— ROELBOB
— ROELBOB

Kubernetes and Docker are two of the most commonly heard buzzwords in modern DevOps conversations. Docker is a tool that enables you to containerize and run your applications, and Kubernetes provides you with a platform to orchestrate or manage these containers—since managing thousands of containers manually using the Docker CLI would be a practical nightmare.
However, simply running thousands of containers and managing them through Kubernetes is not enough. You have to observe and analyze them properly to ensure that your services are up and running optimally. This process is called Site Reliability Engineering (SRE), a term initiated and popularized by Google. Observability and analysis are a major element of SRE. It can be subdivided into the following three areas:
This article will list and describe the most effective open source tools for monitoring and analyzing services running as containers.
Prometheus is the first tool that comes to mind when discussing open source monitoring solutions. It is very popular in the development community, and it is a graduate of the CNCF project, which focuses on the monitoring side of SRE. Originally created and open sourced by SoundCloud, Prometheus simplifies the process of pulling numerical metrics based on a time series from a given metrics endpoint. It was built to monitor highly dynamic container environments.
Prometheus is subdivided into three elements: Prometheus server, Alertmanager and exporters. Exporters are stand-alone, independent processes/containers that can be run on your target resource to generate and export metrics via a metrics API. The Prometheus server then takes care of service discovery as well as pulling metrics from the exporters to be stored in the Prometheus DB and later used for visualization or alerting. Alertmanager is responsible for setting up alert rules, analyzing the data in the Prometheus DB and sending alert messages to multiple receivers if a certain rule is triggered. There are a huge number of exporters, listed here, which are both officially supported by Prometheus and maintained by the community.
Prometheus has become the industry standard for monitoring cloud-native architectures. While it is known for its simplicity with service discovery, its ease of use, its alerting capacity and its integration with Kubernetes, its polling architecture is not ideal. Currently, the metrics endpoint has to be reachable by the Prometheus server. However, there is a pushgateway implemented in Prometheus that enables metric pushing rather than polling. Another downside of Prometheus is that it doesn’t scale well. This problem can be fixed in the Thanos adaptation of Prometheus.
Related tools and technologies: Grafana, Cortex, Thanos, Prometheus Exporters, Alert Manager, Istio, Prometheus Operator.

Grafana is an open source metric analytics and visualization suite. It allows you to create custom dashboards using data taken from multiple sources, such as Prometheus, Elasticsearch, MySQL, Postgres and Redis. Furthermore, Grafana has its own alerting system and a role-based access control (RBAC) system for software. As a data visualization tool, Grafana is famous among Prometheus users for enabling them to effectively visualize the metrics stored in Prometheus. There’s a wide array of official and community-built custom dashboards for various data sources in Grafana, allowing users to easily set up dashboards (found at this link) and get on with monitoring. Grafana offers another associated product called Loki, which aggregates logs in Kubernetes and integrates well with the Grafana UI.
Related tools and technologies: Loki, Prometheus.

Elastic Stack is a group of open source products from Elastic designed to help users search, analyze, and visualize data from any type of source, in any format, in real time. This product was previously recognized as the ELK stack, where each letter of the acronym stood for one of the company’s main products: Elasticsearch, Logstash and Kibana. Elastic Stack provides both monitoring and logging solutions with the help of its big data database, Elasticsearch.
In order to aggregate logs, people tend to use Elasticsearch for storage, Logstash or Fluentd for log streaming, and Kibana for visualization. Fluentd is not part of the Elastic Stack, but it is widely used with Kubernetes to stream logs in place of Logstash, the tool provided by Elastic Stack. Similarly, Metricbeat is used to scrape metrics and set up visualizations on Kibana. The enterprise version of Elastic Stack ships with X-Pack, a set of additional tools that enables reporting, alerting and role-based access control (RBAC), among other features. By default, the Elastic Stack GUI, Kibana, does not support RBAC. You have to use the previously mentioned enterprise version of Elastic Stack to enable it.
Related tools and technologies: X-pack, Metricbeat, Logstash, Kibana.

Sensu Go is a telemetry and service health checking solution for multi-cloud monitoring at scale. It allows you to gain visibility into servers, containers, services, applications, functions and connected devices across any public or private cloud. Sensu can be run side-by-side with Prometheus to get the best out of both solutions, or it can be run natively without Prometheus. Sensu works best alongside Prometheus, since exporting application level metrics to Prometheus requires loading the Prometheus SDK to your application codebase and exposing a metrics endpoint. The endpoint is then scraped and stored in the Prometheus Server. This may sound like a lot of work—and, sometimes, it is. Sensu avoids this complexity by using a sidecar concept. A Sensu agent is deployed alongside your application. The agent constantly collects and exposes metrics to the Prometheus server without your having to change the application codebase.
Sensu works without Prometheus too. It can operate natively in Kubernetes, where it has its own server to store and visualize metric data exposed by the previously mentioned Sensu agent.
