Why Apache Spark is a Crossover Hit for Data Scientists | Digital Analytics | Scoop.it

park is a compelling multi-purpose platform for use cases that span investigative, as well as operational, analytics.

Data science is a broad church. I am a data scientist — or so I’ve been told — but what I do is actually quite different from what other “data scientists” do. For example, there are those practicing “investigative analytics” and those implementing “operational analytics.” (I’m in the second camp.)


Data scientists performing investigative analytics use interactive statistical environments like R to perform ad-hoc, exploratory analytics in order to answer questions and gain insights. By contrast, data scientists building operational analytics systems have more in common with engineers. They build software that creates and queries machine-learning models that operate at scale in real-time serving environments, using systems languages like C++ and Java, and often use several elements of an enterprise data hub, including the Apache Hadoop ecosystem.

And there are subgroups within these groups of data scientists. For example, some analysts who are proficient with R have never heard of Python or scikit-learn, or vice versa, even though both provide libraries of statistical functions that are accessible from a REPL (Read-Evaluate-Print Loop) environment.

A World of Tradeoffs

It would be wonderful to have one tool for everyone, and one architecture and language for investigative as well as operational analytics. If I primarily work in Java, should I really need to know a language like Python or R in order to be effective at exploring data? Coming from a conventional data analyst background, must I understand MapReduce in order to scale up computations? The array of tools available to data scientists tells a story of unfortunate tradeoffs:

R offers a rich environment for statistical analysis and machine learning, but it has some rough edges when performing many of the data processing and cleanup tasks that are required before the real analysis work can begin. As a language, it’s not similar to the mainstream languages developers know.Python is a general purpose programming language with excellent libraries for data analysis like Pandas and scikit-learn. But like R, it’s still limited to working with an amount of data that can fit on one machine.It’s possible to develop distributed machine learning algorithms on the classic MapReduce computation framework in Hadoop (see Apache Mahout). But MapReduce is notoriously low-level and difficult to express complex computations in.Apache Crunch offers a simpler, idiomatic Java API for expressing MapReduce computations. But still, the nature of MapReduce makes it inefficient for iterative computations, and most machine learning algorithms have an iterative component.

And so on. There are both gaps and overlaps between these and other data science tools. Coming from a background in Java and Hadoop, I do wonder with envy sometimes: why can’t we have a nice REPL-like investigative analytics environment like the Python and R users have? That’s still scalable and distributed? And has the nice distributed-collection design of Crunch? And can equally be used in operational contexts?

Via Alex Kantone