Thu, 9 March 2017
We discuss containerization orchestration technologies like Kubernetes, and the healthcare industry’s complex relationship with cloud computing.
We look at the reasons to use Kubernetes as a containerization orchestration tool. Kubernetes represents about 80% of the orchestration tools that exist. The market position is known, skill-sets exist for it, it can scale, and it can provide a one-stop shop to do something effective with containers. In all, companies are not doing a lot of containers today, it is still a small part of the cloud, but it will continue to expand rapidly.
We also look at the healthcare industry’s complex relationship with the cloud. Healthcare is behind the curve in terms of how they’re leveraging technology. The amount of automation that can occur and good that can be done with technology in the healthcare industry is immense. They can move a lot faster with moves to the cloud.
Fri, 3 March 2017
We discuss RightScale’s State of the Cloud report analyzing trends in the cloud. RightScale helps customers adopt cloud by helping them with a cloud management and optimization.
This is the sixth year of the report so we can start to see trends over time now and there were a few interesting takeaways this year. In the report, RightScale asks two big questions for enterprises. First is about cloud strategy and what their intention is on cloud – to use private, public, or combinations of those. Second, they are asked about what they use today for private or public clouds. From a strategy point of view, people are still focused on multi-cloud with a special focus on hybrid cloud. In strategy, there was a shift away from private-only strategies. Fewer people were saying they plan to use only private cloud or multiple private clouds as their strategies. On adoption, there was a slight drop in people who are already using private cloud from 77% last year down to 72% this year. This may indicate companies who had tried to build their own private cloud with Openstack, and are now backing off from that strategy.
The survey found that the average company leverages about four different cloud vendors. This a result of a combination of acquisitions of companies that use different cloud providers and a strategy to leverage different cloud providers. Rightscale asked people for a list of public and private clouds they are running applications on (focused on IaaS and PaaS, not SaaS) and whether they’re experimenting with particular public and private clouds. They found that among people that are using at least one public cloud, they’re running applications in 1.8 public clouds. They are typically experimenting with another 1.8 clouds. Even if they are not using one of the big cloud vendors, they are often at least experimenting with it. The ones that are adopting private cloud are reporting about 2.3 different private clouds.
For top challenges this year there was a three-way tie between security, spend and skills (access to skilled resources). Last year skills was highest and it has dropped a bit this year. The people in IT who are concerned about security has been declining each year. Among enterprises, in 2017 over 35% rated cloud security as a significant challenge, and six years ago that number was about 10% higher. We have now reached a tipping point where people realize that when done right, cloud can be as secure if not more secure than a traditional data center.
As people adopt public cloud, the cost has been increasing and companies are starting to realize they are inefficient with their spend. On average companies believe they are wasting 30% of their cloud spend. RightScale has found that 30-45% or more is typically what companies are wasting on their cloud spend. The survey found that the more mature a cloud instance is, the more important spend becomes.
This year Docker has moved into first place in the list of tools RightScale researches, and while all tools had an increase in usage, Chef and Puppet had a decrease in usage. The survey specifically focuses on configuration management tools and container tools. Docker usage moved from 13% in 2015 to 27% in 2016 to 35% this year, while Chef and Puppet each dropped about 4% this year. The other big increase seen this year was in Kubernetes, which doubled from 7% last year to 14% this year and seems to be in the lead for scheduling and orchestration tools.
There is an early trend RightScale noticed that people are starting to use Docker to take advantage of the temporary instances from the cloud providers such as AWS Spot or Google Preemptibles. For people looking to use those, which can mean 70-90% savings on demand, they need the ability to be very portable when they lose their temporary instances, so using Docker along with a container as a service can be helpful in saving those costs.
Thu, 16 February 2017
We discuss serverless computing and what it means for enterprises. We compare different serverless computing platforms, look at pricing models, and discuss how to determine which applications are right for serverless computing.
Serverless computing is a manifestation of the idea that we should focus on our applications and focus on the code that makes them efficient in providing high-levels of functionality. It brings enterprises away from care, feeding, and operations that are low-calorie work. At this point all major cloud vendors have announced some implementation of serverless computing that allows developers to take a section of code, deploy it on their platform, and have it execute for either very short periods of time, or very long periods of time. In a way, developers do not need to worry about operating systems, connectivity, installing patches, upgrades, or any of the operational pieces that go with having a virtual machine or a physical server.
