ENTRIES TAGGED "real time"
Scalable streaming analytics using a single-server
The simplest and quickest way to mine your data is to deploy efficient algorithms designed to answer key questions at scale.
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 Hadoop and other cluster computing tools. For these distributed frameworks peak volume is function of network topology/bandwidth and the throughput of the individual nodes.
Scaling up machine-learning: Find efficient algorithms
Faced with having to crunch through a massive data set, the first thing a machine-learning expert will try to do is devise a more efficient algorithm. Some popular approaches involve sampling, online learning, and caching. Parallelizing an algorithm tends to be lower on the list of things to try. The key reason is that while there are algorithms that are embarrassingly parallel (e.g., naive bayes), many others are harder to decouple. But as I highlighted in a recent post, efficient tools that run on single servers can tackle large data sets. In the machine-learning context recent examples2 of efficient algorithms that scale to large data sets, can be found in the products of startup SkyTree.
On reading Mike Barlow’s “Real-Time Big Data Analytics: Emerging Architecture”
Barlow's distilled insights regarding the ever evolving definition of real time big data analytics
Reading Barlow on a Sunday afternoon
During a break in between offsite meetings that Edd and I were attending the other day, he asked me, “did you read the Barlow piece?”
“Umm, no.” I replied sheepishly. Insert a sidelong glance from Edd that said much without saying anything aloud. He’s really good at that.
In my utterly meager defense, Mike Loukides is the editor on Mike Barlow’s Real-Time Big Data Analytics: Emerging Architecture. As Loukides is one of the core drivers behind O’Reilly’s book publishing program and someone who I perceive to be an unofficial boss of my own choosing, I am not really inclined to worry about things that I really don’t need to worry about. Then I started getting not-so-subtle inquiries from additional people asking if I would consider reviewing the manuscript for the Strata community site. This resulted in me emailing Loukides for a copy and sitting in a local cafe on a Sunday afternoon to read through the manuscript.
Real-time data needs to power the business side, not just tech
Theo Schlossnagle on the state of real-time data analysis and where it needs to go.
Real-time data analysis has come a long way, but Theo Schlossnagle, principal and CEO of OmniTI, says some technology improvements are actually causing a data analysis devolution.
Strata Gems: Three key data trends for 2011
Data markets, real-time technology, and the race for developers
To conclude our Strata Gems series, we take a look at the important drivers for the data world in 2011: data markets, real-time data processing, and developers.
Counting Unique Users in Real-time with Streaming Databases
As the web increasingly becomes real-time, marketers and publishers need analytic tools that can produce real-time reports. As an example, the basic task of calculating the number of unique users is typically done in batch mode (e.g. daily) and in many cases using a random sample from relevant log files. If unique user counts can be accurately computed in real-time, publishers and marketers can mount A/B tests or referral analysis to dynamically adjust their campaigns.
Pipelining and Real-time Analytics with MapReduce Online
Some organizations create their own real-time analysis tools, while others turn to specialized solutions. In a previous post, I highlighted SQL-based real-time analytic tools that can handle large amounts of data. I noted that other big data management systems such as MPP databases and MapReduce/Hadoop were too batch-oriented to deliver analysis in near real-time. At least for MapReduce/Hadoop systems things may have changed slightly. A group of researchers from UC Berkeley and Yahoo recently modified MapReduce to allow for pipelining between operators.
Big Data and Real-time Structured Data Analytics
The emergence of sensors as sources of Big Data highlights the need for real-time analytic tools. Popular web apps like Twitter, Facebook, and blogs are also faced with having to analyze (mostly unstructured) data in near real-time. But as Truviso founder and UC Berkeley CS Professor Michael Franklin recently noted, there are mountains of structured data generated by web apps that lend themselves to real-time analysis.
