RSiteCatalyst Version 1.4.16 Release Notes

It’s been a while since the last update, but RSiteCatalyst is still going strong! Thanks to Wen for submitting a fix/enhancement to enable the ability to use multiple columns from a Classification within the QueueDataWarehouse function. No other bug fixes were made, nor was any additional functionality added.

Version 1.4.16 of RSiteCatalyst was submitted to CRAN yesterday and should be available for download in the coming days.

Community Contributions

As I’ve mentioned in many a blog post before this one, I encourage all users of the software to continue reporting bugs via GitHub issues, and especially if you can provide a working code example. Even better, a fix via pull request will ensure that your bug will be addressed in a timely manner and for the benefit to others in the community.

Note: Please don’t email directly via the email in the RSiteCatalyst package, it will not be returned. Having a valid email contact in the package is a requirement to have a package listed on CRAN so they can contact the package author, it is not meant to imply I can/will provide endless, personalized support for free.


DataOps Summit: Streaming Real-time Telemetry With OmniSci and StreamSets

In this talk from the StreamSets DataOps 2019 conference, I provide an overview of the data pipeline for the OmniSci F1 Demo. Using StreamSets Data Collector in concert with Apache Kafka and OmniSciDB, you can create a full real-time data pipeline for telemetry data using only open-source components.

The talk outlines using the UDP listener for StreamSets to collect packets from the F1 2018 game, writing the packets to Kafka, reading from Kafka and using Groovy to parse the packets, and using the OmniSci JDBC driver to insert the data into one of nine OmniSciDB tables. With this workflow, you have a robust platform for accelerated analytics, using the power of GPUs for fast computation.


GitHub: https://github.com/omnisci/vehicle-telematics-analytics-demo

Speakerdeck: https://speakerdeck.com/omnisci/the-f1-demo-streaming-real-time-telemetry-using-apache-kafka-and-streamsets


ODSC webinar: End-to-End Data Science Without Leaving the GPU

In this webinar sponsored by the Open Data Science Conference (ODSC), I outline a brief history of GPU analytics and the problems that using GPU analytics solves relative to using other parallel computation methods such as Hadoop. I also demonstrate how OmniSci fits into the broader GPU-accelerated data science workflow, with examples provided using Python.

Check out the video, grab the Jupyter Notebook from the odscwebinar repo and get started with OmniSci and GPU-accelerated data science!


  • RSiteCatalyst Version 1.4.16 Release Notes
  • Using RSiteCatalyst With Microsoft PowerBI Desktop
  • RSiteCatalyst Version 1.4.14 Release Notes
  • RSiteCatalyst Version 1.4.13 Release Notes
  • RSiteCatalyst Version 1.4.12 (and 1.4.11) Release Notes
  • Self-Service Adobe Analytics Data Feeds!
  • RSiteCatalyst Version 1.4.10 Release Notes
  • WordPress to Jekyll: A 30x Speedup
  • Bulk Downloading Adobe Analytics Data
  • Adobe Analytics Clickstream Data Feed: Calculations and Outlier Analysis
  • Adobe: Give Credit. You DID NOT Write RSiteCatalyst.
  • RSiteCatalyst Version 1.4.8 Release Notes
  • Adobe Analytics Clickstream Data Feed: Loading To Relational Database
  • Calling RSiteCatalyst From Python
  • RSiteCatalyst Version 1.4.7 (and 1.4.6.) Release Notes
  • RSiteCatalyst Version 1.4.5 Release Notes
  • Getting Started: Adobe Analytics Clickstream Data Feed
  • RSiteCatalyst Version 1.4.4 Release Notes
  • RSiteCatalyst Version 1.4.3 Release Notes
  • RSiteCatalyst Version 1.4.2 Release Notes
  • Destroy Your Data Using Excel With This One Weird Trick!
  • RSiteCatalyst Version 1.4.1 Release Notes
  • Visualizing Website Pathing With Sankey Charts
  • Visualizing Website Structure With Network Graphs
  • RSiteCatalyst Version 1.4 Release Notes
  • Maybe I Don't Really Know R After All
  • Building JSON in R: Three Methods
  • Real-time Reporting with the Adobe Analytics API
  • RSiteCatalyst Version 1.3 Release Notes
  • Adobe Analytics Implementation Documentation in 60 Seconds
  • RSiteCatalyst Version 1.2 Release Notes
  • Clustering Search Keywords Using K-Means Clustering
  • RSiteCatalyst Version 1.1 Release Notes
  • Anomaly Detection Using The Adobe Analytics API
  • (not provided): Using R and the Google Analytics API
  • My Top 20 Least Useful Omniture Reports
  • For Maximum User Understanding, Customize the SiteCatalyst Menu
  • Effect Of Modified Bounce Rate In Google Analytics
  • Adobe Discover 3: First Impressions
  • Using Omniture SiteCatalyst Target Report To Calculate YOY growth
  • ODSC webinar: End-to-End Data Science Without Leaving the GPU
  • PyData NYC 2018: End-to-End Data Science Without Leaving the GPU
  • Data Science Without Leaving the GPU
  • Getting Started With OmniSci, Part 2: Electricity Dataset
  • Getting Started With OmniSci, Part 1: Docker Install and Loading Data
  • Parallelizing Distance Calculations Using A GPU With CUDAnative.jl
  • Building a Data Science Workstation (2017)
  • JuliaCon 2015: Everyday Analytics and Visualization (video)
  • Vega.jl, Rebooted
  • Sessionizing Log Data Using data.table [Follow-up #2]
  • Sessionizing Log Data Using dplyr [Follow-up]
  • Sessionizing Log Data Using SQL
  • Review: Data Science at the Command Line
  • Introducing Twitter.jl
  • Code Refactoring Using Metaprogramming
  • Evaluating BreakoutDetection
  • Creating A Stacked Bar Chart in Seaborn
  • Visualizing Analytics Languages With VennEuler.jl
  • String Interpolation for Fun and Profit
  • Using Julia As A "Glue" Language
  • Five Hard-Won Lessons Using Hive
  • Using SQL Workbench with Apache Hive
  • Getting Started With Hadoop, Final: Analysis Using Hive & Pig
  • Quickly Create Dummy Variables in a Data Frame
  • Using Amazon EC2 with IPython Notebook
  • Adding Line Numbers in IPython/Jupyter Notebooks
  • Fun With Just-In-Time Compiling: Julia, Python, R and pqR
  • Getting Started Using Hadoop, Part 4: Creating Tables With Hive
  • Tabular Data I/O in Julia
  • Hadoop Streaming with Amazon Elastic MapReduce, Python and mrjob
  • A Beginner's Look at Julia
  • Getting Started Using Hadoop, Part 3: Loading Data
  • Innovation Will Never Be At The Push Of A Button
  • Getting Started Using Hadoop, Part 2: Building a Cluster
  • Getting Started Using Hadoop, Part 1: Intro
  • Instructions for Installing & Using R on Amazon EC2
  • Video: SQL Queries in R using sqldf
  • Video: Overlay Histogram in R (Normal, Density, Another Series)
  • Video: R, RStudio, Rcmdr & rattle
  • Getting Started Using R, Part 2: Rcmdr
  • Getting Started Using R, Part 1: RStudio
  • Learning R Has Really Made Me Appreciate SAS