Workshop: Building Recommender Systems w/ Apache Spark 2.x

Location: Garden A

Duration: 9:00am - 4:00pm

Day of week: Friday

Level: Beginner

Prerequisites

  • Basic familiarity and usage with Apache Spark is helpful
  • Basic programming experience in objected-oriented or functional language is required
  • The exercises will mostly be written in Scala
  • Participants should bring their laptop

Apache Spark has become one of the must-know big data technologies due to its speed, ease of use, and versatility. Spark can be used for performing data analysis and building big-data applications. Increasingly, companies are leveraging Apache Spark to build intelligent applications that use Machine Learning techniques. This workshop will start with covering the major features in Spark 2.x and then focus on building a recommendation system using Spark MLlib library. It will include focused and interactive hands-on exercises.

Signup for a free Databricks Community Edition account - https://community.cloud.databricks.com/

Tutorial materials can be found at - https://sites.google.com/view/apache-spark-workshop/

Here is what you can expect to learn from this tutorial:

  • Spark architecture and execution model
  • Structured data processing with Spark SQL, DataFrames, and Datasets
  • Streaming processing with Structure Streaming
  • Major concepts and utilities in Spark ML library for building intelligent applications
  • Build a recommender system using Spark ML library

Speaker: Hien Luu

Engineering Manager @Linkedin focused on Big Data

Hien Luu is an engineering manager at LinkedIn and he is a big data enthusiast. He is particularly passionate about the intersection between Big Data and Artificial Intelligence. Teaching is one his passions and he is currently teaching Apache Spark course at UCSC Silicon Valley Extension school. He has given presentations at various conferences like QCon SF, QCon London, Hadoop Summit, JavaOne, ArchSummit and Lucene/Solr Revolution.

Find Hien Luu at

Tracks

Monday, 5 November

Tuesday, 6 November

Wednesday, 7 November