Big data is data sets that are so voluminous and complex that traditional data-processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. There are a number of concepts associated with big data: originally there were 3 concepts volume, variety, velocity. Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) and value.

Graph databases for the clueless.
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Orbifold2019-11-02 08:31:452019-11-02 08:31:45What is a graph database?
Using MLLib naive Bayes for spam classification.
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Orbifold2019-08-16 11:19:352019-08-22 18:28:51Classic spam classification using Spark MLLib
A five mins bitcoin-ish implementation to understand it.
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Orbifold2018-08-23 10:40:242018-08-23 10:40:24Poor man’s blockchain
By expanding a time series horizontally you can use H2O to forecast it.
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Orbifold2018-07-23 06:03:242018-07-23 06:03:24Timeseries forecasting with H2O
Using cognitive computing to help intelligence officers be more efficient.
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Orbifold2018-07-09 06:53:422018-07-09 07:58:10Augmented intelligence, terrorism and consultancy
Large scale graph analytics with Spark and Apache GraphFrames.
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Orbifold2018-07-07 15:20:542018-08-23 11:24:54Graph analytics with Spark GraphFrames
Predictive maintenance with Dataiku,
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Orbifold2018-05-22 10:28:172018-07-01 06:28:54Predictive maintenance, the easy way
Basics of stats using Apache Hive.
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Orbifold2018-05-21 10:39:282018-07-01 06:43:05Statistics on Apache Hive
Spark Streaming in HortonWorks.
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Orbifold2018-05-21 10:37:552018-07-01 06:46:12Apache Spark Streaming
Zeppelin as the Jupyter of big data.
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Orbifold2018-05-21 10:31:232018-07-01 06:51:12Getting started with Apache Zeppelin