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.
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.
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.
The definition of an online video stream can mean different things on different sites. This kind of ambiguity hurts everyone involved.