Welcome!

Microservices Expo Authors: Stackify Blog, Automic Blog, Simon Hill, Pat Romanski, Liz McMillan

Related Topics: @DXWorldExpo, Microservices Expo, Containers Expo Blog, @CloudExpo, SDN Journal, @DevOpsSummit

@DXWorldExpo: Article

Shots Across the Data Lake

Big Data Analytics Range War

Range Wars
The settling of the American West brought many battles between ranchers and farmers over access to water. The farmers claimed land near the water and fenced it to protect their crops. But the farmers' fences blocked the ranchers' cattle from reaching the water. Fences were cut; shots were fired; it got ugly.

About a century later, with the first tech land rush of the late1980s and early '90s - before the Web - came battles between those who wanted software and data to be centrally controlled on corporate servers and those who wanted it to be distributed to workers' desktops. Oracle and IBM versus Microsoft and Lotus. Database versus Spreadsheet.

Now, with the advent of SoMoClo (Social, Mobile, Cloud) technologies and the Big Data they create, have come battles between groups on different sides of the "Data Lake" over how it should be controlled, managed, used, and paid for. Operations versus Strategy. BI versus Data Science. Governance versus Discovery.  Oversight versus Insight.

The range wars of the Old West were not a fight over property ownership, but rather over access to natural resources. The farmers and their fences won that one, for the most part.

Those tech battles in the enterprise are fights over access to the "natural" resource of data and to the tools for managing and analyzing it.

In the '90s and most of the following decade, the farmers won again. Data was harvested from corporate systems and piled high in warehouses, with controlled accessed by selected users for milling it into Business Intelligence.

But now in the era of Big Data Analytics, it is not looking so good for the farmers. The public cloud, open source databases, and mobile tablets are all chipping away at the centralized command-and-control infrastructure down by the riverside.  And, new cloud based Big Data analytics solution providers like BigML, Yottamine (my company) and others are putting unprecedented analytical power in the hands of the data ranchers.

A Rainstorm, Not a River
Corporate data is like a river - fed by transaction tributaries and dammed into databases for controlled use in business irrigation.

Big Data is more like a relentless rainstorm - falling heavily from the cloud and flowing freely over and around corporate boundaries, with small amounts channeled into analytics and most draining to the digital deep.

Many large companies are failing to master this new data ecology because they are trying to do Big Data analytics in the same way, with the same tools as they did with BI, and that will never work. There is a lot more data, of course, but it is different data - tweets, posts, pictures, clicks, GPS, etc., not RDBMS records - and different analytics - discovery and prediction, not reporting and evaluation.

Successfully gleaning business value from the Big Data rainstorm requires new tools and maybe new rules.

Embracing Shadows
These days, tech industry content readers frequently see the term "Shadow IT" referring to how business people are using new technologies to process and analyze information without the help of "real IT".  SoMoClo by another, more sinister name.  Traditionalists see it as a threat to corporate security and stability and modernists a boon to cost control and competitiveness.

But, it really doesn't matter which view is right.  Advanced analytics on Big Data takes more computing horsepower than most companies can afford.  Jobs like machine learning from the Twitter Fire Hose will take hundreds or even thousands of processor cores and terabytes of memory (not disk!) to build accurate and timely predictive models.

Most companies will have no choice but to embrace the shadow and use AWS or some other elastic cloud computing service, and new, more scalable software tools to do effective large scale advanced analytics.

Time for New Rules?
Advanced Big Data analytics projects, the ones of a scale that only the cloud can handle, are being held back by reservations over privacy, security and liability that in most cases turn out to be needless concerns.

If the data to be analyzed were actual business records for customers and transactions as it is in the BI world, those concerns would be reasonable.  But more often than not, advanced analytics does not work that way.  Machine learning and other advanced algorithms do not look at business data. They look at statistical information derived from business data, usually in the form of an inscrutable mass of binary truth values that is only actionable to the algorithm.  That is what gets sent to the cloud, not the customer file.

If you want to do advanced cloud-scale Big Data analytics and somebody is telling you it is against the rules, you should look at the rules.  They probably don't even apply to what you are trying to do.

