Ben Lorica

Ben Lorica is the Chief Data Scientist of O'Reilly Media, Inc. and Program Director of Strata+Hadoop World. He has applied Business Intelligence, Data Mining, Machine Learning and Statistical Analysis in a variety of settings including Direct Marketing, Consumer and Market Research, Targeted Advertising, Text Mining, and Financial Engineering. His background includes stints with an investment management company, internet startups, and financial services.

Specialized and hybrid data management and processing engines

A new crop of interesting solutions for the complexity of operating multiple systems in a distributed computing setting.


The 2004 holiday shopping season marked the start of Amazon’s investigation into alternative database technologies that led to the creation of DynamoDB — a key-value storage system that went onto inspire several NoSQL projects.

A new group of startups began shifting away from the general-purpose systems favored by companies just a few years earlier. In recent years, we’ve seen a diverse set of DBMS technologies that specialize in handling particular workloads and data models such as OLTP, OLAP, search, RDF, XML, scientific applications, etc. The success and popularity of such systems reinforced the belief that in order to scale and “go fast,” specialized systems are preferable.

In distributed computing, the complexity of maintaining and operating multiple specialized systems has recently led to systems that bridge multiple workloads and data models. Aside from multi-model databases, there are an emerging number of storage and compute engines adept at handling different workloads and problems. At this week’s Strata + Hadoop World conference in NYC, I had a chance to interact with the creators of some of these new solutions.

OLTP (transactions) and OLAP (analytics)

One of the key announcements at Strata + Hadoop World this week was Project Kudu — an open source storage engine that’s good at both table scans (analytics) and random access (updates and inserts). Its creators are quick to point out that they aren’t out to beat specialized OLTP and OLAP systems. Rather, they’re shooting to build a system that’s “70-80% of the way there on both axes.” The project is very young and lacks enterprise features, but judging from the reaction at the conference, it’s something the big data community will be watching. Leading technology research firms have created a category for systems with related capabilities:  HTAP (Gartner) and Trans-analytics (Forrester).

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Building enterprise data applications with open source components

The O’Reilly Data Show podcast: Dean Wampler on bounded and unbounded data processing and analytics.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.


I first found myself having to learn Scala when I started using Spark (version 0.5). Prior to Spark, I’d peruse books on Scala but just never found an excuse to delve into it. In the early days of Spark, Scala was a necessity — I quickly came to appreciate it and have continued to use it enthusiastically.

For this Data Show Podcast, I spoke with O’Reilly author and Typesafe’s resident big data architect Dean Wampler about Scala and other programming languages, the big data ecosystem, and his recent interest in real-time applications. Dean has years of experience helping companies with large software projects, and over the last several years, he’s focused primarily on helping enterprises design and build big data applications.

Here are a few snippets from our conversation:

Apache Mesos & the big data ecosystem

It’s a very nice capability [of Spark] that you can actually run it on a laptop when you’re developing or working with smaller data sets. … But, of course, the real interesting part is to run on a cluster. You need some cluster infrastructure and, fortunately, it works very nicely with YARN. It works very nicely on the Hadoop ecosystem. … The nice thing about Mesos over YARN is that it’s a much more flexible, capable resource manager. It basically treats your cluster as one giant machine of resources and gives you that illusion, ignoring things like network latencies and stuff. You’re just working with a giant machine and it allocates resources to your jobs, multiple users, all that stuff, but because of its greater flexibility, it cannot only run things like Spark jobs, it can run services like HDFS or Cassandra or Kafka or any of these tools. … What I saw was there was a situation here where we had maybe a successor to YARN. It’s obviously not as mature an ecosystem as the Hadoop ecosystem but not everybody needs that maturity. Some people would rather have the flexibility of Mesos or of solving more focused problems.

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From search to distributed computing to large-scale information extraction

The O'Reilly Data Show Podcast: Mike Cafarella on the early days of Hadoop/HBase and progress in structured data extraction.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.

The_Wonders_of_The_World_British_Library_FlickrFebruary 2016 marks the 10th anniversary of Hadoop — at a point in time when many IT organizations actively use Hadoop, and/or one of the open source, big data projects that originated after, and in some cases, depend on it.

