Analytics Blog

An Introduction to PFA

With Chorus 6.1 we have introduced the support for PFA, the Portable Format for Analytics. Before we get into what PFA is let’s make some observations about the data science process. There are a few important questions we can ask about the process in general: 1.) What is our processing model? 2.) What are our… Read more »


Integrating with Financial Data Providers

In some of our routine customer engagements with the financial services industry, a question we repeatedly face is around the difficulty of integrating analytics platforms like Alpine with industry-standard data providers such as Thomson Reuters, Bloomberg, FactSet or Quandl. Often enough, this sparks a follow-up question on integrating the Alpine platform with third-party external APIs…. Read more »


Announcing Chorus 6.1

Last week we announced the availability of Chorus 6.1. With this latest release, we’ve continued to deliver new enterprise analytics features, including several marquee items such as enterprise data governance, Spark Autotuning, and support for a developing model interchange specification, PFA. Enterprise data governance: Chorus 6.1 introduces support for administrators to exert fine-grain control over… Read more »


Shifting from Pandas to Spark DataFrames Pt 2

Welcome to the second part of this introduction to Spark DataFrames! Using Spark DataFrames If you successfully installed Spark you should be able to launch the Spark/Scala shell with the “spark-shell” command from any terminal window. A few things automatically happen when you launch this shell. This command defaults to local mode, which means that… Read more »


Shifting from Pandas to Spark Dataframes

Like most data scientists, I frequently use a lot of the same tools: Python, Pandas, scikit-learn, R, relational databases, Hadoop and so on. As part of Alpine Data’s Labs team I am frequently exposed to tools that other companies use – and every company has a different stack. This won’t come as a surprise, but… Read more »