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Category Archives: Spark
Measuring Campaign Effectiveness for an Online Service on Spark
Measuring campaign effectiveness is critical for any company to justify the marketing money being spent. Consider a company providing a free online service on signup. It’s critical for the company to convert them so that they subscribe to a paid … Continue reading
Project Assignment Optimization with Simulated Annealing on Spark
Optimizing assignment of people to projects is a very complex problem and classical optimization techniques are not very useful. The topic this post is a project assignment optimization problem where people should be assigned to projects in a way that will … Continue reading
Posted in Data Science, Optimization, Spark
Tagged ptoject assignment, simulated annealing, stochastic optimization
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Machine Learning at Scale with Parallel Processing
Machine Learning can leverage modern parallel data processing platforms like Hadoop and Spark in several ways. In this post we will discuss how to have Machine Learning at scale with Hadoop or Spark. We will consider three different ways parallel … Continue reading
Mobile Phone Usage Data Analytics for Effective Marketing Campaign
Insights gained from analyzing mobile phone usage data can be extremely valuable in marketing campaign and customer engagement efforts. For example, hour of the day when an user engages most with his or her mobile device could be used to … Continue reading
Posted in Big Data, Data Profiling, Marketing Analytic, Spark, Statistics
Tagged marketing campaign, mobile usage
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JSON to Relational Mapping with Spark
If there one data format that’s ubiquitous, it’s JSON. Whether you are calling an API, or exporting data from some system, the format is most likely to be JSON these days. However many databases can not handle JSON and you … Continue reading
Supplier Fulfillment Forecasting with Continuous Time Markov Chain using Spark
In a supply chain, quantity ordered from a down stream supplier or manufacturer are not necessarily always completely fulfilled, because of various factors. If the extent of under fulfillment could be predicted over a time horizon, then the shortfall items … Continue reading
Posted in Big Data, Data Science, Machine Learning, Scala, Spark
Tagged continuous time markov chain, forecasting, supply chain
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Simple Sanity Checks for Data Correctness with Spark
Sometimes when running a complex data processing pipeline with Hadoop or Spark, you may encounter data, where most of the data is just grossly invalid. It might save lot of pain and headache, if we could do some simple sanity checks before feeding … Continue reading