During the latest episode of the O’Reilly Data Show Podcast, I had an extended conversation with Mike Cafarella, assistant professor of computer science at the University of Michigan. Along with Strata + Hadoop World program chair Doug Cutting, Cafarella is the co-founder of both Hadoop and Nutch. In addition, Cafarella was the first contributor to HBase

We talked about the origins of Nutch, Hadoop (HDFS, MapReduce), HBase, and his decision to pursue an academic career and step away from these projects. Cafarella’s pioneering contributions to open source search and distributed systems fits neatly with his work in information extraction. We discussed a new startup he recently co-founded, ClearCutAnalytics, to commercialize a highly regarded academic project for structured data extraction (full disclosure: I’m an advisor to ClearCutAnalytics). As I noted in a previous post, information extraction (from a variety of data types and sources) is an exciting area that will lead to the discovery of new features (i.e., variables) that may end up improving many existing machine learning systems. Read more…

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Showcasing the real-time processing revival

Tools and learning resources for building intelligent, real-time products.

Earth orbiting sun illustration

Register for Strata + Hadoop World NYC, which will take place September 29 to Oct 1, 2015.

A few months ago, I noted the resurgence in interest in large-scale stream-processing tools and real-time applications. Interest remains strong, and if anything, I’ve noticed growth in the number of companies wanting to understand how they can leverage the growing number of tools and learning resources to build intelligent, real-time products.

This is something we’ve observed using many metrics, including product sales, the number of submissions to our conferences, and the traffic to Radar and newsletter articles.

As we looked at putting together the program for Strata + Hadoop World NYC, we were excited to see a large number of compelling proposals on these topics. To that end, I’m pleased to highlight a strong collection of sessions on real-time processing and applications coming up at the event. Read more…


Bridging the divide: Business users and machine learning experts

The O'Reilly Data Show Podcast: Alice Zheng on feature representations, model evaluation, and machine learning models.

Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.

606px-IBM_Electronic_Data_Processing_Machine_-_GPN-2000-001881As tools for advanced analytics become more accessible, data scientist’s roles will evolve. Most media stories emphasize a need for expertise in algorithms and quantitative techniques (machine learning, statistics, probability), and yet the reality is that expertise in advanced algorithms is just one aspect of industrial data science.

During the latest episode of the O’Reilly Data Show podcast, I sat down with Alice Zheng, one of Strata + Hadoop World’s most popular speakers. She has a gift for explaining complex topics to a broad audience, through presentations and in writing. We talked about her background, techniques for evaluating machine learning models, how much math data scientists need to know, and the art of interacting with business users.

Making machine learning accessible

People who work at getting analytics adopted and deployed learn early on the importance of working with domain/business experts. As excited as I am about the growing number of tools that open up analytics to business users, the interplay between data experts (data scientists, data engineers) and domain experts remains important. In fact, human-in-the-loop systems are being used in many critical data pipelines. Zheng recounts her experience working with business analysts:

It’s not enough to tell someone, “This is done by boosted decision trees, and that’s the best classification algorithm, so just trust me, it works.” As a builder of these applications, you need to understand what the algorithm is doing in order to make it better. As a user who ultimately consumes the results, it can be really frustrating to not understand how they were produced. When we worked with analysts in Windows or in Bing, we were analyzing computer system logs. That’s very difficult for a human being to understand. We definitely had to work with the experts who understood the semantics of the logs in order to make progress. They had to understand what the machine learning algorithms were doing in order to provide useful feedback. Read more…

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Pattern recognition and sports data

The O'Reilly Data Show Podcast: Award-winning journalist David Epstein on the (data) science of sports.

Sign-up now to receive a free download of the new O’Reilly report “Data Analytics in Sports: How Playing with Data Transforms the Game” when it publishes this fall.


Julien Vervaecke and Maurice Geldhof smoking a cigarette at the 1927 Tour de France. Public domain photo via Wikimedia Commons.

One of my favorite books from the last few years is David Epstein’s engaging tour through sports science using examples and stories from a wide variety of athletic endeavors. Epstein draws on examples from individual sports (including track and field, winter sports) and major U.S. team sports (baseball, basketball, and American football), and uses the latest research to explain how data and science are being used to improve athletic performance.

