Monitoring Node.js Applications has special challenges. The dynamic nature of the language provides many “opportunities” for developers to produce memory leaks, and a single function blocking the event queue can have a huge impact on the overall application performance. Parallel execution of jobs is done using multiple worker processes using the “cluster” functionality to take full advantage of multi-core CPUs – but the master and worker processes belong to a single application, which means that they should be monitored together. Let’s have a deep look at the Top Metrics in Node.js Applications to get a better understanding of why they are so important to monitor.
The Future of Data Security By @Kevin_Jackson | @CloudExpo #Cloud
The Dell Fellows program recognizes engineers for their outstanding and sustained technical achievements, engineering contributions and advancement of the industry. They are also seen as top innovators that have distinguished themselves through ingenuity, intellectual curiosity and inventiveness in the delivery of technology solutions. For these reasons and more, I couldn’t pass up the opportunity to speak with Timothy G. Brown, Executive Director Security and Dell Fellow. During our broad-ranging discussion, Tim shared with me his exciting view of security in the not too distant future.
Teradata: Embrace the Power of PaaS By @Kevin_Jackson | @CloudExpo #Cloud
Platform-as-a-Service (PaaS) has always been the unappreciated sibling of the cloud computing service model trio. Existing in the dark shadow of the most widely adopted Software-as-a-Service (SaaS) and foundationally powerful Infrastructure-as-a-Service (IaaS), the third service model is often misunderstood and widely ignored.
PaaS provides a platform allowing customers to develop, run, and manage web applications without the complexity of building and maintaining the infrastructure. Its unique power is associated with developing and deploying applications.
Driving Innovation with Platform as a Service | @CloudExpo #Cloud #DevOps
Application development is changing dramatically. PaaS can help by pulling together cloud infrastructure, containers, microservices, and DevOps so you can deliver apps faster. Download now.
Royal Mail bags couriering SaaS specialist NetDespatch
The UK’s Royal Mail has bought cloud-based parcel management system NetDespatch in a bid to expand its range of services and global reach.
NetDespatch will operate as an independent standalone subsidiary so it can continue to service existing clients and is free to offer services to Royal Mail competitors in future. NetDespatch directors Matthew Robertson and Matthew Clark will remain in charge of operations and all existing client terms and conditions will remain unchanged.
The service provider helps carriers (such as its new parent company Royal Mail) to manage the transport of parcels for 130,000 business customers in 100 countries across the world. The NetDespatch parcel management system is a software as a service (SaaS) cloud system that carriers use to track the movements of parcels. It has grown in popularity as it can make it easier to integrate ecommerce websites, sales order processing and warehouse systems at the point of despatch. It makes it easier for users to print shipping labels, customs documents and manifests and automatically pre-advises their carrier of incoming parcels.
The aim is to make a relatively complex process simple, make logistics more efficient and save money for everyone in the supply chain from retailer to carrier to consumer, said Matthew Robertson, NetDespatch’s Commercial Director. “E-commerce is exploding in the run up to Christmas and we expect to continue to steam ahead in 2016 and beyond,” he said.
NetDespatch’s cloud software has made the integration of the Royal Mail’s systems with its customers’ complex IT estates a lot quicker, according to Nick Landon, Managing Director of Royal Mail Parcels. “This acquisition will support our parcels business with new and innovative software solutions,” he said. The fee for the transaction was not disclosed.
Microsoft acquires Metanautix with Quest for intelligent cloud
Microsoft has bought Californian start up Metanautix for an undisclosed fee in a bid to improve the flow of analytics data as part of its ‘intelligent cloud’ strategy.
The Palo Alto vendor was launched by Theo Vassilakis and Toli Lerios in 2014 with $7 million. The Google and Facebook veterans had impressed venture capitalists with their plans for more penetrative analysis of disparate data. The strategy was to integrate the data supply chains of enterprises by building a data computing engine, Quest, that created scalable SQL access to any data.
Modern corporations aspire to data-driven strategies but have far too much information to deal with, according to Metanautix. With so many sources of data, only a fraction can be analysed, often because too many information silos are impervious to query tools.
Metanautix uses SQL, the most popular query language, to interrogate sources as diverse as data warehouses, open source data base, business systems and in-house/on-premise systems. The upshot is that all data is equally accessible, whether it’s from Salesforce or SQL Server, Teradata or MongoDB.
“As someone who has led complex, large scale data warehousing projects myself, I am excited about building the intelligent cloud and helping to realize the full value of data,” said Joseph Sirosh, corporate VP of Microsoft’s Data Group, announcing the take-over on the company web site.
Metanautix’s technology, which promises to connect to all data regardless of type, size or location, will no longer be available as a branded product or service. Microsoft is to initially integrate it within its SQL Server and Cortana Analytics systems with details of integration with the rest of Microsoft’s service portfolio to be announced in later months, Sirosh said.
The blog posting from Metanautix CEO Theo Vassilakis hinted at further developments. “We look forward to being part of Microsoft’s important efforts with Azure and SQL Server to give enterprise customers a unified view of all of their data across cloud and on-premises systems,” he said.
Monetization of Everything | @ThingsExpo @IanKhanLive #IoT #BigData
“IoT is going to be a huge industry with a lot of value for end users, for industries, for consumers, for manufacturers. How can we use cloud to effectively manage IoT applications,” stated Ian Khan, Innovation & Marketing Manager at Solgeniakhela, in this SYS-CON.tv interview at @ThingsExpo, held November 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA.
The Evolution of IoT with @Luxoft | @ThingsExpo #IoT #M2M #InternetOfThings
“What is the next step in the evolution of IoT systems? The answer is data, information, which is a radical shift from assets, from things to input for decision making,” stated Michael Minkevich, VP of Technology Services at Luxoft, in this SYS-CON.tv interview at @ThingsExpo, held November 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA.
