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In this talk we share our story about our journey into polyglot persistence: the use of several types of data stores that are each a good match for a particular part of information processing problem.
As we started embracing Big Data and NoSQL across a number of projects, it quickly became clear that one technology is not going to be a solution for all of our needs. We begin by outlining the issues relational technology has with scalability and new data formats. We then illustrate the examples of dominant NoSQL technologies and how they fit into the big picture.
We present the Hadoop and MapReduce as a dominant solutions to the Big Data problem. Distributed Key/Value and columnar stores like Cassandra help with their near real-time capabilities and scalability. We discuss the flexibility of document data stores like MongoDB and CouchDB and briefly explore the features of graph data stores. From the enterprise point of view, a mix of data stores presents a particular set of challenges that must be addressed.
We will show how to productively bring in NoSQL systems into the enterprise, including classical reporting systems and integration strategies with relational systems. You will benefit from getting a clear picture of what type of NoSQL data store is a good match for data processing piece of puzzle.
- What is polyglot persistence?
- The relational database problems
- Taming big data with Hadoop and Map Reduce
- Scalability with Key/Value and Columnar stores
- Flexibility of Document stores
- Finding connections with Graph databases
- Data Governance for NoSQL
- NoSQL and Master Data Management
- NoSQL integration strategies