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.
Using MLLib naive Bayes for spam classification.
A five mins bitcoin-ish implementation to understand it.
By expanding a time series horizontally you can use H2O to forecast it.
Using cognitive computing to help intelligence officers be more efficient.
Large scale graph analytics with Spark and Apache GraphFrames.
Predictive maintenance with Dataiku,
Basics of stats using Apache Hive.
Spark Streaming in HortonWorks.
Zeppelin as the Jupyter of big data.
Using Spark and H2O to detect spam.
