Cloudera Engineering Blog · Data Science Posts

The New Wrangle Conference: Solving the Hardest Data Science Challenges from Startup to Enterprise

Wrangle, a new conference dedicated to the practice of data science from startup to enterprise, debuts in San Francisco on Oct. 22, 2015.

Even as Cloudera introduce new tools for analytics and machine learning into its platform (like the recently announced Ibis project, for example), we are mindful of the fact that many of the hardest problems in data science cannot be solved by technology alone. From the smallest startups to the largest enterprises, we see companies struggling with how to acquire and manage new data sources, recruit and train the next generation of data scientists, and create a data-driven culture that crosses every level of the organization.

Ibis on Impala: Python at Scale for Data Science

This new Cloudera Labs project promises to deliver the great Python user experience and ecosystem at Hadoop scale.

Across the user community, you will find general agreement that the Apache Hadoop stack has progressed dramatically in just the past few years. For example, Search and Impala have moved Hadoop beyond batch processing, while developers are seeing significant productivity gains and additional use cases by transitioning from MapReduce to Apache Spark.

How-to: Build Re-usable Spark Programs using Spark Shell and Maven

Set up your own, or even a shared, environment for doing interactive analysis of time-series data.

Although software engineering offers several methods and approaches to produce robust and reliable components, a more lightweight and flexible approach is required for data analysts—who do not build “products” per se but still need high-quality tools and components. Thus, recently, I tried to find a way to re-use existing libraries and datasets stored already in HDFS with Apache Spark.

Calculating CVA with Apache Spark

Thanks to Matthew Dixon, principal consultant at Quiota LLC and Professor of Analytics at the University of San Francisco, and Mohammad Zubair, Professor of Computer Science at Old Dominion University, for this guest post that demonstrates how to easily deploy exposure calculations on Apache Spark for in-memory analytics on scenario data.

Since the 2007 global financial crisis, financial institutions now more accurately measure the risks of over-the-counter (OTC) products. It is now standard practice for institutions to adjust derivative prices for the risk of the counter-party’s, or one’s own, default by means of credit or debit valuation adjustments (CVA/DVA).

Advanced Analytics with Apache Spark: The Book

Authored by a substantial portion of Cloudera’s Data Science team (Sean Owen, Sandy Ryza, Uri Laserson, Josh Wills), Advanced Analytics with Spark (currently in Early Release from O’Reilly Media) is the newest addition to the pipeline of ecosystem books by Cloudera engineers. I talked to the authors recently.

Why did you decide to write this book?

Bayesian Machine Learning on Apache Spark

Markov Chain Monte Carlo methods are another example of useful statistical computation for Big Data that is capably enabled by Apache Spark.

During my internship at Cloudera, I have been working on integrating PyMC with Apache Spark. PyMC is an open source Python package that allows users to easily apply Bayesian machine learning methods to their data, while Spark is a new, general framework for distributed computing on Hadoop. Together, they provide a scalable framework for scalable Markov Chain Monte Carlo (MCMC) methods. In this blog post, I am going to describe my work on distributing large-scale graphical models and MCMC computation.

Markov Chain Monte Carlo Methods

How-to: Count Events Like a Data Scientist

The ability to quickly and accurately count complex events is a legitimate business advantage.

In our work as data scientists, we spend most of our time counting things. It is the foundational skill that is used in data cleansing, reporting, feature engineering, and simple-but-effective machine learning models like Naive Bayes classifiers. Hilary Mason has a quote about the benefits of counting that I love:

Estimating Financial Risk with Apache Spark

Learn how Spark facilitates the calculation of computationally-intensive statistics such as VaR via the Monte Carlo method.

Under reasonable circumstances, how much money can you expect to lose? The financial statistic value at risk (VaR) seeks to answer this question. Since its development on Wall Street soon after the stock market crash of 1987, VaR has been widely adopted across the financial services industry. Some organizations report the statistic to satisfy regulations, some use it to better understand the risk characteristics of large portfolios, and others compute it before executing trades to help make informed and immediate decisions.

