As the Big Data marketplace moves closer to a point of mass-maturity, business leaders have begun to take new approaches to implementation and utilization. Advanced analytics solutions have made their way into a range of industries and regions, and companies that successfully align these investments with core goals and requirements will enjoy more progressive improvements to operational sustainability, intelligence and general performance. However, there is some housekeeping that must be addressed as organizations embark on Big Data and analytics initiatives. Data preparation, information governance and security are three fundamental elements of effective analytics strategies, yet, ironically, each has been largely ignored by many organizations in the rush to realize the promise of Big Data.
We in IT live in a complex world. While complexity is rapidly increasing, we are working hard on simplifying it. The two things occurring at the same time certainly do not make for a smooth or even linear transition, but at a minimum we are keeping complexity from overwhelming the number of man-hours available to manage the overall IT world. Our simplification efforts are across the board, which is an amazing phenomenon that I will happily lay the credit for on the DevOps world. While DevOps continues to mean different things to different people, that is the very reason we are seeing improvement in most areas of dev and operations both. And all of these efforts, while providing other benefits, generally work to make someone’s life easier by easing the burden of IT. Whether it be self-service to assist users, automated provisioning to help operations, or CI/CD to help developers, life gets easier and deployment times get faster through these efforts.
Pokemon Go has stormed onto the scene this past week and the buzz is everywhere about it! However, being so successful in such a brief period of time can have a downside. Reports of players being unable to access services started appearing in relatively short order. Amazon's CTO jumped into the fray offering their cloud services to help scale NiantecLab's (creators of Pokemon Go) server-side infrastructure. While the Pokemon Go app has been positively rated by the majority of players, there are clearly a large number of players who thought differently, as indicated in the Google play App ratings.
The big move is Amazon’s beating Exxon Mobile (used to be number 1 for many years) to the fourth spot. The switch came after Amazon posted its fifth straight quarter of profits last week as the oil giant’s profits tumbled 59 percent during the same rough period. If Exxon continues its drop, then Facebook will beat it in days.
DNS is a core component of the Internet. This rather complex system exists to perform the basic function of translating website Domain names (ex: Catchpoint.com) into an IP address. It acts as the liaison between the language that users can understand, and the address of the data center or destination in which the site is being hosted. DNS is what shapes the modern Internet. It enables users of all skill sets and technical knowledge levels to use this incredibly advanced resource on a daily basis. Businesses that exist entirely on the Internet like online banking and ecommerce companies have not only emerged into the market, but have steadily gained a global presence.
The Citrix Technology Professionals Program, or CTP, gives participants a larger say in essential strategy initiatives such as enabling mobile work styles. As an example, we will explore how the Citrix Technology Professionals Program, or CTPs as they are referred to, gives participants a larger say in essential strategy initiatives such as enabling mobile work styles.
During the last two years, Microsoft has upped the ante on Machine Learning and Analytics. From hiring top notch data scientists to acquiring niche startups, Redmond has made the all the right moves to transform Azure into one of the best analytics platforms. These investments have started to pay off for the company. It has been successful in articulating and demonstrating the value of data-driven insights to governments, medical institutions, and public sector organizations.
Infrastructure complexity is not unique to enterprise datacenters. Just because a business or organization is small does not mean it is exempt from the feature needs of big enterprise datacenters. Small and mid-size organizations require fault tolerance, high availability, mobility, and flexibility as much as anyone. Unfortunately, the complexity of traditional datacenter and virtualization architecture hit the SMB the hardest.
Kubernetes, Docker and containers are changing the world, and how companies are deploying their software and running their infrastructure. With the shift in how applications are built and deployed, new challenges must be solved. In his session at @DevOpsSummit at19th Cloud Expo, Sebastian Scheele, co-founder of Loodse, will discuss the implications of containerized applications/infrastructures and their impact on the enterprise. In a real world example based on Kubernetes, he will show how to migrate an existing application to Docker and Kubernetes, and what the benefits are.
Let's just nip the conflation of these terms in the bud, shall we?
"MIcro" is big these days. Both microservices and microsegmentation are having and will continue to have an impact on data center architecture, but not necessarily for the same reasons. There's a growing trend in which folks - particularly those with a network background - conflate the two and use them to mean the same thing.
They are not.
One is about the application. The other, the network. There is a relationship, but it's a voluntary one. They are two very different things and we need to straighten out the misconceptions that are rapidly becoming common.
Microservices are the resulting set of services (mini applications, if you will) that arise from the process of decomposing an application into smaller pieces. If you take a monolithic application and segment it into many pieces, you end up with microservices. It is an application architecture; an approach to designing applications.
