analytics: All content tagged as analytics in NoSQL databases and polyglot persistence
A great matrix of the different analytics use cases across industries in Hortonworks’s post “Enterprise Hadoop and the Journey to a Data Lake“:
The data type column section covers multiple dimensions of data. And the authors took a conservative approach for the structured and unstructured categories (in the sense that they marked very few categories as unstructured).
A couple of interesting exercises that can be done using this matrix as an input:
figure out how adding data from different categories to a specific use case would benefit it. One obvious example is: how would Telecom companies benefit from adding to their infrastructure analysis social data?
Building on the above, decide what tools exist to help with this extra scenario.
can one use case from an industry be applied to a different industry to disrupt it?
What would be the quickest road to accomplish it?
Original title and link: Examples of analytics applications across industries ( ©myNoSQL)
Arnold Matyasi posted 4 articles (with Clojure code, charts, and explanations) on how to analyze Google Analytics data locally with Clojure, Incanter, and MongoDB:
- Part 1: exporting data, setup, Clojure helper functions
- Part 2: first charts
- Part 3: grouping data
- Part 4: implementing weighted sort
Original title and link: Exploring Google Analytics Data With Clojure, Incanter, and MongoDB ( ©myNoSQL)
Ron Bodkin interviewed by Michael Floyd over InfoQ describes the Hadoop growing addiction:
People are using Hadoop for a variety of analytics. Many of the first uses of Hadoop are complementing traditional data warehouses I just mentioned, where the goal is to take some of the pressure of the data warehouse, start to be able to process less structured data more effectively and to be able to do transformations and build summaries and aggregates, but not have to have all that data loaded to the data warehouse. But then the next thing that happens is once people have started doing that level of processing they realize there is a power of being able to ask questions they never thought of before the data, they can store all the data in small samples and they can go back and have a powerful query engine, a cluster of commodity machines that lets them dig into that raw data and analyze it new ways ultimately leading to data science being able to do machine learning and being able to discover patterns in data and keep them improving and refining the data.
The interview is only 16 minutes long and you have the full transcript.
Original title and link: Hadoop and NoSQL in a Big Data Environment with Ron Bodkin ( ©myNoSQL)