Related tools and technologies: Prometheus.

Sysdig has two open source products: Sysdig Inspect and Falco. Here, we’ll focus on Inspect, which monitors and captures the container processes running in your system and allows you to drill into those processes for post-incident forensics. This enables you to analyze your application performance, troubleshoot errors and monitor any processors that could be misbehaving. Furthermore, if your system is breached, Sysdig allows you to understand how the breach occurred and what data was taken in the process. Sysdig Inspect is a very powerful tool that focuses on the performance tuning and security investigation of your system.
Related tools and technologies: Grafana, Sysdig, Sysdig Falco.

Jaeger is an end-to-end distributed tracing solution that was open sourced by Uber Engineering. It allows you to monitor and troubleshoot transactions in complex distributed systems. In modern microservice architectures, the majority of operational problems fall under the umbrellas of networking and observability. When a service failure occurs, you have no idea how the requests have gone from service to service over the network to complete a single business transaction. This makes debugging extremely difficult. Currently incubating under CNCF, Jaeger uses tracing to enable root cause analysis, performance and latency optimization, and distributed transaction monitoring. Jaeger works out of the box with Istio, a popular service mesh implementation open sourced by Google.
Related tools and technologies: Prometheus, Jaeger, Zipkin, Istio.

These tools are widely used in the tech industry, and they all have their benefits. Most of these solutions, however, require skilled implementation and ongoing manual maintenance that can be a burden for DevOps teams and a distraction from the business. There’s no one solution that can cater to all of your requirements, since each tool focuses on one or two specific aspects of observability and analysis. By mixing these tools together, you can derive a unique solution for your individual business needs.
For ease of comparison, the chart below outlines the features offered by each of the tools discussed in this article.


Rancher Labs is a great example of a startup that was smart enough to sense the way the market was moving and adopted a strategy to capitalize on it. And, if you know Rancher co-founder, CEO Sheng Liang, you should not be surprised. Sheng has been seeing the market and staying one step ahead for a long time.
In this DevOps Chat, we sat down with Sheng and spoke about the recent momentum at Rancher. We also spoke a lot about Kubernetes becoming the industry standard for compute.
It is always a good learning experience speaking with Sheng, and this conversation is no different. Have a listen.
Alan Shimel: Hi everyone, this is Alan Shimel for devopsy.kinsta.cloud and you’re listening to another DevOps Chat. I’m really happy to be joined by a good friend of mine who I’ve known probably more years than we want to admit to, my friend Sheng Liang who is the CEO of Rancher Labs. Sheng, welcome.
Sheng Liang: Thank you very much. I’m glad to be here.
Shimel: I’m glad to have you here, my friend. So, Sheng, the news with Rancher, I guess we call it a momentum release, right, you recently put out.
Liang: Yeah.
Shimel: Why don’t you just share some of the highlights with the audience?
Liang: Yeah. Every January, early February, we tend to look back the year before and see how we’ve done, how the whole industry has done, and it’s just been remarkable. I guess the top-level thing is our business, our recurring revenue grew 169%, so that’s like high three-digit growth. It’s way exceeded our expectation. That itself is good news for us, but it’s not really–I don’t think an average person in the industry needs to care about it as much.
What I want to, Alan, spend some more time talking to you is some of the underlying technology changes and industry movement that’s kind of enabling us to thrive in this environment.
Shimel: Absolutely. Let’s be fair, congratulations. A hundred and sixty nine percent year-over-year growth is nothing to sneeze at. You’re right, there is a little bit I think of a rising tide lifts all boats when we’re talking about the Kubernetes market. Nevertheless, 169% is still 169%. You and I both come from that startup world and congratulations.
Liang: Thank you.
Shimel: It’s something to be proud of.
Liang: Yeah.
Shimel: Also, for our audience out there who are not familiar with Rancher Labs and Rancher OS, it’s a great startup story. Sheng, your background, cloud.com, all the things you’ve been involved in, you’re the perfect person to have led this. But Rancher Labs came out there originally with the Container Revolution, right?
Liang: Yes.
Shimel: With an OS for running containers. You were agile, nimble, adaptable enough to see Kubernetes and say, “Hey, we’re not gonna–we’re shoveling sand against the tide, right, if we think we’re gonna beat Kubernetes. If you can’t beat them, join them.” You incorporated Kubernetes into the Rancher Lab system.
Liang: Yes, and in a fairly unique way as well. I’d love to share the story a little bit if you don’t mind.
Shimel: No, you’ll tell it better than I will.
Liang: We’re just over five years old, over 200 people now. We operate in 14 countries and we’re open source business. It’s 100% open source. Every single product we ship is available free in source code form, Apache open source, and it really worked well for us. I know in the industry there’s actually been a lot of talks about “Is open source even a great business anymore in the context of cloud?” But we continue to believe so, at least especially in the industry we’re in, and we can talk a little bit more about that.
