Viktor Gamov covers In-Memory technology, distributed data topologies, making in-memory reliable, scalable and durable, when to use NoSQL, and techniques for Big In-Memory Data.
Christopher Meiklejohn looks at applying two techniques together, deterministic data flow programming and conflict-free replicated data types, to create highly available and fault-tolerant systems.
Howard Chu covers highlights of the LMDB design and discusses some of the internal improvements in slapd due to LMDB, as well as the impact of LMDB on other projects.
Piotr Kołaczkowski discusses how they integrated Spark with Cassandra, how it was done, how it works in practice and why it is better than using a Hadoop intermediate layer.
Andrew Kennedy talks about the reasons for creating a Docker cloud and how Clocker was born.
Kristoffer Dyrkorn presents the experiences gained by the Norwegian Public Roads Administration in building a new infrastructure for road traffic measurements.
Ken Kousen discusses combining various technologies: Groovy, Ratpack, MongoDB, Grails, REST.
Emily Green is taking a look at how SoundCloud uses Cassandra. She describes a couple of Cassandra instances, from the point of view of the products and functionality they support.
Thore Thomassen shares from experience how to combine structured data in a DWH with unstructured data in NoSQL, and using parallel data warehouse appliances to boost the analytical capabilities.
Julien Le Dem discusses the advantages of a columnar data layout, specifically the features and design choices Apache Parquet uses to achieve goals of interoperability, space and query efficiency.
James Richardson, Nat Pryce discuss some of the challenges faced using Neo4J for interactive analysis of large data imports (80K nodes, 150k relationships) and how they overcame them.
Dean Wampler takes a look at SQL’s resurgence and specific example technologies, including: NewSQL, Hybrid SQL, SQL abstractions on top of file-based data, SQL as a functional programming language.
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