Ben Lorica

Ben Lorica is the Chief Data Scientist and Director of Content Strategy for Data at O'Reilly Media, Inc. 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.

Jai Ranganathan on architecting big data applications in the cloud

The O’Reilly Data Show podcast: The Hadoop ecosystem, the recent surge in interest in all things real time, and developments in hardware.

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


Given the quick pace of innovation in the data ecosystem, we like to take a step back from the details of individual components, architecture, and applications, in order to take a wider view of the landscape of big data. This allows us to evaluate the progress of technology and infrastructure along the way, shifting our attention from the details of individual components like Spark and Kafka, to larger trends.

Some of the larger trends we’ve been exploring include the capabilities of distributed machine learning and the tradeoffs and design decisions involved in cloud architecture and stream processing.

In this episode of the O’Reilly Data Show, I sat down with Jai Ranganathan, senior director of product management at Cloudera. We talked about the trends in the Hadoop ecosystem, cloud computing, the recent surge in interest in all things real time, and hardware trends:

Large-scale machine learning

This sounds a bit like this should already exist in really good form right now, but one of the things that I’m really interested in is expanding the set of capabilities for distributed machine learning. While there are systems out there today that do do this, I think relative to what you can experience from a singular environment learning scikit-learn or R, the set of things you can do in a distributed fashion is limited. …  It’s not easy to distribute various algorithms and model-building techniques. I think there is still a lot of work for us to do to improve that experience. … And I do want to have good open source options like MLlib. MLlib may be the right answer. I would be perfectly happy if that’s the final answer, but we do need systems just to provide the kind of depth that you typically are used to in the singular environment. That’s just a matter of time and investment because these are non-trivial problems, but they are things that people are working on.

Read more…

Comment: 1

Building systems for massive scale data applications

The O’Reilly Data Show podcast: Tyler Akidau on the evolution of systems for bounded and unbounded data processing.

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


Many of the open source systems and projects we’ve come to love — including Hadoop and HBase — were inspired by systems used internally within Google. These systems were described in papers and implemented by people who needed frameworks that could comfortably scale to massive data sets.

Google engineers and scientists continue to publish interesting papers, and these days some of the big data systems they describe in publications are available on their cloud platform.

In this episode of the O’Reilly Data Show, I sat down with Tyler Akidau one of the lead engineers in Google’s streaming and Dataflow technologies. He recently wrote an extremely popular article that provided a framework for how to think about bounded and unbounded data processing (a follow-up article is due out soon). We talked about the evolution of stream processing, the challenges of building systems that scale to massive data sets, and the recent surge in interest in all things real time:

On the need for MillWheel: A new stream processing engine

At the time [that MillWheel was built], there was, as far as I know, literally nothing externally that could handle the scale that we needed to handle. A lot of the existing streaming systems didn’t focus on out-of-order processing, which was a big deal for us internally. Also we really wanted to hit a strong focus on consistency — being able to get absolutely correct answers. … All three of these things were lacking in at least some area in [the systems we examined].

Read more…


Turning big data into actionable insights

The O’Reilly Data Show podcast: Evangelos Simoudis on data mining, investing in data startups, and corporate innovation.

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


Can developments in data science and big data infrastructure drive corporate innovation? To be fair, many companies are still in the early stages of incorporating these ideas and tools into their organizations.

Evangelos Simoudis has spent many years interacting with entrepreneurs and executives at major global corporations. Most recently, he’s been advising companies interested in developing long-term strategies pertaining to big data, data science, cloud computing, and innovation. He began his career as a data mining researcher and practitioner, and is counted among the pioneers who helped data mining technologies get adopted in industry.

In this episode of the O’Reilly Data Show, I sat down with Simoudis and we talked about his thoughts on investing, data applications and products, and corporate innovation:

Open source software companies

I very much appreciate open source. I encourage my portfolio companies to use open source components as appropriate, but I’ve never seen the business model as being one that is particularly easy to really build the companies around them. Everybody points to Red Hat, and that may be the exception, but I have not seen companies that have, on the one hand, remained true to the open source principles and become big and successful companies that do not require constant investment. … The revenue streams never prove to be sufficient for building big companies. I think the companies that get started from open source in order to become big and successful … [are] ones that, at some point, decided to become far more proprietary in their model and in the services that they deliver. Or they become pure professional services companies as opposed to support services companies. Then they reach the necessary levels of success.

Read more…


Resolving transactional access and analytic performance trade-offs

The O’Reilly Data Show podcast: Todd Lipcon on hybrid and specialized tools in distributed systems.

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

350px-Dolderbrug_Steenwijk_inclusief_lichtontwerpIn recent months, I’ve been hearing about hybrid systems designed to handle different data management needs. At Strata + Hadoop World NYC last week, Cloudera’s Todd Lipcon unveiled an open source storage layer — Kudu —  that’s good at both table scans (analytics) and random access (updates and inserts).

While specialized systems will continue to serve companies, there will be situations where the complexity of maintaining multiple systems — to eke out extra performance — will be harder to justify.

During the latest episode of the O’Reilly Data Show Podcast, I sat down with Lipcon to discuss his new project a few weeks before it was released. Here are a few snippets from our conversation:

HDFS and Hbase

[Hadoop is] more like a file store. It allows you to upload files onto an arbitrarily sized cluster with 20-plus petabytes, in single clusters. The thing is, you can upload the files but you can’t edit them in place. To make any change, you have to basically put in a new file. What HBase does in distinction is that it has more of a tabular data model, where you can update and insert individual row-by- row data, and then randomly access that data [in] milliseconds. The distinction here is that HDFS is pretty good for large scans where you’re putting in a large data set, maybe doing a full parse over the data set to train a machine learning model or compute an aggregate. If any of that data changes on a frequent basis or if you want to stream the data in or randomly access individual customer records, you’re kind of out of luck on HDFS. Read more…


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).

Read more…

Comment: 1

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.

Read more…


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…

Comment: 1

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…

Comments: 4