Predicting Flight Delays using Apache Spark and Kylin

Predicting Flight Delays using Apache Spark and Kylin

In this project, we will be building and querying an OLAP Cube for Flight Delays on the Hadoop platform.

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What will you learn

Discuss the installation of Apache Kylin in a Hortonworks sandbox.
Design star schema on our flight dataset
Implementing our star schema in Kylin
Building and merging Kyline segments incrementally.
Building Cubes using Kylin Restful API
How to execute Cubes using Spark Engine

Project Description

In previous Hackerday sessions, we have introduced how to bring OLAP to extremely large datasets in Apache Kylin. For those who don't know what Kylin is, Kylin (kylin.apache.org) is a Distributed Analytics Engine that provides SQL interface and multidimensional analysis (OLAP) on the large dataset using MapReduce or Spark. This means that I can answer classical aggregate queries in the Hadoop platform with a low latency over billions of records.

In this Hackerday, we will be performing an OLAP cube design using the flight on-time dataset. Since we have previously introduced Kylin, this Hackerday session will look at more involved features like incremental build, performance tuning or consideration tips, we will discuss the Spark engine as well as how to build different types of model.

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