But let’s talk about the business and market and Kubernetes. The great opportunity we see with Kubernetes is Kubernetes for once has become an industry standard for compute. Now, think about it. We all know compute these days is everywhere. Computing is on cars, in ATM machines, on your phone, certainly on the server, in the cloud, but really, before Kubernetes where the world was getting into was quite challenging for a lot of organizations.
Because these clouds as good as they were, as good as they still are, they’re just different. If I’m a CIO, say if I wanna be efficient, I wanna maybe go all in on one cloud whether it’s Azure or Amazon or Google, but until the day comes, now I merge with another company and now bringing a whole different set of infrastructures, and I don’t really wanna worry about that because my whole point is develop the apps, transform the business, help the–all the stuff I’m doing to worry about infrastructure is just plumbing, and that’s the true power of Kubernetes.
I don’t think enough people in our industry are promoting Kubernetes this way yet, so I’d like to make a strong point with you, that in my mind, Kubernetes is the TCPIP of compute. Now, think about it.
Shimel: It’s the stack. It’s the [Crosstalk] as you say. It’s the protocol.
Liang: It’s a stack and a protocol because before TCPIP if you and I could remember back in–
Shimel: I do remember.
Liang: There were a handful, almost a dozen different network protocols, and after TCPIP we had 3Com, we have Cisco, we have Yahoo!, we have Google. All that stuff happened, internet. Revolution happened because finally–
Shimel: I remember when you had to add the TCPIP stack separately. It didn’t come with Windows, so you had to add that in. That was an add-on. Early open source stuff.
Liang: That’s exactly what’s happening because–this is a great point you brought up. Someday people will say, “Oh, I remember there was a time I had to talk to vendors like Red Hat and Rancher just to implement Kubernetes, whereas today if I get infrastructure like from Amazon, Google, from VMWare, from Dell, wherever, it comes with Kubernetes.” That’s exactly the same thing that’s happening. You see that, right?
Shimel: Yeah. History repeats itself. When you get old enough, Sheng, you realize that. Let’s talk something else. I wanted to talk a little bit about CNCF because that’s their mission to make sure that Kubernetes is part of the plumbing. Truthfully, it’s not just Kubernetes. When you look at CNCF’s mission, they truly are trying to develop a stack or at least help foster the development of a stack built on several open source projects.
Beyond Kubernetes, for Rancher now, what are some of the other CNCF projects that you see making their way into Rancher either now or in the future?
Liang: I would say pretty much the whole stack. We’re very deeply involved in CNCF. We’re very early member of CNCF, and my co-founder Shannon Williams is elected to the CNCF governing board. Just this week I was elected to the CNCF TLC.
Shimel: Really? Congratulations.
Liang: So, I have some power kind of deciding what projects are gonna go into CNCF. CNCF fundamentally is a governing body for projects, and really our goal is anything and everything that–we wanna provide basically the complete software stack that organizations need to complete their cloud native transformation. Kubernetes is really just the start. Beyond Kubernetes you see everything else that’s going on from monitoring to logging. I’m talking about projects like Prometheus and ____.
Those are some very mature projects now. Some of the more recent projects, things around security and policy management like OPA. I think those are very, very–
Shimel: Love OPA.
Liang: Yeah, and service match Linkerd. That’s also part of our CNCF. We last year contributed to a project called Longhorn. It’s cloud native storage, Kubernetes storage into CNCF. It’s a CNCF sandbox project now. So, it’s pretty much really the whole stack. If you think I was saying Kubernetes is TCPIP, then the rest of the CNFC, it’s the rest of the software stack, from the platforms to some of the key applications to some of the tools and equipment that more people have used.
They’re gonna build this cloud native ecosystem. So, I’m really excited about it. I’ll be serving this CNCF technical oversight committee for the next two years and play a role in really growing the scope, further grow the scope of CNCF.
Shimel: I just wanna say one other thing about CNCF, and that is we did a virtual event here called Predict last month where we took a look at a year in the future, and one of the points I made is that to me, CNCF is sort of the poster child for this third generation of open source that we’re in. I call it the first stage, sort of the cathedral in the bazaar back in the day. I know you know about it, Sheng.
And then we moved from the cathedral and the bazaar to what I call the big brother era where every open source project was really owned by one company, and everything that that project did was for the benefit of one company, and that discouraged others from joining in and helping do it. Now we’re in sort of the foundational era of open source where you have foundations managing these projects for the good of the whole community with multiple vendors involved, people like you on the board who are doing it.
Yes, it’s good for Rancher, but it’s good for all of the community, and that’s a very different–as I said, it’s the third generation of open source and it’s a great thing. Let’s focus in on Rancher a little bit though, Sheng.
Liang: Yeah, absolutely.
Shimel: So, we’ve got all these great projects. You’re literally building the stack or at least distributing the stack, your distribution of the stack. Fantastic revenue growth. What are people doing with Rancher that this demand is in such high demand?
