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.

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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].

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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.

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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.

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Get started with cloud-based data science

Learn how to deploy machine learning solutions using Azure ML.

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Download the free, updated report “Data Science in the Cloud with Microsoft Azure Machine Learning and R: 2015 Update.

Cloud-based machine learning platforms, like Microsoft’s Azure Machine Learning (Azure ML), provide a simplified path to create and deploy analytic solutions. Azure ML is a fully managed and secure machine learning platform that resides within the Microsoft Cortana Analytics Suite.

Azure ML workflows (known as “experiments”) are constructed using a combination of drag-and-drop modules, SQL, R, and Python scripts. The wide range of built modules support the typical steps in a machine learning workflow, from data ingestion and data munging to model construction and cross validation.

Once your Azure ML experiment is ready, there are several options to deploy it. Azure ML experiments can access large-scale data stored in Azure Blob storage, Azure SQL and Hive, to name a few options. Similarly, your experiment can write results back to multiple scalable Azure storage options.

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Movement data is going to transform everything

The O'Reilly Radar Podcast: Rajiv Maheswaran on the science of moving dots, and Claudia Perlich on big data in advertising.

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Subscribe to the O’Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come.

In this week’s Radar Podcast episode, O’Reilly’s Mac Slocum chats with Rajiv Maheswaran, CEO of Second Spectrum. Maheswaran talks about machine learning applications in sports, the importance of context in measuring stats, and the future of real-time, in-game analytics.

Here are some highlights from their chat:

There’s a lot of parts of the game of basketball — pick and rolls, dribble hand-offs — that coaches really care about, about analyzing how it works on offense, how to guard them. Before big data and machine learning, people basically watched the games and marked them. It turns out that people are pretty bad at marking them accurately, and they also miss a ton of stuff. Right now, machine learning tells coaches, ‘This is how many pick and rolls these two players have had over the course of the season, how often they do all the different variations, what they’re good at, what they’re bad at.’ Coaches can really find tendencies that can help them play offense, play defense, far more efficiently, based off of machine learning.

What we’re doing is having the machine match human intuition. If I’m watching a game, I know that the shot is harder if I’m farther away, if I have multiple defenders, if they’re close, if they’re closing in on me, if I’m dribbling, the type of shot I’m taking. As a human, I watch this and I have an intuition about it. Now, by giving all that data to the machine, it can make a predictor that actually matches our intuition, and goes beyond it because it can put a number onto what our intuition tells us.

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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…

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