First User Advantage
Advanced Big Data analytics is sufficiently new and difficult that not many companies are doing much of it yet.  But where BI helps you run a tighter ship, Big Data analytics helps you sink your enemy's fleet.

Some day, technologies like high performance statistical machine learning will be ubiquitous and the business winners will be the ones who uses the software best.  But right now, solutions are still scarce and the business winners are ones willing to use the software at all.

More Stories By Tim Negris

Tim Negris is SVP, Marketing & Sales at Yottamine Analytics, a pioneering Big Data machine learning software company. He occasionally authors software industry news analysis and insights on Ulitzer.com, is a 25-year technology industry veteran with expertise in software development, database, networking, social media, cloud computing, mobile apps, analytics, and other enabling technologies.

He is recognized for ability to rapidly translate complex technical information and concepts into compelling, actionable knowledge. He is also widely credited with coining the term and co-developing the concept of the “Thin Client” computing model while working for Larry Ellison in the early days of Oracle.

Tim has also held a variety of executive and consulting roles in a numerous start-ups, and several established companies, including Sybase, Oracle, HP, Dell, and IBM. He is a frequent contributor to a number of publications and sites, focusing on technologies and their applications, and has written a number of advanced software applications for social media, video streaming, and music education.

@MicroservicesExpo Stories
DevOps teams have more on their plate than ever. As infrastructure needs grow, so does the time required to ensure that everything's running smoothly. This makes automation crucial - especially in the server and network monitoring world. Server monitoring tools can save teams time by automating server management and providing real-time performance updates. As budgets reset for the New Year, there is no better time to implement a new server monitoring tool (or re-evaluate your current solution)....
The benefits of automation are well documented; it increases productivity, cuts cost and minimizes errors. It eliminates repetitive manual tasks, freeing us up to be more innovative. By that logic, surely, we should automate everything possible, right? So, is attempting to automate everything a sensible - even feasible - goal? In a word: no. Consider this your short guide as to what to automate and what not to automate.
Cavirin Systems has just announced C2, a SaaS offering designed to bring continuous security assessment and remediation to hybrid environments, containers, and data centers. Cavirin C2 is deployed within Amazon Web Services (AWS) and features a flexible licensing model for easy scalability and clear pay-as-you-go pricing. Although native to AWS, it also supports assessment and remediation of virtual or container instances within Microsoft Azure, Google Cloud Platform (GCP), or on-premise. By dr...
High-velocity engineering teams are applying not only continuous delivery processes, but also lessons in experimentation from established leaders like Amazon, Netflix, and Facebook. These companies have made experimentation a foundation for their release processes, allowing them to try out major feature releases and redesigns within smaller groups before making them broadly available. In his session at 21st Cloud Expo, Brian Lucas, Senior Staff Engineer at Optimizely, discussed how by using ne...
Agile has finally jumped the technology shark, expanding outside the software world. Enterprises are now increasingly adopting Agile practices across their organizations in order to successfully navigate the disruptive waters that threaten to drown them. In our quest for establishing change as a core competency in our organizations, this business-centric notion of Agile is an essential component of Agile Digital Transformation. In the years since the publication of the Agile Manifesto, the conn...
While some developers care passionately about how data centers and clouds are architected, for most, it is only the end result that matters. To the majority of companies, technology exists to solve a business problem, and only delivers value when it is solving that problem. 2017 brings the mainstream adoption of containers for production workloads. In his session at 21st Cloud Expo, Ben McCormack, VP of Operations at Evernote, discussed how data centers of the future will be managed, how the p...
The cloud revolution in enterprises has very clearly crossed the phase of proof-of-concepts into a truly mainstream adoption. One of most popular enterprise-wide initiatives currently going on are “cloud migration” programs of some kind or another. Finding business value for these programs is not hard to fathom – they include hyperelasticity in infrastructure consumption, subscription based models, and agility derived from rapid speed of deployment of applications. These factors will continue to...
While we understand Agile as a means to accelerate innovation, manage uncertainty and cope with ambiguity, many are inclined to think that it conflicts with the objectives of traditional engineering projects, such as building a highway, skyscraper or power plant. These are plan-driven and predictive projects that seek to avoid any uncertainty. This type of thinking, however, is short-sighted. Agile approaches are valuable in controlling uncertainty because they constrain the complexity that ste...
