[A version of this post appears on the O’Reilly Strata blog.] Simple example of a near realtime app built with Hadoop and HBase Over the past year Hadoop emerged from its batch processing roots and began to take on interactive and near realtime applications. There are numerous examples that fall under these categories, but oneContinue reading “Near realtime, streaming, and perpetual analytics”
Tag Archives: real time
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!”
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”
Scalable streaming analytics using a single-server
[A version of this post appears on the O’Reilly Strata blog.] For many organizations real-time1 analytics entails complex event processing systems (CEP) or newer distributed stream processing frameworks like Storm, S4, or Spark Streaming. The latter have become more popular because they are able to process massive amounts of data, and fit nicely with HadoopContinue reading “Scalable streaming analytics using a single-server”
Data Science Tools: Fast, easy to use, and scalable
[A version of this post appears on the O’Reilly Strata blog.] Here are a few observations based on conversations I had during the just concluded Strata Santa Clara conference. Spark is attracting attention I’ve written numerous times about components of the Berkeley Data Analytics Stack (Spark, Shark, MLbase). Two Spark-related sessions at Strata were packedContinue reading “Data Science Tools: Fast, easy to use, and scalable”
Mining Time-series with Trillions of Points: Dynamic Time Warping at scale
Take a similarity measure that’s already well-known to researchers who work with time-series, and devise an algorithm to compute it efficiently at scale. Suddenly intractable problems become tractable, and Big Data mining applications that use the metric are within reach. The classification, clustering, and searching through time series have important applications in many domains. InContinue reading “Mining Time-series with Trillions of Points: Dynamic Time Warping at scale”