Meet the Data Scientist: Sandy Ryza

Meet Sandy Ryza (@SandySifting), the newest member of Cloudera’s data science team. See Sandy present at Spark Summit 2014 (June 30-July 1 in San Francisco; register here for a 20% discount).

What is your definition of a “data scientist”?

Meet the Data Scientist: Alan Paulsen

Meet Alan Paulsen, among the first to earn the CCP: Data Scientist distinction.

Big Data success requires professionals who can prove their mastery with the tools and techniques of the Apache Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At Cloudera, we’re drawing on our industry leadership and early corpus of real-world experience to address the Big Data talent gap with the Cloudera Certified Professional (CCP) program.

A New Python Client for Impala

The new Python client for Impala will bring smiles to Pythonistas!

As a data scientist, I love using the Python data stack. I also love using Impala to work with very large data sets. But things that take me out of my Python workflow are generally considered hassles; so it’s annoying that my main options for working with Impala are to write shell scripts, use the Impala shell, and/or transfer query results by reading/writing local files to disk.

Meet the Data Scientist: Stuart Horsman

Meet Stuart Horsman, among the first to earn the CCP: Data Scientist distinction.

Big Data success requires professionals who can prove their mastery with the tools and techniques of the Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At Cloudera, we’re drawing on our industry leadership and early corpus of real-world experience to address the Big Data talent gap with the Cloudera Certified Professional (CCP) program.

Meet the Data Scientist: David F. McCoy

Meet David F. McCoy, one of the first to have earned the title “CCP: Data Scientist” from Cloudera University.

Big Data success requires professionals who can prove their mastery with the tools and techniques of the Hadoop stack. However, experts predict a major shortage of advanced analytics skills over the next few years. At Cloudera, we’re drawing on our industry leadership and early corpus of real-world experience to address the Big Data talent gap with the Cloudera Certified Professional (CCP) program.

Why Apache Spark is a Crossover Hit for Data Scientists

Spark 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.)

How-to: Do Statistical Analysis with Impala and R

The new RImpala package brings the speed and interactivity of Impala to queries from R.

Our thanks to Austin Chungath, Sachin Sudarshana, and Vikas Raguttahalli of Mu Sigma, a Decision Sciences and Big Data analytics company, for the guest post below.

How-to: Use Cascading Pattern with R and CDH

Our thanks to Concurrent Inc. for the how-to below about using Cascading Pattern with CDH. Cloudera recently tested CDH 4.4 with the Cascading Compatibility Test Suite verifying compatibility with Cascading 2.2.

Cascading Pattern is a machine-learning project within the Cascading development framework used to build enterprise data workflows. Cascading provides an abstraction layer on top of Apache Hadoop and other computing topologies that allows enterprises to leverage existing skills and resources to build data processing applications on Hadoop, without the need for specialized Hadoop skills.

How-to: Use MADlib Pre-built Analytic Functions with Impala

Thanks to Victor Bittorf, a visiting graduate computer science student at Stanford University, for the guest post below about how to use the new prebuilt analytic functions for Cloudera Impala.

Cloudera Impala is an exciting project that unlocks interactive queries and SQL analytics on big data. Over the past few months I have been working with the Impala team to extend Impala’s analytic capabilities. Today I am happy to announce the availability of pre-built mathematical and statistical algorithms for the Impala community under a free open-source license. These pre-built algorithms combine recent theoretical techniques for shared nothing parallelization for analytics and the new user-defined aggregations (UDA) framework in Impala 1.2 in order to achieve big data scalability. This initial release has support for logistic regression, support vector machines (SVMs), and linear regression.

Customer Spotlight: Persado Makes Marketing a Data Science

It’s common to hear people describe themselves as being “left-brained” or “right-brained” based on their tendency to be more logical and mathematically driven (left-brained), or, conversely, to be intuitive and creatively driven (right-brained). For example, people who prefer math over art are often considered left-brained. People who get a higher verbal score on their SATs than for math are often considered right-brained.