In a recent episode of the O’Reilly Data Show Podcast, I spoke with Epstein about his book, data science and sports, and his recent series of articles detailing suspicious practices at one of the world’s premier track and field training programs (the Oregon Project).

Nature/nurture and hardware/software

Epstein’s book contains examples of sports where athletes with certain physical attributes start off with an advantage. In relation to that, we discussed feature selection and feature engineering — the relative importance of factors like training methods, technique, genes, equipment, and diet — topics which Epstein has written about and studied extensively:

One of the most important findings in sports genetics is that your ability to improve with respect to a certain training program is mediated by your genes, so it’s really important to find the kind of training program that’s best tailored to your physiology. … The skills it takes for team sports, these perceptual skills, nobody is born with those. Those are completely software, to use the computer analogy. But it turns out that once the software is downloaded, it’s like a computer. While your hardware doesn’t do anything alone without software, once you’ve got the software, the hardware actually makes a lot of a difference in how good of an operating machine you have. It can be obscured when people don’t study it correctly, which is why I took on some of the 10,000 hours stuff. Read more…


Understanding neural function and virtual reality

The O'Reilly Data Show Podcast: Poppy Crum explains that what matters is efficiency in identifying and emphasizing relevant data.


Like many data scientists, I’m excited about advances in large-scale machine learning, particularly recent success stories in computer vision and speech recognition. But I’m also cognizant of the fact that press coverage tends to inflate what current systems can do, and their similarities to how the brain works.

During the latest episode of the O’Reilly Data Show Podcast, I had a chance to speak with Poppy Crum, a neuroscientist who gave a well-received keynote at Strata + Hadoop World in San Jose. She leads a research group at Dolby Labs and teaches a popular course at Stanford on Neuroplasticity in Musical Gaming. I wanted to get her take on AI and virtual reality systems, and hear about her experience building a team of researchers from diverse disciplines.

Understanding neural function

While it can sometimes be nice to mimic nature, in the case of the brain, machine learning researchers recognize that understanding and identifying the essential neural processes is much more critical. A related example cited by machine learning researchers is flight: wing flapping and feathers aren’t critical, but an understanding of physics and aerodynamics is essential.

Crum and other neuroscience researchers express the same sentiment. She points out that a more meaningful goal should be to “extract and integrate relevant neural processing strategies when applicable, but also identify where there may be opportunities to be more efficient.”

The goal in technology shouldn’t be to build algorithms that mimic neural function. Rather, it’s to understand neural function. … The brain is basically, in many cases, a Rube Goldberg machine. We’ve got this limited set of evolutionary building blocks that we are able to use to get to a sort of very complex end state. We need to be able to extract when that’s relevant and integrate relevant neural processing strategies when it’s applicable. We also want to be able to identify that there are opportunities to be more efficient and more relevant. I think of it as table manners. You have to know all the rules before you can break them. That’s the big difference between being really cool or being a complete heathen. The same thing kind of exists in this area. How we get to the end state, we may be able to compromise, but we absolutely need to be thinking about what matters in neural function for perception. From my world, where we can’t compromise is on the output. I really feel like we need a lot more work in this area. Read more…

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6 reasons why I like KeystoneML

The O'Reilly Data Show Podcast: Ben Recht on optimization, compressed sensing, and large-scale machine learning pipelines.


As we put the finishing touches on what promises to be another outstanding Hardcore Data Science Day at Strata + Hadoop World in New York, I sat down with my co-organizer Ben Recht for the the latest episode of the O’Reilly Data Show Podcast. Recht is a UC Berkeley faculty member and member of AMPLab, and his research spans many areas of interest to data scientists including optimization, compressed sensing, statistics, and machine learning.

At the 2014 Strata + Hadoop World in NYC, Recht gave an overview of a nascent AMPLab research initiative into machine learning pipelines. The research team behind the project recently released an alpha version of a new software framework called KeystoneML, which gives developers a chance to test out some of the ideas that Recht outlined in his talk last year. We devoted a portion of this Data Show episode to machine learning pipelines in general, and a discussion of KeystoneML in particular.

