Big data
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The document discusses using Cassandra and Hadoop for storing and analyzing massive amounts of data from a survey. Cassandra is presented as a good option for its scalability and performance, but requires careful data modeling. Hadoop is useful for answering ad-hoc questions via MapReduce but is slow. An ideal solution would leverage both Cassandra for storage and Hadoop's analysis capabilities via tools like Hive.
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C* for Deep Learning (Andrew Jefferson, Tracktable) | Cassandra Summit 2016
A deep learning startup has a requirement for a robust and scalable data architecture. Training a Deep Neural Network requires 10s-100s of millions of examples consisting of data and metadata. In addition to training it is necessary to support test/validation, data exploration and more traditional data science analytics workloads. As a startup we have minimal resources and an engineering team of 1.
Cassandra, Spark and Kafka running on Mesos in AWS is a scalable architecture that is fast and easy to set up and maintain to deliver a data architecture for Deep Learning.
About the Speaker
Andrew Jefferson VP Engineering, Tractable
A software engineer specialising in realtime data systems. I've worked at companies from Startups to Apple on applications ranging from Ticketing to Genetics. Currently building data systems for training and exploiting Deep Neural Networks.
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- 21. Cassandra is eventually consistent makes this hard
- 22. Be clever and you will win
- 23. Demo 2
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- 29. Reduce - function that merges all intermediate values associated with the same intermediate key
- 31. cassandra asher
- 33. Redis 1 AND the winner is cassandra w/ 2 votes!!!
- 38. Dangerous if you don't know what you are doing
- 39. Schemaless – ironically modelling is extermely imp.