In general, language and creative writing are considered right-brained exercises. Many people also associate marketing and advertising as a right-brained function, whereas engineering is considered very left-brained.

Meet the Project Founder: Josh Wills

In this installment of “Meet the Project Founder,” we speak with Josh Wills (@josh_wills), Cloudera’s Senior Director of Data Science and founder of Apache Crunch and Cloudera ML.

What led you to your project idea(s)?
When I first started at Cloudera in 2011, I had a fairly vague job description, no real responsibilities, and wasn’t all that familiar with the Apache Hadoop stack, so I started working on various pet projects in order to learn more about the tools and the use cases in domains like healthcare and energy.

Get Hired as a Certified Data Scientist

To paraphrase Nate Silver: “There is lots of data coming. Who will speak for all this data?”

Nearly every day, I read new articles about how Big Data is “changing everything.” Data scientists are unlocking new approaches that help researchers find the cure for cancer, banks fight fraud, the police fight drug-related crimes, and fantasy sports leaguers fight each other.

Myrrix Joins Cloudera to Bring "Big Learning" to Hadoop

What a short, strange trip it’s been. Just a year ago, I founded Myrrix in London’s Silicon Roundabout to commercialize large-scale machine learning based on Apache Hadoop and Apache Mahout. It’s been a busy scramble, building software and proudly watching early customers get real, big data-sized machine learning into production.

And now another beginning: Myrrix has a new home in Cloudera. I’m excited to join as Director of Data Science in London, alongside Josh Wills. Some of the Myrrix technology will be coming along to benefit CDH and its customers too. There was no question that Cloudera is the right place to continue building out the vision that started as Myrrix, because Josh, Jeff Hammerbacher and the rest of the data science team here have the same vision. It’s an unusually perfect match. Cloudera has made an increasingly complex big-data ecosystem increasingly accessible (Hadoop, real-time queries, search), and we’re going to make “Big Learning” on Hadoop easy and accessible too.

What is Old is New Again

Data-savvy companies of all sizes can now accomplish many viable machine learning projects.

How the SAS and Cloudera Platforms Work Together

On Monday April 29, Cloudera announced a strategic alliance with SAS. As the industry leader in business analytics software, SAS brings a formidable toolset to bear on the problem of extracting business value from large volumes of data.

Over the past few months, Cloudera has been hard at work along with the SAS team to integrate a number of SAS products with Apache Hadoop, delivering the ability for our customers to use these tools in their interaction with data on the Cloudera platform. In this post, we will delve into the major mechanisms that are available for connecting SAS to CDH, Cloudera’s 100% open-source distribution including Hadoop.

SAS/ACCESS to Hadoop

Algorithms Every Data Scientist Should Know: Reservoir Sampling

Data scientists, that peculiar mix of software engineer and statistician, are notoriously difficult to interview. One approach that I’ve used over the years is to pose a problem that requires some mixture of algorithm design and probability theory in order to come up with an answer. Here’s an example of this type of question that has been popular in Silicon Valley for a number of years: 

Say you have a stream of items of large and unknown length that we can only iterate over once. Create an algorithm that randomly chooses an item from this stream such that each item is equally likely to be selected.

How-to: Analyze Twitter Data with Hue

Hue 2.2 , the open source web-based interface that makes Apache Hadoop easier to use, lets you interact with Hadoop services from within your browser without having to go to a command-line interface. It features different applications like an Apache Hive editor and Apache Oozie dashboard and workflow builder.

This post is based on our “Analyzing Twitter Data with Hadoop” sample app and details how the same results can be achieved through Hue in a simpler way. Moreover, all the code and examples of the previous series have been updated to the recent CDH4.2 release.

Collecting Data

One User’s Impala Experience at Data Hacking Day

The following guest post comes to you from Alan Gardner of remote database services and consulting company Pythian, who participated in Data Hacking Day (and was on the winning team!) at Cloudera’s offices in February.