This architectural approach has a significant impact on the network architecture, as it forces broader distribution of application-affine services like load balancing, caching and acceleration to be located closer to the individual service. Microservices as an approach is a forcing factor in the bifurcation of the network as it separates application-affine services from corporate-affine services.
Microservice architectures are beneficial in that they are highly efficient; it separates functional or object domains and thus lends itself well to a more targeted and efficient scalability model. It is particularly useful when designing APIs, as in addition to the scalability benefits it also localizes capabilities and enables isolated upgrades and new features without necessarily disrupting other services (and the teams developing other services). This lends itself well to agile methodologies while enabling a greater focus on API development as it relates to other services as well as the applications that will use the service.
Microsegmentation is about the network; to be precise, at the moment it's about the security functions in the network and where they reside. It's a network architecture that, like microservices, breaks up a monolithic approach to something (in this case security) and distributes it into multiple services. You could say that microsegmentation is micro-security-services, in that it decomposes a security policy into multiple, focused security policies and distributes them in an resource-affine manner. That is, security policies peculiar to an application are physically located closer to that application, rather than at the edge of the network as part of a grandiose, corporate policy.
This approach, while initially focusing on security, can be applied to other services as well. As noted above, a result of a microservice approach to applications the network naturally bifurcates and application-affine services (like security) move closer to the application. Which is kind of what microsegmentation is all about; smaller, distributed "segments" of security (and other application-affine services like load balancing and caching) logically deployed close to the application.
Thus, if there is any relationship between the two approaches, it is that microservices tend to create an environment in which microsegmentation occurs.
There are other reasons for microsegmentation, including the reality that the scale required at the edge to support every application-specific service is simply pushing IT to the edge of its wits (pun only somewhat intended). The other driving factor (or maybe it's a benefit?) is that of service isolation, which provides for fewer disruptions in the event of changes occurring in a single service. For example, a change to the core firewall is considered potentially highly disruptive because if it goes wrong, every thing breaks. Changing the firewall rules on a localized, isolated service responsible for serving two or three applications, has a much lower rate of disruption should something go wrong.
This is highly desirable in a complex environment in which stability is as important as agility.
In a nutshell, microservices are to applications what microsegmentation is to network services. Both are about decomposing a monolithic architecture into its core components and distributing them topologically in a way that enables more scalable, secure and isolated domains of control.
The thing to remember is that just because dev has decided to leverage microservices does not in turn mean that the network somehow magically becomes microsegmented or that if microsegmentation is used to optimize the network service architecture that suddenly apps become microservices. Microsegmentation can be used to logically isolate monolithic applications as easily as it can microservices.
Either approach can be used independently of one another, although best practices in networking seem to indicate that if dev decides to go with microservices, microsegmentation is not going to be far behind. But the use of microsegmentation in the network does not mean dev is going to go all in with microservices.
Identity is in everything and customers are looking to their providers to ensure the security of their identities, transactions and data. With the increased reliance on cloud-based services, service providers must build security and trust into their offerings, adding value to customers and improving the user experience. Making identity, security and privacy easy for customers provides a unique advantage over the competition.
Generally speaking, there are two kinds of companies in the world: data rich and data poor. The richest of the data rich are easy to name: Google, Facebook, Amazon, Apple. But you don’t need to be at the top of this list to use data to create value. You need to have the tools in place to turn information (data) into action. That’s what the data rich do that the data poor and the data middle class do not.
Sharding has become a popular means of achieving scalability in application architectures in which read/write data separation is not only possible, but desirable to achieve new heights of concurrency. The premise is that by splitting up read and write duties, it is possible to get better overall performance at the cost of a slight delay in consistency. That is, it takes a bit of time to replicate changes initiated by a "write" to the read-only master database. It's eventually consistent, and it's generally considered an acceptable trade off when searching for higher and higher scalability.
Right off the bat, Newman advises that we should "think of microservices as a specific approach for SOA in the same way that XP or Scrum are specific approaches for Agile Software development". These analogies are very interesting because my expectation was that microservices is a pattern. So I might infer that microservices is a set of process techniques as opposed to an architectural approach. Yet in the book, Newman clearly includes some elements of concept model and architecture as well as process and organization.
Competitors can't compete, and leaders can't lead if they don't know the rules of the game. Understanding how points are scored, and what is required to win is key to any competition. In the age of digital transformation there are key rules to learn: Data is the modern commercial battlefield Information dominance is the strategic goal
With over 720 million Internet users and 40–50% CAGR, the Chinese Cloud Computing market has been booming. When talking about cloud computing, what are the Chinese users of cloud thinking about? What is the most powerful force that can push them to make the buying decision? How to tap into them? In his session at 18th Cloud Expo, Yu Hao, CEO and co-founder of SpeedyCloud, answered these questions and discussed the results of SpeedyCloud’s survey.
