In their session at DevOps Summit, Asaf Yigal, co-founder and the VP of Product at Logz.io, and Tomer Levy, co-founder and CEO of Logz.io, will explore the entire process that they have undergone – through research, benchmarking, implementation, optimization, and customer success – in developing a processing engine that can handle petabytes of data. They will also discuss the requirements of such an engine in terms of scalability, resilience, security, and availability along with how the architecture accomplishes these requirements. Lastly, they will review the gory details of the technologies they have chosen, which are based mostly on open source platforms including Kafka, Elasticsearch, and Docker as well as other proprietary technologies.
SYS-CON Events announced today that Vitria will exhibit, conduct a demo theater presentation, and CTO Dale Skeen will deliver a technical session at @ThingsExpo, which will take place on November 3–5, 2015, at the Santa Clara Convention Center in Santa Clara, CA. Vitria is a leading provider of advanced analytics platforms that enable businesses to transform their operations and boost revenue growth through Faster Analytics, Smarter Actions, and Better Outcomes Faster.
Test-Driven Development is a great tool for functional testing, but can you apply the same technique to performance testing? Why not? The purpose of TDD is to build out small unit tests, or scenarios, under which you control your initial coding. Your tests will fail the first time you run them because you haven’t actually developed any code. But once you do start coding, you’ll end up with just enough code to pass the test. There’s no reason the same philosophy can’t be applied to performance testing.
Electric power utilities face relentless pressure on their financial performance, and reducing distribution grid losses is one of the last untapped opportunities to meet their business goals. Combining IoT-enabled sensors and cloud-based data analytics, utilities now are able to find, quantify and reduce losses faster – and with a smaller IT footprint. Solutions exist using Internet-enabled sensors deployed temporarily at strategic locations within the distribution grid to measure actual line loads.
The Internet of Things is clearly many things: data collection and analytics, wearables, Smart Grids and Smart Cities, the Industrial Internet, and more. Cool platforms like Arduino, Raspberry Pi, Intel's Galileo and Edison, and a diverse world of sensors are making the IoT a great toy box for developers in all these areas. In this Power Panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, panelists will discuss what things are the most important, which will have the most profound effect on the world, and what should we expect to see over the next couple of years.
Still here? Okay then, let me explain further. This whole thing started because I was reading the Internet the other day and happened upon a claim that stated: “the attack surface for cloud applications is dramatically different than for highly controlled data centers”. And that made me frustrated because it isn’t true at all.
What it means to build quality software has taken a beating over the years. We’re no longer content to strive for defect-free code. We must also make sure the software meets both its functional and nonfunctional requirements. Only now with the rise of more Agile ways of thinking, we’ve placed the notion of a software requirement under the microscope, as building flexible, resilient software often trumps checking items off our requirements list.
Riak TS is focused to handle time series application needs. It can do fast read/write to IOT devices, there in is its strength. It also targets financial and economics data as well as in scientific research applications. This is straight from Riak TS: "Riak TS automatically co-locates, replicates, and distributes data across the cluster to achieve fast performance and high availability. The unique master-less architecture enables near-linear scale using commodity hardware so you can easily add capacity as your time series data grows."
As with most modernized economies, the United States economy utilizes capitalist principles. It is only fitting that we invented a technological solution that will help companies engage in c-api-talism using APIs in a more efficient manner. If we look back into the history of mankind, we progressed towards more civilized, mature, monetary-based economies on a steady basis. The progression from the Stone Age, to the Bronze Age, to the Iron Age, to the Industrial Age and to the current Digital Age all made strides towards the current digital economy.
Microservices architecture compartmentalize the application by function allowing for greater application flexibility, portability and an increase in update/changes. This architecture introduces new layer of monitoring challenges to an already complex application environment. The strides and enhancements we made in CA APM 10 with APM Team Center Perspectives, Timeline views and Differential Analysis has helped us to make strides in solving these challenges of monitoring microservices that include:
No one should need to be convinced the value of good data. It gives you the confidence to make decisions quickly and with less risk, it allows you to measure your success, and it lets you know when you need to adjust your course. But there’s a difference between knowing the value of data, and creating a culture around it. A data-driven culture is a culture where everyone quantifies their actions as much as possible, and asks themselves how their teams are having a tangible impact on the business. It turns your entire organization into a squad of analysts. But creating a data-driven culture isn’t always easy. Here are five steps that will help you get there.
The move to DevOps also introduces additional constraints to our burgeoning Iron Polygon, as individual projects become less distinct. In an environment focused on continuous automated testing as well as continuous integration and deployment, individual iterations become the project unit as organizations establish regular cadences of repeated iterations (link is external) instead of the discrete, monolithic project releases that characterize traditional waterfall-oriented development.
Contextual Analytics of various threat data provides a deeper understanding of a given threat and enables identification of unknown threat vectors. In his session at @ThingsExpo, David Dufour, Head of Security Architecture, IoT, Webroot, Inc., will discuss how through the use of Big Data analytics and deep data correlation across different threat types, it is possible to gain a better understanding of where, how and to what level of danger a malicious actor poses to an organization, and to determine the measures to implement to prevent future occurrences.
