Technical Tips

How to Build “Stanzas” Using MapR Installer for Easy and Efficient Provisioning

At MapR, we have set up multiple clusters for several of our enterprise customers, and we have brought that knowledge and best practices to MapR Installer. Increasingly, these deployments have not only grown in number, but have also evolved based on the type, purpose, and lifetime for these clusters.

Using Spark GraphFrames to Analyze Facebook Connections

Sooner or later, if you eyeball enough data sets, you will encounter some that look like a graph, or are best represented a graph. Whether it's social media, computer networks, or interactions between machines, graph representations are often a straightforward choice for representing relationships among one or more entities.

The Ultimate 3-Minute Guide to Time Series Data and OpenTSDB

What is a time series? A time series is a sequence of data points which are ordered in time. Time series data can come in multiple shapes, and can be used in many facets of everyday life, such as measuring rainfall, earthquake activity, or even stock prices. With the growth of the Internet of Things, the volume of time series data you can collect is staggering - reaching 100 million data points per second.

Four Examples of Characterizations for Discovery from Big Data

We previously discussed the “Top 8 Reasons that Characterization is Right for Your Data.” Here we move the discussion of characterization from the theoretical to the practical, by providing four simple examples of characterizations of data. In each of these cases, the set of characterizations that are generated can then be fed into different types of analytics algorithms for discovery from your data: predictive patterns, clusters (segments), associations, correlations, trends, and anomalies (outliers, surprises).


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