Oracle Open World: Beginning to Focus on the Middle Word (includes video)

Sometimes words can be misleading – let me give an example from my background: in England a "public school" is one you pay for, and therefore what most of the world would consider private! The reason is centuries old and based in how different types of schools were established. The early "public" schools in the UK were public in as much as – unlike local free schools that were invariably tied to a manor or church - they were open to anyone with the money to pay! One of the biggest takeaways from last week’s Oracle Open World is that its use of the word “Open” – that I’d long assumed had a similar logic to those early English schools – is genuinely something Oracle is working on!

Topics: Storage Big Data Oracle Oracle Open World 2016

ESG Video Capsule: Upcoming Research on Database Market Trends

Preview the topics of ESG's upcoming research project examining who the buyers and influencers of database decisions are and what drives their evaluation by watching this ESG Video Capsule:

Topics: Big Data Data Management database

IT Operations Analytics from the Source


One of the top uses of big data today is IT operations analytics. This makes sense. By nature, IT components are designed to log all of their many status messages, and this information is generated with debug, tracking, and audit purposes in mind. The aggregate output, however, can be a logistical problem in itself. For each device, some poor sysadmin has to decide what level of logging is desired, and then live with the consequences of that decision. Set the logging threshold to "errors only" and important context will be missing when it's time to diagnose an issue. Set the logging criteria to "everything" and staggering amounts of data will be generated, often too much to process, and certainly much of little or no value. Limit the time period to an hour or a day, and the key information may have been overwritten by the time it's needed, and then the problem will have to be recreated.

Topics: Big Data Data Management Data Analytics IT operations analytics

Understanding Poetry at the HPE Big Data Conference

HPE's Big Data Conference was given the tag line #SeizeTheData which immediately made me think of the wonderful film "Dead Poets Society." One of my favorite scenes is when the students learn how to measure and analyze poetry. You can refresh your memory of the dialogue by watching this clip  or reading here. Of course, the whole point is that using analytics doesn't work in poetry appreciation. Which I thought made #SeizeTheData rather ironic as a hashtag. 

Topics: Big Data Data Management HPE

"Big data, huh, what is it good for?" at the Hadoop Summit

The mood of this week’s Hadoop Summit has felt wonderfully diverse. There is a cognitive disconnect between the incremental progress of dot release feature sets and the revolutionary new business and societal applications of the technology.

In the same keynote session the topics can swerve from optimizing cluster utilization to optimizing marketing yields to finding a cure for cancer. The technical lectures were packed, while the expo floor was focused. A loud rock and roll string trio in (unnecessarily short) black dresses exits the stage to be replaced by serious talk of open-source projects and community. One day a presenter will explain how clever they are to be able to apply pervasive surveillance of drivers for more profits, the next day a keynote is focused on rallying the audience to develop their ethics and fight the forces of ignorance.

Topics: Big Data Hadoop Data Management

IBM and Cisco partner for edge-to-cloud IoT analytics

On June 2nd, IBM and Cisco announced a partnership for IoT analytics which aims to combine IBM's cognitive computing analytics in the cloud with Cisco's fog computing analytics at the edge. Overall, the combination is a positive one for IoT and analytics decisions makers since it begins to help clarify the IoT data journey story.

Topics: Analytics Big Data IoT partners

IoT World 2016 — an ecosystem in flux

I recently attended IoT World, held May 11-13th in Santa Clara. As you might expect, there was a lot of energy at the event, and enthusiasm for IoT. And it was well-attended, with reportedly more than 10,000 people expected over the course of the three days of the exhibition.

Topics: Analytics Big Data Internet of Things IoT

EMC, Dell, Pivotal, Microsoft, and ... Ford?

Last week was interesting. I spent most of it in Las Vegas at EMC World, which was as much about Dell as it was about EMC itself. There was a ceremonial handoff from Joe Tucci to Michael Dell to lead the new combined entity, but Jeremy Burton was perhaps even more in the spotlight as he outlined the vision.

Much of this vision was about the balance between traditional data center environments and something he called "cloud-native" applications. Substituting "next-gen" for "cloud-native" might be more accurate, as this category included everything from PaaS to big data to containers to hybrid clouds. Hadoop, Cassandra, and MongoDB were cited as examples as cloud-native, which felt odd. Certainly they are cloud-friendly, but they're by no means exclusively cloud-centric. See my last post on cloud big data for more thoughts on this topic.

Topics: Microsoft EMC Big Data Dell Data Management pivotal

A curious reversal is happening around cloud-based big data

Remember when the cloud was just getting going? Somehow it morphed from the traditional concepts of hosting and XSP (I always hated that acronym) into a new class of service. Virtualization and management tools were a big part of facilitating this transformation, as was the sudden shift in economics. The problem with hosting and "other" managed services was it usually wasn't any more agile or any less expensive than doing it yourself.

Topics: Cloud Computing Big Data Data Management

Data is getting boring again

For the last five years, the term "big data" has evoked heady dreams of transforming business as we know it. The use cases were as broad and brash as as the imaginations of senior executives. Data scientists became rockstars and could name their own salaries. New approaches to storage and analytics, including distributed workloads on commodity hardware with open-source software, meant the performance and economics of extracting insights from data was radically shifted. The future's so bright, we've got to wear shades.

Topics: Analytics Big Data Data Management