Featured Author

Carol McDonald
Solutions Architect, MapR

Carol has extensive experience as a developer and architect building complex, mission-critical applications in the Banking, Health Insurance and Telecom industries. As a Java Technology Evangelist at Sun Microsystems, Carol traveled all over the world speaking at Sun Tech Days, JUGs, companies, and conferences. She is a recognized speaker in Java communities.

Author's Posts

Posted on January 5, 2017 by Carol McDonald

The first post discussed creating a machine learning model using Apache Spark’s K-means algorithm to cluster Uber data based on location. This second post will discuss using the saved K-means model with streaming data to do real-time analysis of where and when Uber cars are clustered.

Posted on December 8, 2016 by Carol McDonald

This post is the second in a series where we will go over examples of how MapR data scientist Joe Blue assisted MapR customers, in this case a regional bank, to identify new data sources and apply machine learning algorithms in order to better understand their customers. In this second part, we will cover a bank customer profitability 360° example, presenting the before, during and after.

Posted on November 28, 2016 by Carol McDonald

According to Gartner, by 2020, a quarter of a billion connected cars will form a major element of the Internet of Things. Connected vehicles are projected to generate 25GB of data per hour, which can be analyzed to provide real-time monitoring and apps, and will lead to new concepts of mobility and vehicle usage.

Posted on October 4, 2016 by Carol McDonald

With the rapid expansion of smart phones and other connected mobile devices, communications service providers (CSPs) need to rapidly process, store, and derive insights from the diverse volume of data travelling across their networks. Big data analytics can help CSPs improve profitability by optimizing network services/usage, enhancing customer experience, and improving security.

Posted on October 17, 2016 by Carol McDonald

In this blog post, I’ll help you get started using Apache Spark’s spark.ml Logistic Regression for predicting cancer malignancy. Spark’s spark.ml library goal is to provide a set of APIs on top of DataFrames that help users create and tune machine learning workflows or pipelines.

Posted on September 6, 2016 by Carol McDonald

This post will help you get started using Apache Spark Streaming for consuming and publishing messages with MapR Streams and the Kafka API. Spark Streaming is an extension of the core Spark API that enables continuous data stream processing.

Posted on August 29, 2016 by Carol McDonald

Building a robust, responsive, secure data service for healthcare is tricky. For starters, healthcare data lends itself to multiple models: Document representation for patient profile views or updates; Graph representation to query relationships between patients, providers, and medications; Search representation for advanced lookups. This post will describe how stream-first architectures can solve these challenges, and look at how this has been implemented at Liaison Technologies.

Posted on August 8, 2016 by Carol McDonald

This post is the first in a series where we will review examples of how Joe Blue, a Data Scientist in MapR Professional Services, assisted MapR customers in identifying new data sources and applying machine learning algorithms in order to better understand their customers. The first example in the series is an advertising customer 360°; the next example in the series will be banking and healthcare customer 360° examples.

Posted on July 12, 2016 by Carol McDonald

Random forests are one of the most successful machine learning models for classification. In this blog post, I’ll help you get started using Apache Spark’s spark.ml Random forests for classification of bank loan credit risk.

Posted on June 29, 2016 by Carol McDonald

This post will use Apache Spark SQL and DataFrames to query, compare and explore S&P 500, Exxon and Anadarko Petroleum Corporation stock prices.

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