Liang: Yeah, I know. It’s one thing that Rancher of course supports and distributes a lot of CNCF technologies including Kubernetes and Prometheus and Linkerd and Fluentd, and we’re gonna soon be doing OPA. That’s all valuable, but fundamentally, a company like us really only have a reason to exist if we innovate. We basically create things that people can uniquely get from us and not from anywhere else.
There are two pieces of content or two technologies that we created that actually help put Rancher on the map. One is K3S. It’s one of the most–if not the most–popular open source project in the Kubernetes ecosystem today outside of Kubernetes itself, and it’s just got tons of momentum. What K3S achieved was it took Kubernetes that was already proven in data center and cloud, and we brought it out to Raspberry Pi’s and surveillance cameras and set top boxes.
Shimel: This is K8 lite. I remember we spoke about this, the one in the KubeCons. I remember.
Liang: We launched GA at KubeCon, so it’s very big. We’re very excited about it. You remember, again I keep using TCPIP as an example because in the 90s when TCPIP just came, it was obviously a computer networking standard, but nobody probably could’ve imagined like a phone would also be talking TCPIP, right?
Shimel: They all do.
Liang: They all do now. I can see in ten years every phone, every camera will be running Kubernetes as well. Every car will be running maybe multiple Kubernetes. So, we’re very excited about that. Then the other big piece of innovation that really Rancher helped to make it happen is this thing called multi-cluster management. We believed Kubernetes again is a protocol. It runs everywhere, so you kinda need something to tie them all together.
It’s not really good enough to be able to operate individual clusters. You need to treat all the clusters as a resource pool. In fact, you actually kinda have to treat every single cluster as a cattle or as a pet. That’s where the Rancher analogy came in.
Shimel: I remember talking to you about that three, four years ago.
Liang: I know.
Shimel: It’s cattle, not pets.
Liang: Our namesake product Rancher, which is named after our company, is a Kubernetes management platform. It takes all of your Kubernetes clusters in the cloud. In the cloud, that could be GKE, EKS, AKS. Could be Kubernetes clusters on-premise. It could be RKE. It could be open shift. It could be Project Pacific, PKS. It could be K3S clusters on the edge in cameras.
We present it to your DevOps teams and ITOps teams as a unified computing platform, so you can manage access, you can deploy apps, you can run CICB, and that’s what Rancher is. It really started to resonate last year, even though we’ve been building it for three, four years, and that’s ultimately what propelled our tremendous business growth last year.
Shimel: Great. Sheng, we’re coming up on the end of our time. I’m gonna throw one more kind of topic question out at you we can discuss, and that is really what does the future hold for Rancher not necessarily from a product deployment point of view but you’ve started and had exits with multiple startups now in your career. Where do you see Rancher in two to three to five years? Do you go public with this? Is this something maybe some larger entity wants to acquire to kinda own the stack if you will? What do you think?
Liang: I think these days I probably think a little less about ultimately how Rancher will exit, just making sure we build a great business. I think we’ve kind of grown past the point that sort of an early stage easy equal hire or a technology would be viable.
Shimel: It’s not a tuck in.
Liang: We’re big enough to be sustainable by ourselves, and honestly, I absolutely believe it’s a big enough market. We solve a big enough problem that there’s a potential for tremendous business. Think about people actually all talk about hybrid cloud and multi-cloud, edge computing, but I think a lot of these terminologies are very attractive from a market demand or business case perspective but always lack some technical meat to actually bring it to reality. That’s actually been the problem.
Of course, we all know hybrid cloud is a real business need, but the problem is because there was no fundamental consistent experience that could actually implement that. So, as a result, no big businesses have been built. We kind of approach it a little bit differently. We’re very much centered around Kubernetes as a common protocol for compute.
I think by building on that, I really believe there’s a tremendous business opportunity here because fundamentally, we’re gonna solve these multi-cloud, hybrid cloud and edge computing problems. Those are business problems with Kubernetes as the common compute protocol. So, I think that will keep us busy for the next five, ten years, and I think this business has lots and lots of room to grow.
Shimel: Let the chips fall where they may. Sheng, we’re about out of time. I wanna thank you for taking time out of your busy day and joining and talking to our devopsy.kinsta.cloud and Container Journal audiences. Always a pleasure to speak with you and get an update on what’s happening at Rancher Labs. I will see you in about a month and a half in Amsterdam for KubeCon.
Liang: Thank you. Thank you, Alan. It was great talking to you.
Shimel: Always a pleasure. Sheng Ling, CEO, co-founder Rancher Labs. This is Alan Shimel for devopsy.kinsta.cloud and Container Journal, and you’ve just listened to another DevOps Chats.
Red Hat today made available an update to its distribution of OpenStack that enables it to run on Red Hat Enterprise Linux (RHEL) 8 operating system.
