Building A Prediction Engine

Richard Williamson explains how to build a prediction engine using technologies such as Spark, Kudu, Impala, and Kafka:

We’ll aim to predict the volume of events for the next 10 minutes using a streaming regression model, and compare those results to a traditional batch prediction method. This prediction could then be used to dynamically scale compute resources, or for other business optimization. I will start out by describing how you would do the prediction through traditional batch processing methods using both Apache Impala (incubating) and Apache Spark, and then finish by showing how to more dynamically predict usage by using Spark Streaming.

Of course, the starting point for any prediction is a freshly updated data feed for the historic volume for which I want to forecast future volume. In this case, I discovered that Meetup.com has a very nice data feed that can be used for demonstration purposes. You can read more about the API here, but all you need to know at this point is that it provides a steady stream of RSVP volume that we can use to predict future RSVP volume.

This is pretty dense, but it is a great look at one potential architecture leveraging Spark and several tools in the Hadoop ecosystem.

Related Posts

Security Improvements In Kafka And Confluent Platform

Vahid Fereydouny demonstrates a number of security improvements made to Apache Kafka 2.0 as well as Confluent Platform 5.0: Over the past several quarters, we have made major security enhancements to Confluent Platform, which have helped many of you safeguard your business-critical applications. With the latest release, we increased the robustness of our security feature […]

Read More

SparkSession Versus SparkContext

Abhishek Baranwal explains the differences between the SparkSession object and the SparkContext object when writing Spark code: Prior to spark 2.0, SparkContext was used as a channel to access all spark functionality. The spark driver program uses sparkContext to connect to the cluster through resource manager. SparkConf is required to create the spark context object, […]

Read More

Categories

May 2016
MTWTFSS
« Apr Jun »
 1
2345678
9101112131415
16171819202122
23242526272829
3031