Serverless computing seems like something that should have been built into cloud computing from the beginning. With IaaS, we try to mimic what’s in the data center. They require most of the same upkeep as a regular server. Serverless computing abstracts us from having to deal with these details and allows us to deal with the brilliance of development instead.
It’s likely that if serverless computing had emerged in 2006, it would not have had an easy time being adopted. In early days of cloud computing, enterprises were trying to break data center habits they had built such as change management, control, and approvals that took a long time. Early services on cloud were focused on getting out of the data center, and allowed people to start the transition. If we had serverless technology at that time, it’s possible people would not have used it because it was too big of a leap to transition their enterprises and skill sets all at once. We’re at the time now where people have adopted the cloud and are comfortable with the concept of not having physical access to the applications and systems. We are now okay with not being able to touch the servers. Now people are realizing the amount of time that goes into patching systems and monitoring systems and that it is low-calorie work and doesn’t contribute to being able to build features and capabilities. We’re at that point in the adoption curve where people have adopted the cloud and shed those data center habits, and now they want to shed those operating system and server habits to make their operations more efficient. This is where serverless computing comes in.
To be clear, the servers are still there, we are just abstracting ourselves from having to deal with the operations and details. This allows us to focus on building the best apps we can without needing to worry about underlying infrastructures.
Security in serverless computing takes a certain level of engagement with the platform vendor to fully understand. Older models of building in network layers of protection don’t apply as much in the serverless world. With serverless computing, we look at the cloud platform (Azure, Google, AWS) to provide network-level security. It shifts the responsibility to enterprises to focus deeper on application-level security (log monitoring, databases, data stores) and to look for anomalous behaviors at the application-level.
In terms of what applications are best for serverless computing, consider it for net new based systems. Traditional lift-and-shift environments would typically go to virtual CPUs and storage devices that sit on the cloud. There are a variety of interesting startups looking at ways to automatically refactor code to move it from an OS-based environment to a serverless-based environment. As applications and companies continue to migrate to the cloud at an accelerated rate, we will see more of these migration tools to make the transition more simple and enable adopters of the cloud to use more innovative services rather than moving applications as they are.
We look at the tradeoffs between Amazon Lambda and Azure Functions.
In addition, Google is just beginning to play in the serverless computing space with its alpha version of Google Cloud Functions.
Thu, 9 February 2017
We discuss Juniper Networks and their acquisition of Contrail, Oracle’s vendor lock-in and new pricing changes, and why Kubernetes has pulled ahead of Docker in the container world. Juniper Networks is expanding their cloud capability with two recent acquisitions: Contrail and AppFormix, a software-defined networking play and a cloud monitoring software.
For Randy, containers are the most exciting part of cloud computing right now. The Openstack movement is slowing down and there is fatigue because people are starting to realize that public cloud is going to win. Private infrastructures that still do exist (such as VMWare) come with too much complexity from set-up to organizing and managing. People are deciding to leapfrog past those now and going straight to containers. They want to pair containers with things like Contrail because the combination allows for a robust infrastructure that can run on both private and public cloud. An example of this is a recent Riot Games blogpost about streaming video games and shows how they combine container orchestration systems with Contrail to give themselves a true hybrid cloud solution at the container level. This means developers do not need to worry about what infrastructure they are running on.
In the news recently, Oracle doubled their license fees to run in AWS. As enterprises are migrating to the cloud, they have a lot of Oracle software currently running in their enterprise and are looking to bring licenses and run Oracle in the cloud, so now they’re being asked to pay more for that migration. This could lead to acceleration of companies leaving Oracle because of the higher prices. People are fed up with proprietary software, licensing, and vendor lock-in. Ten years ago, there weren’t a lot of alternatives to Oracle for relational databases. Now there are lots from Aurora, and RDS and Redshift on Amazon. The problem is, a typical enterprise built a lot of procedures and triggers in the applications that run in the Oracle database ten years ago. Now they either have to ditch all of that or rewrite it, which can be risky, or they can pay more for Oracle licenses and stay with it.