Sean Cohen, senior manager for product management at Red Hat, said Red Hat OpenStack Platform 16 combines three releases of OpenStack, known as “Rocky,” “Stein” and “Train” into a single update that the company plans to update on a streaming basis. In addition, future updates to Red Hat OpenStack Platform will be aligned with updates to RHEL 8, said Cohen.
Red Hat OpenStack Platform 16 is also now being made available under a long-term support subscription as well.
Cohen said Red Hat continues to see continued growth in terms of customers adopting OpenStack to build their own cloud computing platforms. Telecommunications companies have already made major investments in cloud frameworks that are now being extended to the network edge. Earlier this week, Orange Egypt announced it will be using Red Hat OpenStack Platform to deploy virtual network functions (VNFs) to manage network traffic. Enterprise IT organizations, meanwhile, are building their own private clouds on top of an open source platform.
In some cases, enterprise IT organizations are relying on OpenStack to provide the foundation on which cloud-native applications based on Kubernetes are deployed, added Cohen. The goal is to take advantage of the virtual machines on which OpenStack is deployed to ensure isolation between instances of containerized applications running on top of Kubernetes. In other cases, IT organizations are taking advantage of tools such as Ironic to automate the deployment of Kubernetes cluster on bare metal machines, said Cohen.
The relationship between OpenStack and Kubernetes, however, remains complicated because there are those organizations that are deploying Kubernetes clusters in on-premises IT environments without relying on any legacy infrastructure-as-a-service (IaaS) platforms such as OpenStack or VMware vSphere. Nevertheless, Cohen said Red Hat expects to see wider adoption of the Red Hat OpenShift application development and deployment platform, which is based on Kubernetes, on top of Red Hat OpenStack Platform 16.
Cohen said there is a high correlation between organizations that have embraced best DevOps practices and usage of OpenStack as IT organizations move to automate the management of IT infrastructure at scale. The challenge organizations face is determining which modules of a very comprehensive OpenStack framework to employ. In fact, that challenge is why organizations rely on Red Hat to curate OpenStack on their behalf. In terms of adoption, Red Hat has the largest market share of any OpenStack distribution, said Cohen.
It’s not clear to what degree the shift toward cloud-native applications based on Kubernetes might result in increased adoption of OpenStack. As organizations look to build hybrid cloud computing environments, the ability to deploy a common framework on-premises and in the public cloud has a certain appeal. However, public cloud computing platforms such as Amazon Web Services (AWS) already make available a proprietary platform for managing their IaaS environment. Many IT teams would naturally prefer to integrate with that AWS platform rather than replace it. Regardless of the approach to hybrid cloud computing, however, chances are high OpenStack will be in the mix in one form or another.

There’s never been a more exciting time for data. Increasing demands for size, speed, performance and reliance have propelled the data market forward at a faster rate than could have ever been anticipated, and new technologies are making some dreams for data a reality.
From open source adoption in place of legacy databases to cloud-native app stacks and more, here are seven trends driving the acceleration of data and data storage.
Organizations of all sizes continue to eschew Oracle for cloud-native databases, shrinking the user base of the monolithic legacy provider. Two major trends driving this activity are the ongoing move to microservices-based redesign of applications and the rise of cloud-native deployments running in public clouds or Kubernetes. Overall, Oracle has lost market share every year since 2013, and legacy relational database players have dropped about five percentage points per year. Major organizations, such as Amazon and Salesforce, have already figured out that it’s to their benefit to use less, not more, Oracle.
Open source databases that claimed 0% of the market 10 years ago, now make up more than 7%. It’s clear that the market is shifting and an increase in open source database adoption is on the rise. This goes against the recent trend of database and data infrastructure companies abandoning open source licenses for some, or all, of their core projects. However, as technology rapidly advances it will be in the best interest of database providers to switch to a 100% open source model, since freemium models take a significantly longer period of time for the software to mature to the same level as a true open source offering.
Furthermore, Gartner predicts that, by 2022, more than 70% of new in-house applications will be developed on an OSDBMS or OSDBMS-based dbPaaS. It’s clear that open source has proven to be the most successful approach to developing and distributing business-critical infrastructure software, driven by the benefits of reduced total cost of ownership, access to community support and innovation and compatibility with commercial databases, making it easy to migrate towards an open source solution.
There has been continued growth in the adoption of both microservices and containerization. With the combination of massive adoption and the support of a large and thriving open-source community, it is clear that the dynamic nature of containerization and microservices deployments will have huge implications on fundamental application architecture. To put it simply, this is the future of how applications will be built. This is also why the adoption of Kubernetes has continued to grow.
Multi-cloud deployments are becoming the norm in today’s enterprise. A multi-cloud approach is critically important for organizations that run on-premise, since they need to stay in a hybrid mode when moving microservices to the cloud. As a result, enterprises are widely embracing distributed SQL databases to ensure agility without the availability constraints of traditional monolithic databases, such as Oracle.