With 2016 crossing the half way point, let’s take a look at some technology trends thus far. Breaches: Well, many databases are half empty due to the continued rash of intrusions while the crooks are half full with our personal information. According to the Identity Theft Resource Center (ITRC), there have been 522 breaches thus far in 2016 exposing almost 13,000,000 records. Many are health care providers as our medical information is becoming the gold mine of stolen info. Not really surprising since the health care wearable market is set to explode in the coming years. Many of those wearables will be transmitting our health data back to providers. There were also a bunch of very recognizable names getting blasted in the media: IRS, Snapchat, Wendy’s and LinkedIn. And the best advice we got? Don’t use the same password across multiple sites. Updating passwords is a huge trend in 2016.
This digest provides an overview of good resources that are well worth reading. We’ll be updating this page as new content becomes available, so I suggest you bookmark it. Also, expect more digests to come on different topics that make all of our IT-hearts go boom!
Actian Corporation has announced the latest version of the Actian Vector in Hadoop (VectorH) database, generally available at the end of July. VectorH is based on the same query engine that powers Actian Vector, which recently doubled the TPC-H benchmark record for non-clustered systems at the 3000GB scale factor (see tpc.org/3323). The ability to easily ingest information from different data sources and rapidly develop queries to make better business decisions is becoming increasingly important, particularly for those companies looking to respond to changes real-time or explore machine-learning. When paired with Actian VectorH, the industry’s fastest Enterprise SQL database that sits natively in Hadoop, Spark users have a new powerful way to help derive true business value from their data.
On Dice.com, the number of job postings asking for skill in Amazon Web Services increased 76 percent between June 2015 and June 2016. Salesforce.com saw its own skill mentions increase 37 percent, while DevOps and Cloud rose 35 percent and 28 percent, respectively. Even as they expand their presence in the cloud, companies are also looking for tech professionals who can manage projects, crunch data, and figure out how to make systems run more autonomously. Mentions of ‘data science’ as a skill rose 13 percent, followed by automation with 11 percent and program management with 10 percent.
Having automated workflows sounds like a sexy thing. And it is. It feels pretty awesome to be able to initiate a process with a button and forget about it while the work gets done. But that doesn’t mean you have to automate every process in your office. I mean, I think people can take care of replacing the copy paper on their own, right? Right? Come on, people. Because automation shouldn’t be blatantly applied to every corner of your business, it’s good to start with a very basic question before you begin. Why? There are a lot of ways to improve your business – why are you looking to automation? What do you hope to get out of it? Here are some good and bad reasons companies automate their processes.
ReadyTalk has expanded the capabilities of the FoxDen collaboration platform announced late last year to include FoxDen Connect, an in-room video collaboration experience that launches with a single touch. With FoxDen Connect, users can now not only engage in HD video conferencing between iOS and Android mobile devices or Chrome browsers, but also set up in-person meeting rooms for video interactions. A host’s mobile device automatically recognizes the presence of a meeting room via beacon technology and wirelessly initiates an onscreen FoxDen meeting, making FoxDen Connect the first solution to use beacons to start video collaboration.
In his session at Cloud Expo, Robert Cohen, an economist and senior fellow at the Economic Strategy Institute, will provide economic scenarios that describe how the rapid adoption of software-defined everything including cloud services, SDDC and open networking will change GDP, industry growth, productivity and jobs. This session will also include a drill down for several industries such as finance, social media, cloud service providers and pharmaceuticals.
Node.js and io.js are increasingly being used to run JavaScript on the server side for many types of applications, such as websites, real-time messaging and controllers for small devices with limited resources. For DevOps it is crucial to monitor the whole application stack and Node.js is rapidly becoming an important part of the stack in many organizations. Sematext has historically had a strong support for monitoring big data applications such as Elastic (aka Elasticsearch), Cassandra, Solr, Spark, Hadoop, and HBase, as well as more traditional databases, web servers like Nginx, Nginx Plus and Apache, Java applications, cache servers like Redis and Memcached, messaging middleware like everyone's darling Kafka, etc. With such rapid adoption of Node.js and now io.js, we'd be remiss not to add performance monitoring, alerting, and anomaly detection for them in SPM!
I wanted to gather all of my Internet of Things (IOT) blogs into a single blog (that I could later use with my University of San Francisco (USF) Big Data “MBA” course). However as I started to pull these blogs together, I realized that my IOT discussion lacked a vision; it lacked an end point towards which an organization could drive their IOT envisioning, proof of value, app dev, data engineering and data science efforts. And I think that the IOT end point is really quite simple…