HBase:** Column-family databases that offer different ways of structuring data.  
  - **Neo4j:** A graph database, making complex relationships easier to manage.  
  - **Riak:** A distributed key-value store with flexibility in data formats.  
  - **Other Mentioned Databases:** CouchDB, Couchbase, and DynamoDB, among others.  
- **Emerging Use Cases:** He introduces lesser-known NoSQL databases such as Titan (a distributed graph database), Exist DB (XML storage), and Atomic (offering cache and transaction efficiency).  
- **Critical Reasons to Consider NoSQL:**  
  - **Fault Tolerance:** Designed to handle hardware failures and ensure data availability across multiple nodes.  
  - **High Availability:** Supports simultaneous operations across nodes to maintain function during outages.  
  - **Scalability:** Highlights both vertical and horizontal scaling, noting the appeal of horizontal scaling in NoSQL databases.  
- **Data Growth Challenges:** Stresses the crucial need for scalable solutions in a landscape where data is exponentially increasing.  
- **Access Patterns Framework:** Discusses the importance of understanding different access patterns when designing applications with NoSQL, and the need to shift dynamic patterns to static ones for efficiency.  
- **Conclusion:** Emphasizes the absence of a universal theory of data, reinforcing that selecting the correct database relies on experience and understanding of specific application needs. Rosenthal encourages audience members to explore NoSQL solutions to better manage data effectively in modern applications.

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