While the rise of edge data centers is assuredly a trend, Gartner predicts that three quarters of all databases will either be deployed or migrated to a cloud platform by 2022. There will also be an increase in multi-cloud adoption as a result of the geo-distribution of data. Edge data centers are already starting to offer data services with geo-distribution, a clear sign that demand is creating supply.
Modern microservices-oriented architectures are becoming increasingly popular. Over time, this leads to environments with lots of microservices that should be able to notify one another of changes and store these changes in a system of record. The application stack that is getting very popular for such use-cases is a combination of:
Other application stacks that are rising in popularity are the JAMstack ( Javascript, APIs and Markup) where there is no dependency on any particular web server. Javascript frameworks such as ReactJS and VueJS have become very popular for building the frontend portion of the app. GraphQL frameworks such as Apollo and Hasura are getting increasingly adopted as efficient ways to build the API portion of the app.
Advances in innovation for data and data storage will undoubtedly continue, driven by major trends across all areas of business, as long as organizations are prepared to embrace change and hit the accelerator on their data strategies.

Gartner estimated that by 2020, 20% of the world’s organizations will have gone serverless. Serverless computing initiates a mind shift in the way businesses are run and improves the accuracy and impact of technology services. Leading to higher functionality, reduced AWS bills, increased delivery speed and multi-cloud support, serverless computing paves the way into the future.
The scope of serverless is yet unexplored. With new and high-potential developments (such as FaaS and GitOps) still in their nascent stages, organizations can expect massive benefits leveraging the power of serverless computing going forward.
Servers have been a very important part of the computing architecture for decades. But, with the introduction of cloud, we’ve moved to predominantly web-based server infrastructure, and the migration to serverless architecture has begun.
Serverless computing–abstract and virtual server support–has made the computing landscape more dynamic, robust, intuitive and powerful. But advancement in serverless technology is still in its nascent stage. The next few years are expected to bring in numerous developments and transformations.
First, let’s take a look at some serverless trends you should keep an eye out for.
Kubernetes has become synonymous with open-source containerization. But in the next few years, we will see Kubernetes become the base for serverless infrastructure. Features such as networking, service discovery, agile scaling and multi-cloud environment support make Kubernetes the perfect choice for serverless.
Additionally, with Knative already released, the process of standardization and interoperability of serverless platforms may soon become a reality.
Restriction of serverless for stateless apps is a thing of the past. The next few years will see stateful apps benefiting from the immense power of serverless. Developers will be able to use database connections, BaaS and event payloads to run their applications.
The FaaS market is expected to grow to $7.72 billion by 2021. Large-scale adoption of serverless will be seen across the world, and there will be a 32.7% CGAR.
The version-controlled system of DevOps, GitOps, will see more takers in the future, especially in FaaS. GitOps is expected to improve operational workflows, especially in fast-moving projects, and enable implementation of technologies such as Kubernetes and other cloud solutions.
Additionally, this will enable cloud vendors to provide greater ecosystem support and tooling capabilities to users.
Containers by nature are segmented, isolated and contained spaces of application-specific servers. While they offer high degrees of specialized (and often customized) tools, resources and support, they suffer from numerous challenges:
The challenges that come with containers prompt organizations to choose serverless. Already, many companies have implemented serverless.
Here is how the move to serverless will benefit you, too.
Developing even the most basic server-bound application is a challenging task. It requires the set-up of high-powered and complex application stacks that can be both difficult to build and difficult to manage. By going serverless, organizations can eliminate all these heartaches.
Serverless stacks, such as Lambda and Azure, create a robust and intuitive environment for the development of agile applications, by providing organizations with a large set of capabilities and allowing them to run at scale, at the lowest costs.
Unfortunately, IT infrastructure is considered a capital expenditure by many organizations. So, building your own servers can lead to a high CapEx.
Consider this. Physical servers are vulnerable to breakdown and depreciation, which add to CapEx. If you report this value as expenses in your income statement, it may ultimately negatively affect your P&L and your company valuation. Going serverless allows organizations to avoid such a challenge and maintain their profitability and high valuation.
Plus, serverless infrastructure reduces the need for human resource deployment as focus on batch processing decreases. Additionally, serverless allows organizations to convert workflows into step functions, which reduces the number of EC2 and SWF issues. Together, these reduce company expenses tremendously.
Serverless computing allows organizations to benefit from the magnificent power and scope of microservices. From making minute application-specific developments to isolating and rectifying issues, to managing and deploying individual applications, organizations can do a lot of things using serverless computing.
In fact, a survey shows that utilizing serverless microservices can bring down the standard release cycle from 65 days to a mere 16 days.
Again, serverless computing microservices gives organizations the flexibility, technical support and clarity needed to process data. ELT tasks become more consistent and allow the creation of structured data warehouses.
Similarly, since isolated components and applications can be created, deployed and fixed in serverless, it’s easier to implement cron-type jobs as well.