Digital transformation has changed the way users interact with the world, and the traditional healthcare experience no longer meets rising consumer expectations. Enterprise Health Clouds (EHCs) are designed to easily and securely deliver the smart and engaging digital health experience that patients expect today, while ensuring the compliance and data integration that care providers require. Jikku Venkat
identify the sources of event storms and performance anomalies will require automated, real-time root-cause analysis. I think Enterprise Management Associates said it well: “The data and metrics collected at instrumentation points across the application ecosystem are essential to performance monitoring and root cause analysis. However, analytics capable of transforming data and metrics into an application-focused report or dashboards are what separates actual application monitoring from relat...
"This all sounds great. But it's just not realistic." This is what a group of five senior IT executives told me during a workshop I held not long ago. We were working through an exercise on the organizational characteristics necessary to successfully execute a digital transformation, and the group was doing their ‘readout.' The executives loved everything we discussed and agreed that if such an environment existed, it would make transformation much easier. They just didn't believe it was reali...
"Codigm is based on the cloud and we are here to explore marketing opportunities in America. Our mission is to make an ecosystem of the SW environment that anyone can understand, learn, teach, and develop the SW on the cloud," explained Sung Tae Ryu, CEO of Codigm, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
"We're developing a software that is based on the cloud environment and we are providing those services to corporations and the general public," explained Seungmin Kim, CEO/CTO of SM Systems Inc., in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
Many enterprise and government IT organizations are realizing the benefits of cloud computing by extending IT delivery and management processes across private and public cloud services. But they are often challenged with balancing the need for centralized cloud governance without stifling user-driven innovation. This strategy requires an approach that fundamentally reshapes how IT is delivered today, shifting the focus from infrastructure to services aggregation, and mixing and matching the bes...
DevOps promotes continuous improvement through a culture of collaboration. But in real terms, how do you: Integrate activities across diverse teams and services? Make objective decisions with system-wide visibility? Use feedback loops to enable learning and improvement? With technology insights and real-world examples, in his general session at @DevOpsSummit, at 21st Cloud Expo, Andi Mann, Chief Technology Advocate at Splunk, explored how leading organizations use data-driven DevOps to close th...
"CA has been doing a lot of things in the area of DevOps. Now we have a complete set of tool sets in order to enable customers to go all the way from planning to development to testing down to release into the operations," explained Aruna Ravichandran, Vice President of Global Marketing and Strategy at CA Technologies, in this SYS-CON.tv interview at DevOps Summit at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
We just came off of a review of a product that handles both containers and virtual machines in the same interface. Under the covers, implementation of containers defaults to LXC, though recently Docker support was added. When reading online, or searching for information, increasingly we see “Container Management” products listed as competitors to Docker, when in reality things like Rocket, LXC/LXD, and Virtualization are Dockers competitors. After doing some looking around, we have decided tha...
The nature of test environments is inherently temporary—you set up an environment, run through an automated test suite, and then tear down the environment. If you can reduce the cycle time for this process down to hours or minutes, then you may be able to cut your test environment budgets considerably. The impact of cloud adoption on test environments is a valuable advancement in both cost savings and agility. The on-demand model takes advantage of public cloud APIs requiring only payment for t...
"We are an integrator of carrier ethernet and bandwidth to get people to connect to the cloud, to the SaaS providers, and the IaaS providers all on ethernet," explained Paul Mako, CEO & CTO of Massive Networks, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
From our perspective as consumers, perhaps the best thing about digital transformation is how consumerization is making technology so much easier to use. Sure, our television remote controls still have too many buttons, and I have yet to figure out the digital display in my Honda, but all in all, tech is getting easier for everybody. Within companies – even very large ones – the consumerization of technology is gradually taking hold as well. There are now simple mobile apps for a wide range of ...