Oracle does not have a good cloud play yet, though they are trying. They do still need to upkeep the legacy databases for enterprises now as the transition occurs. Perhaps if oracle could remain reasonable on pricing, they would have more of a chance of surviving, but it seems the recent news makes it easier for customers to decide to leave. They may need to start skating further ahead of the puck soon if they want to weather the cloud disruption.
We also discuss Docker and why Kubernetes has taken the lead for container software in recent years. Docker took off because it was the “Easy Button” for application developers who did not want to learn Chef or Puppet. Then they tried to each Infrastructure teams who did not know what containers were, so they tried to add complexity to make containers look like nextgen virtual machines. That is not how app developers viewed them, and now Kubernetes has become more preferred. Most Openstack startups have bet on Kubernetes. Docker has built a big platform that has not found its killer app yet and are no longer able to take advantage over the dominance they once had. Docker acquired a lot of companies and linked together a lot of different technologies along the way, always adding layers of complexity. Kubernetes is in front because it has simplified everything and that is what developers want right now.
For an enterprise who is moving to Docker, Randy provides tips and warnings.
It won’t be easy, and be wary of anyone who tells you all the problems with containers are completely solved. Use DevOps and have an application-centric model, so you can focus on velocity.
Thu, 2 February 2017
We discuss how Fast Forward Labs applies the machine learning algorithms of academia to the business world to impact industries. They help clients leverage what they’ve uncovered in their research to build prototypes and demonstrate the potential of new algorithms. Companies are starting to use neural networks for things like image classification, natural language, text summarization, and more. Though it is not a new technique, it is new to the business world and is now available for companies to use. One example of a neural network use-case is to take a long article and automatically cut it down to the five sentences that capture the article’s essence. Artificial Intelligence has been around for more than 30 years, but what has changed now is that it is much easier to store data, and deep learning requires a large amount of data to be effective. Also, the cloud infrastructure makes machine learning possible today because it opens up the use of these algorithms to large companies and even small startups. AI used to require more horsepower, physical space, and cost more to implement than it does today.
Fast Forward Lab clients often struggle to identify the right problem for machine learning. For example, there is a lot of hype around conversational agents, or chat bots, today to replace human agents with AI. But early entrants have found that these bots do not get used frequently by customers, which turns out ot be a user experience problem and not something that can be fixed with machine learning.
The real promise of machine learning is that it can help us with repetitive tasks inside the company. Look in your organization for places where a similar decision or action needs to be made over and over and that is where machine learning could be used.
We discuss the different options for tapping into machine learning algorithms from AWS to Google TensorFlow and open-source tools to combine with them. The most important thing to get started is to determine what clients want to start with defining what the business is trying to achieve. Next it’s important to determine exactly what data is available, and only then to develop a machine learning strategy. Starting simple is important, and only adding complexity when it is necessary.
Thu, 26 January 2017
We discuss how data streams, or real-time stream processing, is the next fundamental architectural and organizational revolution in IT. Today, the capability is available though it is complex to implement at scale. James predicts that within ten years using data streams will become mainstream. Traditionally, an application owns a set of data which comes to rest and is taken somewhere else to correlate with another data set through batch processing. This is an expensive process and can miss key events, which is crucial in industries like banking or finance. What’s available now for data streams takes directly from open-source software and becomes a utility.
How data streams work is that you can start with one stream and decide which functions you want to use on it. You can pass one stream through a different stream, then combine and recombine new streams in a relatively agile way so you can evolve them as you go. You could be adding new data compliance requirements or regulations as they emerge. The application is no longer the focus of how data gets managed, the data becomes the center of attention. There is still a lot about data streams that needs to be built from how to operate them, how to optimally monetize the cost, and what baseline services are required for it to be most useful.
The use of data streams does not eliminate the ability to analyze historical data stores, but allows you to bring AI, machine learning, and pattern recognition into that process. For instance, you can compare two online historical campaigns and do a reasonable prediction of what you’ll expect from a third campaign. This allows for event-processing and anomaly detection with machine learning.
Data stream technology is available now, but you would need a team of very strong developers to get it working at scale. The question is whether the average enterprise can adopt this quickly enough. If adopted, data streams will drive how organizations work and make real-time decisions in the future. It is an exciting trend and we look forward to seeing how real-time stream processing evolves.