Adoption of serverless computing allows organizations to shift from the restrictive, time-consuming and draining traditional workflows and engage in event-based computing. Now, coders can just write the algorithms and allow the serverless provider to take care of the data storage and computing needs. Not only will this reduce the time of transaction processing and the cost of DevOps, but it will also free up the time and space for coders to take on other business logic tasks.
With climate change becoming such an imminent threat to the world, it’s important for organizations to become mindful about the way they operate. One way to become sustainable is to go serverless.
For one, serverless computing ensures organizations don’t over-utilize electricity. Additionally, with no physical servers and data centers that emit toxic radiation as they cool down, there will be lesser concerns about health and climate change.
Another aspect is scalability. Serverless is highly-scalable, meaning you don’t need to set-up more servers at your office. Given that 30% of the world’s servers are unused at any point in time and most servers only utilize 5%-15% of their total capacity, serverless may be the best way to go green.
Serverless computing gives organizations the freedom to focus on their core business offerings and deal with only low-level OS issues. This time, money and opportunity can be used by deployers, coders and top management to develop competitive offerings, which can help the organization overtake competitors.
With physical servers, it’s more likely than not for organizations to focus on technical server issues than the business process.
Serverless computing could be one of the most exciting developments of the 21st century. It is breaking down old barriers and allowing organizations to expand and explore new frontiers. Its high deliverability, coupled with the immense opportunities it promises, makes it a must-adopt in any organization.

Open source is the dominant model of software consumption in the modern era. Cutting-edge startups and entrenched incumbents alike find open source software development and community building a significant part of their overall business strategies.
For many open source projects, independent steering committees ensure that projects remain stable and are not beholden to a single company or profit motive. Alternatively, for open core projects where a single company financially supports and contributes the vast majority of open source code, open sourcing makes a project publicly available and accessible for potential new users. Opening a project to public contribution widens the pool of potential users and contributors, therefore ensuring more rapid innovation than commercial software offerings.
There are seemingly limitless open source projects to evaluate. Further complicating matters, each project has its own domain competence and cultural nuances. Considering a single distributed application will have tens or (many) more open source dependencies, development teams must continuously test new open source offerings and hone new skills to use these solutions, while simultaneously architecting their applications.
In order to help sort through the noise, I’ve looked at six categories of open source projects for this article: data storage, message systems, service meshes, REST frameworks and streaming frameworks. For each category, I’ll identify any dominant players as well as some other projects of note.
Here are the top 13 open source projects by category.
Apache Cassandra, originally developed at Facebook, is one of the most widely used open source projects for real-time applications. Functionally, it’s a distributed NoSQL database management system. But practically, it’s become the linchpin of most at-scale, real-time applications. This is because Cassandra was designed to handle large amounts of real-time data across many servers, and has robust clustering support.
For applications that require extremely low latency or have exceptionally high data rates, Hazelcast may provide a useful alternative. Unlike Cassandra, Hazelcast is an in-memory data grid database, which enables development teams to benefit from the latency advantages of in-memory computing.
Apache Kafka is the dominant open source messaging system used by application development teams today. Originating at LinkedIn, Kafka has grown into the de facto standard for high performance open source messaging systems. For streaming use cases, Kafka users can also turn to Kafka Streams.
Apache Pulsar, developed at Yahoo!, is another alternative messaging system which has been optimized for streaming data sources. Pulsar further differentiates itself with TTL message support and queuing via shared subscription.
For applications where latency is a critical consideration, NATS.io (a CNCF project) is one of the fastest pub-sub messaging systems available.
Trends such as microservices, serverless and hybrid cloud deployment have contributed to the increasing architectural complexity of distributed applications. With hundreds or thousands of distributed application services, service meshes provide a mechanism for pulling all the disparate services together into a manageable and cohesive whole.
The most notable open source service mesh offering is Istio. One of the most popular open source service mesh projects, Istio is primarily used for applications built on top of Kubernetes. Another alternative is Linkerd, a flexible service mesh solution for Kubernetes or other frameworks.
Node.js is a popular JavaScript framework for building scalable web applications. When comparing web development frameworks, Node.js typically ranks among the top three most (along with frontend frameworks React.js and Angular.JS). While Node.js can support large scale applications, it does not support real-time use cases well at scale.
Spring is another application framework, optimized for distributed microservices-style architectures. Like Node.js, Spring “focuses on the ‘plumbing’ of applications so that teams can focus on application-level business logic, without unnecessary ties to specific deployment environments.”
SwimOS is a software framework for distributed streaming applications. Unlike REST-based frameworks, swimOS utilizes a unique and lightweight Web Agent architecture, optimized for composing and transforming many data streams in real-time. Whereas DevOps oriented projects, such as Spinnaker, seek to provide an abstraction layer to converge distributed cloud services, swimOS provides an abstraction across distributed data streams, so teams can build applications by composing many distributed data streams in a single mesh environment.
In order to ensure real-time consistency of distributed services, swimOS automatically ensures eventual consistency between all Web Agents. Complete with a UI toolkit and it’s own stateful messaging protocol called WARP, swimOS is the fastest way to build stateful streaming applications.
