When, at some point in the distant future, we look back to the pioneering days of the golden age of data wrangling, a number of words that sound rather ridiculous without context, like Hadoop or Mesos, will filter into the conversation. …
In-memory databases are all the rage for very fast query processing, but you have to have the right balance of compute and memory for queries against in-memory databases to really scream. …
Hadoop started out as a batch-oriented system for chewing through massive amounts of unstructured data on the cheap, enabling all sorts of things such as search engines and advertising systems. …
One look at the IBM technology roadmap reveals that future emphasis, particularly on the software and applications side, will revolve around that catch-all trend that couples various elements from speech, text, and image recognition to create a brain-like system with reasoning and judgment capabilities–not to mention near-instant access to the world’s information. …
By the time Ashish Thusoo left Facebook in 2011, the company had grown to around 4,000 people, many of whom needed to access the roughly 150 petabytes of data—quite a hike from the 15 petabytes his team was trying to wrench from data warehouses in 2008. …
Although Spark has garnered a reputation as being a real-time analytics engine that is married to Hadoop, its life before being glued to that framework offers a different story. …
There is a disconnect between the database engines that underpin both relational databases and the SQL front ends that have been grafted onto Hadoop analytics tools and the underlying hardware on which these databases run. …
Enterprises like choices, they abhor vendor lock in, and they like the options that open source gives. …
No matter how inadvertently, DARPA has helped spawn a number of new companies and mainstream technologies over the years, including recognizable mainstays like the Siri speech recognition engine, which evolved from the artificial intelligence CALO (Cognitive Assistant that Learns and Organizes), a five year, $200 million backed effort backed by the agency. …
As a platform for doing analytics on large datasets that is much less costly than would be possible with parallel data warehouses, Hadoop and its myriad extensions and modified underpinnings has fulfilled its purpose. …
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