Since its release in May, I’ve had a chance to play around with KeystoneML and while it’s quite new, there are several things I already like about it:

KeystoneML opens up new data types

Most data scientists don’t normally play around with images or audio files. KeystoneML ships with easy to use sample pipelines for computer vision and speech. As more data loaders get created, KeystoneML will enable data scientists to leverage many more new data types and tackle new problems. Read more…

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Why data preparation frameworks rely on human-in-the-loop systems

The O'Reilly Data Show Podcast: Ihab Ilyas on building data wrangling and data enrichment tools in academia and industry.


As I’ve written in previous posts, data preparation and data enrichment are exciting areas for entrepreneurs, investors, and researchers. Startups like Trifacta, Tamr, Paxata, Alteryx, and CrowdFlower continue to innovate and attract enterprise customers. I’ve also noticed that companies — that don’t specialize in these areas — are increasingly eager to highlight data preparation capabilities in their products and services.

During a recent episode of the O’Reilly Data Show Podcast, I spoke with Ihab Ilyas, professor at the University of Waterloo and co-founder of Tamr. We discussed how he started working on data cleaning tools, academic database research, and training computer science students for positions in industry.

Academic database research in data preparation

Given the importance of data integrity, it’s no surprise that the database research community has long been interested in data preparation and data wrangling. Ilyas explained how his work in probabilistic databases led to research projects in data cleaning:

In the database theory community, these problems of handling, dealing with data inconsistency, and consistent query answering have been a celebrated area of research. However, it has been also difficult to communicate these results to industry. And database practitioners, if you like, they were more into the well-structured data and assuming a lot of good properties around this data, [and they were also] more interested in indexing this data, storing it, moving it from one place to another. And now, dealing with this large amount of diverse heterogeneous data with tons of errors, sidled across all business units in the same enterprise became a necessity. You cannot really avoid that anymore. And that triggered a new line of research for pragmatic ways of doing data cleaning and integration. … The acquisition layer in that stack has to deal with large sets of formats and sources. And you will hear about things like adapters and source adapters. And it became a market on its own, how to get access and tap into these sources, because these are kind of the long tail of data.

The way I came into this subject was also funny because we were talking about the subject called probabilistic databases and how to deal with data uncertainty. And that morphed into trying to find data sets that have uncertainty. And then we were shocked by how dirty the data is and how data cleaning is a task that’s worth looking at.

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Building self-service tools to monitor high-volume time-series data

The O'Reilly Data Show Podcast: Phil Liu on the evolution of metric monitoring tools and cloud computing.

One of the main sources of real-time data processing tools is IT operations. In fact, a previous post I wrote on the re-emergence of real-time, was to a large extent prompted by my discussions with engineers and entrepreneurs building monitoring tools for IT operations. In many ways, data centers are perfect laboratories in that they are controlled environments managed by teams willing to instrument devices and software, and monitor fine-grain metrics.

During a recent episode of the O’Reilly Data Show Podcast, I caught up with Phil Liu, co-founder and CTO of SignalFx, a SF Bay Area startup focused on building self-service monitoring tools for time series. We discussed hiring and building teams in the age of cloud computing, building tools for monitoring large numbers of time series, and lessons he’s learned from managing teams at leading technology companies.

Evolution of monitoring tools

Having worked at LoudCloud, Opsware, and Facebook, Liu has seen first hand the evolution of real-time monitoring tools and platforms. Liu described how he has watched the number of metrics grow, to volumes that require large compute clusters:

One of the first services I worked on at LoudCloud was a service called MyLoudCloud. Essentially that was a monitoring portal for all LoudCloud customers. At the time, [the way] we thought about monitoring was still in a per-instance-oriented monitoring system. [Later], I was one of the first engineers on the operational side of Facebook and eventually became part of the infrastructure team at Facebook. When I joined, Facebook basically was using a collection of open source software for monitoring and configuration, so these are things that everybody knows — Nagios, Ganglia. It started out basically using just per-instance instant monitoring techniques, basically the same techniques that we used back at LoudCloud, but interestingly and very quickly as Facebook grew, this per-instance-oriented monitoring no longer worked because we went from tens or thousands of servers to hundreds of thousands of servers, from tens of services to hundreds and thousands of services internally.

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