Usually data modeling is independent of the query language used to implement that data model. When CQL3 was introduced, however, it added a relational-database-centric abstraction that hides many key details of the underlying storage. What’s worse, many data modeling articles reference the deprecated Thrift interface, making it difficult to transfer their wisdom into CQL3. Though CQL can be an efficient and convenient tool to use when querying, knowing how CQL actually maps to Cassandra’s storage structure is key to being able to create scalable and flexible data models.
As needs continue to increase for highly-scalable applications (e.g. Internet of Things, Big Data Collect Everything), Cassandra provides an excellent solution when high-concurrency writes, no-master, near-linear-scalability, and user-chosen consistency are a must. In the broad family of NoSQL data stores, Cassandra is distinct from the document-store model of MongoDB.
Knowing data modeling for relational databases or for document stores does not transfer well to Cassandra; some anti-patterns of those systems are core patterns in Cassandra. Data modeling in relational databases is primarily about the normalization of the data being stored. With Cassandra, data modeling must also take into account the primary access patterns and, in many cases, denormalize certain areas.
Topics we will cover include:
Mike Biglan, an avid technologist, currently heads two Oregon-based startups: Analytic Spot (analyticspot.com), a SaaS service for collecting and visualizing educational analytics for mobile Apps, and Twenty Ideas (twentyideas.com), a software consulting, architecture, and development agency. Before that, Mike was the lead technologist of two highly-successful companies: Silicon Valley startup Happy Bits, maker of the AnyVideo/Evercam/Joya line of iOS and Android Apps (e.g. http://bit.ly/18yaQVa); and Concentric Sky (concentricsky.com), where he oversaw a quadrupling of the size of the company, achieved high employee retention, and provided a strong and collaborative culture. With a background in software architecture and development, computer science, bioinformatics, statistics, education, psychology, and economics, Mike returned to Eugene in 2005 after living in Chicago (B.A. in economics at the University of Chicago), Washington DC, and San Diego (M.S. in Computer Science and Engineering at UCSD). He has also served on several boards such as the Promise Neighborhoods Research Consortium (promiseneighborhoods.org) and the City of Eugene’s budget committee and also has spoken at OSCON and DjangoCon.
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