Using Spark Efficiently | Understanding Spark Event 7/29/17

This page is dedicated to resources related to the 7/29/17 Understanding Spark event presentation in Bellevue, WA. Slides Great [FREE!] resources on all things Spark: Databricks was founded by the original creators of Spark and is currently the largest contributor to Apache Spark. As such, they are a phenomenal resource for information and… Continue reading

Switching between Scala and Python on Spark tips

Switching between Scala and Python on Spark is relatively straightforward, but there are a few differences that can cause some minor frustration. Here are some of the little things I’ve run into and how to adjust for them. PySpark Shell does not support code completion (autocomplete) by default. Why? PySpark uses the basic Python interpreter… Continue reading

Real Time Big Data analytics: Parquet (and Spark) + bonus

Apache Spark and Parquet (SParquet) are a match made in scalable data analytics and delivery heaven. Spark brings a wide ranging, powerful computing platform to the equation while Parquet offers a data format that is purpose-built for high-speed big data analytics. If this sounds like fluffy marketing talk, resist the temptation to close this tab,… Continue reading

Connecting Apache Spark to External Data sources (e.g. Redshift, S3, MySQL)

Pre-requisites AWS S3 Hadoop AWS Jar AWS Java SDK Jar * Note: These AWS jars should not be necessary if you’re using Amazon EMR. Amazon Redshift JDBC Driver Spark-Redshift package * * The Spark-redshift package provided by Databricks is critical particularly if you wish to WRITE to Redshift, because it does bulk file operations instead… Continue reading