[A version of this post appears on the O’Reilly Strata blog.] What happens after data scientists build analytic models? Model deployment, monitoring, and maintenance are topics that haven’t received as much attention in the past, but I’ve been hearing more about these subjects from data scientists and software developers. I remember the days when itContinue reading “Data scientists tackle the analytic lifecycle”
Author Archives: Ben Lorica
Pattern-detection and Twitter’s Streaming API
[A version of this post appears on the O’Reilly Strata blog.] Researchers and companies who need social media data frequently turn to Twitter’s API to access a random sample of tweets. Those who can afford to pay (or have been granted access) use the more comprehensive feed (the firehose) available through a group of certifiedContinue reading “Pattern-detection and Twitter’s Streaming API”
Moving from Batch to Continuous Computing at Yahoo!
[A version of this post appeared on the O’Reilly Strata blog.] My favorite session at the recent Hadoop Summit was a keynote by Bruno Fernandez-Ruiz, Senior Fellow & VP Platforms at Yahoo! He gave a nice overview of their analytic and data processing stack, and shared some interesting factoids about the scale of their bigContinue reading “Moving from Batch to Continuous Computing at Yahoo!”
Analytic engines that factor in security labels
[A version of this post appears on the O’Reilly Strata blog.] Originated by the NSA, Apache Accumulo is a BigTable inspired data store known for being highly scalable and for its interesting security model. Federal agencies and Defense contractors have deployed Accumulo on clusters of a thousand or more servers. It also uses “cell-level” securityContinue reading “Analytic engines that factor in security labels”
HBase looks more appealing to data scientists
[A version of this post appears on the O’Reilly Strata blog.] When Hadoop users need to develop apps that are “latency sensitive”, many of them turn to HBase1. Its tight integration with Hadoop makes it a popular data store for real-time applications. When I attended the first HBase conference last year, I was pleasantly surprisedContinue reading “HBase looks more appealing to data scientists”
It’s getting easier to build Big Data Applications
[A version of this post appears on the O’Reilly Strata blog.] Hadoop’s low-cost, scale-out architecture has made it a new platform for data storage. With a storage system in place, the Hadoop community is slowly building a collection of open source, analytic engines. Beginning with batch processing (MapReduce, Pig, Hive), Cloudera has added interactive SQLContinue reading “It’s getting easier to build Big Data Applications”
Tracking the progress of large-scale Query Engines
[A version of this post appears on the O’Reilly Strata blog.] As organizations continue to accumulate data, there has been renewed interest in interactive query engines that scale to terabytes (even petabytes) of data. Traditional MPP databases remain in the mix, but other options are attracting interest. For example, companies willing to upload data intoContinue reading “Tracking the progress of large-scale Query Engines”
How signals, geometry, and topology are influencing data science
[A version of this post appears on the O’Reilly Strata blog.] I’ve been noticing unlikely areas of mathematics pop-up in data analysis. While signal processing is a natural fit, topology, differential and algebraic geometry aren’t exactly areas you associate with data science. But upon further reflection perhaps it shouldn’t be so surprising that areas thatContinue reading “How signals, geometry, and topology are influencing data science”
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Improving options for unlocking your graph data
[A version of this post appears on the O’Reilly Strata blog.] The popular open source project GraphLab received a major boost early this week when a new company comprised of its founding developers, raised funding to develop analytic tools for graph data sets. GraphLab Inc. will continue to use the open source GraphLab to “pushContinue reading “Improving options for unlocking your graph data”