Wed, 18 January 2017
Our guest on the podcast this week is Zohar Alon, CEO and Co-Founder at Dome9 Security. We discuss the shift from information security in the 1990’s to the cloud security of today. We look at how Dome9 protects company security at the network-level by being proactive and continuously monitoring and updating security practices to mirror the constantly changing cloud environment. Dome9 is a SaaS solution that helps security teams streamline their operations, management, and compliance across their cloud infrastructure. Traditional internal security teams at large enterprises are well-equipped for traditional networking, but these teams are not ready to keep public cloud infrastructures secure on their own. The rise of public security breaches is a result of hacking being a business now. People around the world are monetizing knowledge, which is why there have been so many public data breaches now from Yahoo to Target, Home Depot to the DNC. What we have learned from these breaches is the importance of leadership being transparent with customers when they happen and taking responsibility for the breach. Most instances we hear of come from cloud-related hacking of hosted environments the brands used. Hackers are trying every door and window to get into a brand’s system every second, and the only way to stop this is through continuous proactive security. We also talk about serverless computing such as AWS Lambda and what that means for security. With serverless computing, functions are triggered by a change, so the function observes until it sees something it can act on, and it is given the right permissions to act on its own and make a change to the API or the VPC. This presents many security challenges that customers are not fully aware of because permissions can easily be given out more than is required.
Wed, 21 December 2016
We discuss upcoming enterprise cloud trends for 2017. We look at how collaboration is advancing with platforms like Slack and determine whether this trend is here to stay. Deep learning is another important 2017 focus, especially because the economics have become favorable and analytics no longer cost as much. This means we can now create things like neural networks and advanced predictive algorithms at a much lower cost. We also look at the return of SQL, and discuss whether it ever left at all. It seems SQL will be important in 2016, from MongoDB and beyond. We see how containers are here to stay and to manage clusters, Kubernetes will be the top player in 2017. We see how serverless computing makes development easier, especially looking at AWS Lambda. Instead of deploying apps and microservices, we now deploy functions that do only one thing with serverless computing. This is a lot more to manage for large enterprises because of how granular functional programming can become. We also look at custom cloud processors as an alternative to using major chip manufacturers and processors. IoT will continue to forge ahead in 2017 and the keyword will be interoperability. With so many new devices, standards on security and governance will be crucial next year. IT spending as we know it may change in 2017 as more and more enterprises look into PC-as-a-service models instead of purchasing expensive equipment. Last, we discuss the rise of Python programming language and where it will go in 2017 from data science to teaching it to kids.
InfoWorld: 9 enterprise tech trends for 2017 and beyond
Tue, 13 December 2016
Our guest on the podcast this week is JP Morgenthal, CTO, Digital Applications, Americas at CSC. We discuss new product releases, improvements, and announcements from AWS re:Invent 2016. It is clear Amazon still is the leading cloud provider and this year proved that they still know how to innovate and their lead has not made them sit still. This year they extended many services already in play from Amazon EC2 to AWS Lambda and announced new services like Amazon Athena, Lightsail, and AWS Snowmobile and Glue. For the first time, Amazon began to release services directly for business use-cases instead of features targeted only to IT. We also look at next wave proficiencies and what will be important for those seeking careers in cloud computing. These proficiencies used to be defined by certifications (CISCO, CCIE, NetApp). With the rise of cloud computing we saw some general cloud certifications emerge, but none caught on. Now the most sought after skill is AWS Certification, and it’s likely Azure and Google will follow soon with their own certifications.
Thu, 8 December 2016
Our guest on the podcast this week is Mat Keep, Director of Product & Market Analysis at MongoDB. We discuss the differences between open-source database systems like MongoDB and relational databases that use SQL. In the early days of open-source, it used to be built to replicate what proprietary software did well. Today, open-source is the innovator because that is what developers expect and need to be successful. In many ways MongoDB builds on the success of relational databases and replicates their best features while allowing for more rapid innovation and time to market. Modern applications deal with data that is both structured and unstructured, which means the database needs to be able to evolve rapidly. At its core, MongoDB is built for developers to improve productivity. It allows them to develop applications much faster and to build what had previously not been possible on relational databases.