Celebrating You, Our Customers: Thanks for a Great Year!
As 2016 gets closer and closer, we can’t help but look back at the past year and feel a lot of different things: amazement, a sense of accomplishment, and most of all, gratitude. Gratitude towards you, our customers! Without you, none of what we do would be possible, so we wanted to take a moment […]
The post Celebrating You, Our Customers: Thanks for a Great Year! appeared first on Parallels Blog.
A trillion tiny robots in the cloud: The future of AI in an algorithm world
(c)iStock.com/davincidig
It’s not words, but how we say them that speaks volumes. By analysing your tone of voice, a new computer algorithm can predict whether your relationship will last, and it arguably does so more accurately than professional therapists.
Now, I’m not here to advise you on couples therapy. What I’m interested in is how algorithms like this one are going to change the way we live and provide a massive opportunity for the cloud industry.
Data + machine learning algorithm = AI
Popular culture leads us to believe that the future of artificial intelligence (AI) will be a single, magical supercomputer. Think HAL 9000 from 2001: A Space Odyssey, or the ship’s computer on Star Trek. But we’re starting to understand now that we’ve been looking at it all wrong.
x.ai has created an AI-powered personal assistant that schedules meetings for you. In the US alone, there are 87 million knowledge workers who spend up to five hours a week scheduling meetings
The future of AI isn’t about one giant super-intelligence. Instead, it’s about many small, dedicated agents that know you intimately and work on your behalf to improve your everyday life. That could be helping you shop, get to work or, even, find a partner. Each is focused on a discrete task, and each gets better over time and adapts to your needs as they evolve.
This kind of smart software isn’t new. It’s been almost 20 years since chess Grandmaster Garry Kasparov lost to IBM’s Deep Blue in a chess match.
Amazon has had machine learning for many years too. Every time the giant retailer serves up a recommendation, AI made the decision and what would be the best option for you on that particular day.
But if Amazon can have AI working to sell you more things, shouldn’t you have your own AI working to find better deals from other vendors, looking for reliable reviews, or keeping you ahead of the latest trends?
Of course, algorithms are also nothing new, but it’s become vastly easier to write and use them in recent years. The main driver behind this has been cheap and ubiquitous computing, an abundance of data, and a platform that brings these elements together: cloud computing.
For AI to be useful it needs all three: a good algorithm, millions of relevant data points, and computing power to process it quickly so it can drive actions in real-time. Lose any one of these and it’s not nearly as useful in the modern world.
The point is this: your business is going to get disrupted by AI, but not in the way you might have thought. Rather than preparing yourself for one monolithic, all knowing consciousness, it’s going to be a trillion tiny agents all focused on specific tasks, and all of them hungry for data.
Finding the time
One example of this focused approach is x.ai. The New York City startup has created an AI-powered personal assistant that schedules meetings for you. It’s a simple enough to use. You connect your calendar to x.ai and then CC in amy@x.ai whenever a discussion starts about scheduling. Once you copy in Amy, she takes over the thread, finds a mutually agreeable time and place, and sets up the meeting for you. The person at the other end has no idea she’s not a human.
How brilliant is that? In the US alone, there are 87 million knowledge workers who spend nearly five hours per week scheduling meetings. I don’t imagine many of us enjoy the process and would more than happy to delegate to a virtual assistant instead.
It’s not such a simple fix though. The technology that powers the virtual assistant is complex. Amy passes each email through natural language processing and supervised learning engines that understand the context of the information. The data is then enriched and stored in MongoDB where it is combined with other information such as the user’s preferred working hours and their current time zone. Based on these inputs Amy determines the appropriate course of action and crafts a response. There’s no app to install. Amy exists only in the cloud.
This is only one example of how algorithms are changing our lives. Cities are starting to automatically adjust traffic flow based on weather, construction, congestion, events, and other real-time factors. Ads you land on while browsing your favourite sites run an algorithm over your data and match it with their calculated preferences about you to serve up something that is highly relevant.
Many of the most popular cloud and data technologies are already responding to this trend. Apache Spark is full of machine learning libraries that come built into the framework. Google released TensorFlow as an open source project, which makes the machine learning technology behind Google Translate and many other products, freely available to anyone.
With these tools easily accessible by developers, it’s easy to see how many different tasks could be quickly re-imagined as algorithms that delivered as convenient services. In fact Peter Sondergaard at Gartner is predicting a whole new Algorithm Economy.
Things you can’t algorithm
Cloud computing solved the two biggest hurdles for AI: abundant, low cost computing and a way to leverage massive volumes of data. However, a number of challenges remain. Chief among those challenges is the one affecting the whole industry: skills.
Cloud computing solved the two biggest hurdles for AI – abundant, low cost computing, and a way to leverage massive volumes of data – however a number of challenges remain
While open source libraries make it easy to get started, for genuinely powerful AI you need actual data scientists. People with strong programming backgrounds, a deep understanding of mathematics and statistics, as well as business domain knowledge. Needless to say, those people are rare.
The other challenges will mainly be around the data. Most modern data is inherently unstructured – it’s geographic data, sensor data, and social data. If your stack is built on decades-old relational technology you are going to struggle to feed modern algorithms running in the cloud.
Despite the challenges, the main lesson is this: small, focused, cloud-based algorithms are going to be the AI that changes our lives over the next decade. It’s better to solve one problem really well, than it is to solve 100 problems poorly. Today’s markets reward companies that maintain their focus.
To take advantage of these trillion robots in the cloud, you’re going to need a thoroughly modern infrastructure.