Last Feb. 25, just prior to attending Strata, Alex Gorbachev (our CTO) and I had the chance to visit Cloudera’s Palo Alto offices for Data Hacking Day. The goal of the event was to produce something cool that leverages Cloudera Impala – the new open source, low-latency platform for querying data in Apache Hadoop.

Cloudera’s Jeff Hammerbacher on Charlie Rose

In this Charlie Rose interview that aired on March 22, 2013, Cloudera’s Chief Scientist Jeff Hammerbacher (@hackingdata) offers fascinating insights into the origins of Big Data and data science techniques at Google and their re-implementation into open source used by consumer Web companies. Furthermore, he offers great detail about their positive application across healthcare diagnostics and delivery – as well as the overall need for better balance between “numerical imagination” and “narrative imagination” in everything we do (in order to “ask bigger questions”, as some would say).

It’s an incredibly valuable look into where Big Data came from, where it’s going, and how Cloudera is helping it get there.

Cloudera ML: New Open Source Libraries and Tools for Data Scientists

Editor’s note (12/19/2013): Cloudera ML has been merged into the Oryx project. The information below is still valid though.

Last month, Apache Crunch became the fifth project (along with Sqoop, Flume, Bigtop, and MRUnit) to go from Cloudera’s github repository through the Apache Incubator and on to graduate as a top-level project within the Apache Software Foundation. As the founder of the project and a newly minted Apache VP, I wanted to take this opportunity to express my gratitude to the Crunch community, who have taught me that leadership in the Apache Way means service, humility, and investing more time in building a community than I spend writing code. Working with you all on our shared vision is the highlight of every work week.

Creating Analytical Applications with Crunch: Cloudera ML

Video Premiere: Training a New Generation of Data Scientists

Data scientists drive data as a platform to answer previously unimaginable questions. These multi-talented data professionals are in demand like never before because they identify or create some of the most exciting and potentially profitable business opportunities across industries. However, a scarcity of existing external talent will require companies of all sizes to find, develop, and train their people with backgrounds in software engineering, statistics, or traditional business intelligence as the next generation of data scientists.

Join us for the premiere of Training a New Generation of Data Scientists on Tuesday, March 26, at 2pm ET/11am PT. In this video, Cloudera’s Senior Director of Data Science, Josh Wills, will discuss what data scientists do, how they think about problems, the relationship between data science and Hadoop, and how Cloudera training can help you join this increasingly important profession. Following the video, Josh will answer your questions about data science, Hadoop, and Cloudera’s Introduction to Data Science: Building Recommender Systems course.

How-to: Resample from a Large Data Set in Parallel (with R on Hadoop)

UPDATED 20130424: The new RHadoop treats output to Streaming a bit differently, so do.trace=FALSE must be set in the randomForest call.

UPDATED 20130408: Antonio Piccolboni, the author of RHadoop, has improved the code somewhat using his substantially greater experience with R. The most material change is that the latest version of RHadoop can bind multiple calls to keyval correctly.

Save 15% on Multi-Course Public Training Enrollments in January and February

Cloudera University is the world leader in Apache Hadoop training and certification. Our full suite of live courses and online materials is the best resource to get started with your Hadoop cluster in development or advance it towards production.  We offer deep industry insight into the skills and expertise required to establish yourself as a leading Developer or Administrator managing and processing Big Data in this fast-growing field.

But did you know Cloudera training can also help you plan for the advanced stages and progress of your Hadoop cluster? In addition to core training for Developers and Administrators, we also offer the best (and, in some cases, only) opportunity to get up to speed on lifecycle projects within the Hadoop ecosystem in a classroom setting. Cloudera University’s course offerings go beyond the basics to include Training for Apache HBase, Training for Apache Hive and Pig, and Introduction to Data Science: Building Recommender Systems. Depending on your Big Data agenda, Cloudera training can help you increase the accessibility and queryability of your data, push your data performance towards real-time, conduct business-critical analyses using familiar scripting languages, build new applications and customer-facing products, and conduct data experiments to improve your overall productivity and profitability.

