Hypnotic butt !
Hypnotic butt !
Gartner, Inc. has identified the top technology trends that will play key roles in modernizing information management (IM) in 2013 and beyond, making the role of information governance increasingly important.
According to the PR announcement accompanying the report, there are 9 technologies in this list:
- Big Data
- Modern Information Infrastructure
- Semantic Technologies
- The Logical Data Warehouse
- NoSQL DBMSs
- In-Memory Computing
- Chief Data Officer and Other Information-Centric Roles
- Information Stewardship Applications
- Information Valuation/Infonomics
Notable technologies missing:
- Data-oriented appliances
Note: the last two could be part of the larger category “Modern Information Infrastructure”, but without access to the report I cannot tell if they are part of it or not.
Note: Gartner touts the “Logical Data Warehouse” concept for a while and it superseeds analytic databases and data virtualization
Note: I could see though this being included in the “Information Stewardship Applications” category
Note: most probably this is part of the “Information Valuation/Infonomics” category.
I ended up wondering if there’s any data related technology left out from this list.
Original title and link: Top Technology Trends Impacting Information Infrastructure in 2013 According to Gartner ( ©myNoSQL)
Stephen Brobst of Teradata:
There are four primary reasons that big data projects fail:
- They focus on technology rather than business opportunities.
- They are unable to provide data access to subject matter experts.
- They fail to achieve enterprise adoption.
- The enterprise lacks the sophistication to understand that the project’s total cost of ownership includes people as well as information technology systems.
I tend to disagree with the last 3 points or at least not consider those as primary reasons.
Except the novelty of this new field and thus its inherent challenges of new technologies being used, I don’t think big data projects are any different to other projects. And their failures are caused by the same well known reasons. Maybe the only new one is the unreasonable expectations. But even this one doesn’t seem so new to me.
Original title and link: Why Big Data Projects Fail ( ©myNoSQL)
125 EC2 memcached instances, from which 90 for production and 35 for internal usage:
Another 90 EC2 instances are dedicated towards caching, through…
According to Michael Stonebraker and Jeremy Kepner the future of Hadoop is doomed:
Computational space Data Management Adopt Hadoop for pilot projects Adopt Hadoop for pilot projects Scale Hadoop to production use Scale Hadoop to production use Hit the wall, as the above…
An interesting post on Teradata Aster blog which is indirectly emphasizing the weaknesses of the Hadoop platform:
- Make platform and tools to be easier to use to manage and curate data. Otherwise, garbage in = garbage out, and you will get garbage analytics.
- Provide rich analytics functions out of the box. Each line of programming cuts your reachable audience by 50%.
- Provide tools to update or delete data. Otherwise, data consistency will drift away from truth as history accumulates.
- Provide applications to leverage data and find answers relevant to business. Otherwise the cost of DIY applications is too high to influence business – and won’t be done.
It’s difficult to argue against these points, but they are not insurmountable. I’d even say that once the operational complexity of Hadoop deployments will get simpler—I think the Apache community, Cloudera, and Hortonworks are already working on these aspects—, Hadoop will see even more adoption and with that contributions addressing points 2 to 4 will follow shortly.
Yet another interesting part of the post is the two “equations” describing the two environments:
big clusters = big administration = big programs = big friction = low influence (Hadoop) big data = small clusters = easy administration = big analytics = big influence (ideal/Teradata Aster)
I think these are revealing how Teradata Aster is positioning their solutions and where they see themselves making money in the Big Data market. It goes like this: “we can make a lot of money if we offer a platform with lower complexity and operational costs and higher productivity leading to better business results”. This is a sound strategy and the competitors from the Hadoop space should better focus on these same aspects which are essential to wide adoption.
Original title and link: Hadoop Weaknesses and Where Teradata Aster Sees the Big Data Money ( ©myNoSQL)
Google’s head of web spam, Matt Cutts, posted a 8 minute video on how Google search works. From crawling, indexing to ranking, he gets into a brief overview of how Google’s search engine does its job.