JavaScript is the language of the Web. There is no other language that can run literally on any old or new device connected to the Internet. On the other hand, there are dozens of languages that compile (a.k.a. transpile) to JavaScript. Why not just writing JavaScript applications in JavaScript? Let me start with analogy with Assembly. Programs written in the a particular flavor of Assembly language run on any device that have a CPU that understand it. See the shortcoming comparing to JavaScript? An Assembly program can’t run on any device, but on any device with a specific CPU architecture. Still, why not writing all the code for a specific platform in Assembly? Why use Java, C#, Python or C++?
Too often with compelling new technologies market participants become overly enamored with that attractiveness of the technology and neglect underlying business drivers. This tendency, what some call the “newest shiny object syndrome” is understandable given that virtually all of us are heavily engaged in technology. But it is also mistaken. Without concrete business cases driving its deployment, IoT, like many other technologies before it, will fade into obscurity.
The United States spends around 17-18% of its GDP on healthcare every year. Translated into dollars, it is a mind-boggling $2.9 trillion. Unfortunately, that spending will grow at a faster rate now due to baby boomers becoming an aging population, and they are the largest demographic in the U.S. Unless the U.S. gets this spiraling healthcare spending under control, in a few short years we will be spending almost 25% of our entire GDP in healthcare trying to fix people’s failing health, instead of spending it somewhere else where it is desperately needed. Obviously, we can’t stop the aging population, but we can make the healthcare system more efficient. In the past, digitization of patient records was just for record keeping purposes. But with the advances in IoT, predictive analytics, cognitive computing and the mighty APIs to connect them all together, things have changed dramatically.
We all know that data growth is exploding and storage budgets are shrinking. Instead of showing you charts on about how much data there is, in her session at 17th Cloud Expo, Barbara Murphy, Vice President of Marketing at HGST, will show you how to capture all of your data in one place. After you have your data under control, you can then analyze it in one place, saving time and resources. See how HGST has used these solutions to gain more value out of the information we have – and capitalize on that value by delivering better products.
Developing software for the Internet of Things (IoT) comes with its own set of challenges. Security, privacy, and unified standards are a few key issues. In addition, each IoT product is comprised of (at least) three separate application components: the software embedded in the device, the back-end service, and the mobile application for the end user’s controls. Each component is developed by a different team, using different technologies and practices, and deployed to a different stack/target – this makes the integration of these separate pipelines and the coordination of software updates for IoT more problematic.
The potential of big data is only limited by the creative thinking of your business stakeholders, and that may be the most important concept in the “thinking like a data scientist” process. The “thinking like a data scientist” process guides the business stakeholders into envisioning how big data can optimize their key business processes, create a more compelling customer engagement and uncover new monetization opportunities. But neither the business stakeholders, nor the data scientists, can likely do that envisioning entirely by themselves.
Financial institutions, like other critical service industries such as health care and air travel, have the unique challenge of no room for failure. It's a bad day if your ATM card doesn't work. It's a really bad day if you do a bunch of online trading based on incorrect information. Fintech startups, beware. Money, as it turns out, is kind of a big deal to a lot of people.
With the exponential growth of network traffic slowing down data transmission, companies are looking for solutions. Recently, a solution has emerged that can help improve your data speed with data centers on the edge. These micro data center solutions can simplify the lives of many data center owners and operators because they are self-contained, secure computing environments, assembled in a factory and shipped in one enclosure which includes all the necessary power, cooling, security, and management tools. Their flexibility opens up a wave of new applications, made possible through reduced latency, increased security and cost efficiency.
In his session at @ThingsExpo, Ben Bromhead, CTO of Instaclustr, will walk you through the basics of building an IoT-based platform leveraging Cassandra, Spark and Kafka. This session is aimed at developers, admins and DevOps engineers who have to build, run and maintain high performance IoT platforms as well as data scientists/engineers who are sick of ETL and want to work with the most up to date information.
The revocation of Safe Harbor has radically affected data sovereignty strategy in the cloud. In his session at 17th Cloud Expo, Jeff Miller, Product Management at Cavirin Systems, will discuss how to assess these changes across your own cloud strategy, and how you can mitigate risks previously covered under the agreement.
The Internet of Things is in the early stages of mainstream deployment but it promises to unlock value and rapidly transform how organizations manage, operationalize, and monetize their assets. IoT is a complex structure of hardware, sensors, applications, analytics and devices that need to be able to communicate geographically and across all functions. Once the data is collected from numerous endpoints, the challenge then becomes converting it into actionable insight.
Yesterday, Dell announced the largest technology M&A in history with a proposed$67B buyout of EMC and VMware (via EMC’s 80% ownership of VMW). The combined company will have over $80B in revenue, employ tens of thousands of people around the world and sell everything from PCs, servers & storage to security software and virtualization software. Not to be overlooked is the fact that Dell and EMC will be private companies and free from the scrutiny of activist investors.