Analyzing Clickstream Data With Spark

Tony Cruz and Denny Lee analyze advertising data in Spark and predict click counts given certain input features:

Let’s look at a concrete example with the Click-Through Rate Prediction dataset of ad impressions and clicks from the data science website Kaggle.  The goal of this workflow is to create a machine learning model that, given a new ad impression, predicts whether or not there will be a click.

To build our advanced analytics workflow, let’s focus on the three main steps:

  • ETL

  • Data Exploration, for example, using SQL

  • Advanced Analytics / Machine Learning

The Databricks blog has a couple other examples, but this was the most interesting one for me.

Related Posts

Working With The Databricks API Via Powershell

Gerhard Brueckl has a Powershell module for interacting with Databricks, either Azure or AWS: As most of our deployments use PowerShell I wrote some cmdlets to easily work with the Databricks API in my scripts. These included managing clusters (create, start, stop, …), deploying content/notebooks, adding secrets, executing jobs/notebooks, etc. After some time I ended […]

Read More

Kafka Connect Converters And Serialization

Robin Moffatt goes into great detail on Apache Kafka Connect converters and serialization techniques: Kafka Connect is modular in nature, providing a very powerful way of handling integration requirements. Some key components include: Connectors – the JAR files that define how to integrate with the data store itself Converters – handling serialization and deserialization of […]

Read More

Categories

July 2018
MTWTFSS
« Jun Aug »
 1
2345678
9101112131415
16171819202122
23242526272829
3031