A Guide to Python Frameworks for Hadoop

I recently joined Cloudera after working in computational biology/genomics for close to a decade. My analytical work is primarily performed in Python, along with its fantastic scientific stack. It was quite jarring to find out that the Apache Hadoop ecosystem is primarily written in/for Java. So my first order of business was to investigate some of the options that exist for working with Hadoop from Python.

In this post, I will provide an unscientific, ad hoc review of my experiences with some of the Python frameworks that exist for working with Hadoop, including:

This Month in Data Science

Data science has been a ubiquitous topic of conversation in the IT and business worlds across the month of November. In this brief post, I’ll bring you just a small cross-section of the data science meme on the Interwebs in the past 4 weeks:

What’s New in CDH4.1 Mahout

Cloudera recently announced the general availability of CDH4.1, an update to our open-source, enterprise-ready distribution of Apache Hadoop and related projects. Among various components, Apache Mahout is a relatively recent addition to CDH (first added to CDH3u2 in 2011), but is already attracting increasing interest out in the field. 

Mahout started as a sub-project of Apache Lucene to provide machine-learning libraries in the area of clustering and classification. It later evolved into a top-level Apache project with much broader coverage of machine-learning techniques (clustering, classification, recommendation, frequent itemset mining etc.). 

See You at Data Science Day (Nov. 29, New York)!

[Updated Nov. 26, 2012: Sorry, this event has reached capacity and is now closed.]

Please join us in New York on Nov. 29, 2012, for a unique opportunity to hear from industry icons Jeff Hammerbacher (@hackingdata), Amr Awadallah (@awadallah) and Josh Wills (@josh_wills) as they discuss their approach to Data Science and how it transformed business for companies like Facebook, Yahoo! and Google. You will also hear more about Cloudera Enterprise: The Platform for Big Data powered by Cloudera Impala, which takes Hadoop “beyond batch” and into the world of real-time interactivity.

Training a New Generation of Data Scientists

Last week at Strata + Hadoop World 2012, we announced a new data science training and certification program. I am very excited to have been part of the team that put the program together, and I would like to answer some of the most frequently asked questions about the course and the certification that we will be offering.

Why is Cloudera offering data science training?

The primary bottleneck on the success of Hadoop is the number of people who are capable of using it effectively to solve business problems. Addressing that bottleneck with training has always been a very large part of our mission here at Cloudera, and we are very fortunate to have one of the best training teams anywhere. So far, we have trained over 15,000 Hadoop developers and administrators, and our courses and certification exams are available all over the world.

Data Science: The New Heart of Healthcare

We at Cloudera are tremendously excited by the power of data to effect large-scale change in the healthcare industry. Many of the projects that our data science team worked on in the past year originated as data-intensive problems in healthcare, such as analyzing adverse drug events and constructing case-control studies. Last summer, we announced that our Chief Scientist Jeff Hammerbacher would be collaborating with the Mt. Sinai School of Medicine to leverage large-scale data analysis with Apache Hadoop for the treatment and prevention of disease. And next week, it will be my great pleasure to host a panel of data scientists and researchers at the Strata Rx Conference (register with discount code SHARON for 25% off) to discuss the meaningful use of natural language processing in clinical care.

Of course, the cost-effective storage and analysis of massive quantities of text is one of Hadoop’s strengths, and Jimmy Lin’s book on text processing is an excellent way to learn how to think in MapReduce. But a close study of how the applications of natural language processing technology in healthcare have evolved over the last few years is instructive for anyone who wants to understand how to use data science in order to tackle seemingly intractable problems.

Lesson 1: Choose the Right Problem

Data Science: Hot or Not?

You may have noticed that Harvard Business Review is calling data science “the sexiest job of the 21st century.” So our answer to the question is: Hot. Definitely hot. 

If you need an explanation, watch the “Definition of a Data Scientist” talk embedded below from Cloudera data science director Josh Wills, which was hosted by Cloudera partner Lilien LLC recently in Portland, Ore. The key take-away is, you don’t literally have to be a “scientist,” just someone with the curiosity of one.