Kubernetes is one of the most talked about open source projects in the world. Kubernetes describes itself as “an open-source system for automating deployment, scaling and management of containerized applications.”
While it may simply be a platform for container orchestration, rightly or wrongly, Kubernetes has become a stand-in for DevOps on the whole. In spite of its dominance, there are alternatives to Kubernetes, for example there’s RancherOS to manage Docker containers.
Furthermore, as application architectures continue to become more distributed and complex there is a need for platforms which can converge multiple distributed edge and cloud environments. Spinnaker provides an abstraction layer to connect the various environments and provides a simple mechanism for continuous integration/continuous delivery (CI/CD).
Your own particular use case should determine the appropriate open source technologies for your team’s real-time application. Whether you’re building an ultra-high performance low latency application or simply trying to tame large volumes of real-time data, knowing your options and choosing the right framework or platform can save you significant time and effort.
The open source software landscape is changing every day. If you have something to add about one of the technologies above or think I skipped over the next OSS breakthrough, I’d love to hear about it. Let me know your thoughts in the comments section below.

Today, modernizing systems is more than simply moving technology to a new location. It requires an IT stack of a new generation of technologies and tools to work.
As we head into the new year, let’s take a look at the top eight trends that will shape how we will develop and deliver software.
Yes, it’s big and getting bigger. The pace of enterprise adoption of this leading container orchestration solution will only gain momentum in 2020. Look no further than the 2,300% attendance increase from KubeCon 2015 (500) to KubeCon 2019 (12,000). As more companies begin to see the direct financial and operational benefits to the flexibility, scalability, automation, high availability and portability of Kubernetes, the demand for developers and engineering experts in this complex software will grow accordingly. Case in point: A 2019 national job search on LinkedIn turned up 16,744 open positions for Kubernetes-related roles. Given the already large and still developing Kubernetes community, we should also see an increase in the speed and volume of feature releases.
It’s predicted that by 2022, 90% of all apps will feature microservices architectures. The growing business imperative for software engineering teams to produce and release software in ever shorter cycles—continuous delivery—has paved the way for a huge surge in microservices, the ideal architecture for CD. As enterprises continue their accelerated move to the cloud in 2020 and beyond, they increasingly prize the flexibility to try out the new technology stacks that microservices provide. All businesses want to optimize resources, minimize downtime, reduce infrastructure costs and avoid technology or vendor lock-in. In 2020, microservices architectures will continue to offer up more and better of all these benefits as the migration from monoliths picks up speed.
The rise of microservices has led to a parallel boom in service meshes. This will only accelerate, given how integral the current class of service meshes—Istio, Linkerd, Consul, Envoy—is to the successful functioning of microservices. As a software designed to enable the thousands of microservices that make up an application to talk to each other, it’s quickly become an essential component with a vendor ecosystem poised for substantial growth. Feature-rich Istio, backed by IBM, Google and Lyft, is the leading Kubernetes service mesh, and that shows no signs of changing in 2020, especially given the relative ease of setting up and running it. If your enterprise has embarked on the journey from monolith to microservices, it will soon be crossing paths with service meshes.
In the recent past, the sheer complexity of modern distributed systems has outstripped the tools designed to allow teams to see into them. Enter observability—which isn’t monitoring. Like good helicopter parents, software engineers, application developers, SREs and DevOps teams want to know and understand what their systems are doing at all times. LightStep, Datadog and Honeycomb, to name a few, are vendors poised to offer logging, tracing and metrics solutions to the rapidly growing number of enterprise teams building cloud native systems. Keep a close eye out for OpenTelemetry, an open source observability framework currently a member of the CNCF Sandbox.
One of the great challenges ahead for this still-emerging field is for enterprises of varying sizes and work cultures to find a form of SRE that works. What succeeds at Google may not be the recipe for SRE implementation at smaller tech enterprises or startups. The role of SRE and the expectations that go with it remain fluid. The SRE’s constant imperative to automate will only add to the profile of critical SRE and DevOps tools such as Terraform, Ansible and Jenkins. Ponder Amazon’s Prime Day 2018 losses of up to $99 million for a clear understanding of the business-critical importance of SREs in the years to come. The job market reflects this new reality.
The shift-left movement in the software development lifecycle is on. The days of bolted-on post-production security are rapidly fading. DevSecOps, though still a buzz term, is here to stay, with more practice and use cases added to the theory. There are huge opportunities here for automated application security tools that support the software development workflow.
Then there’s privacy. The GDPR took effect last May and the California Consumer Privacy Act takes effect January 1, 2020. On top of these, there’s HIPAA and the regulatory compliance pressures on businesses in the area of security, which are already enormous—plus the immense challenges of cloud security. A 2019 Symantec survey on cloud security threats reports that 65% of businesses in the cloud still aren’t using multi-factor authentication. The same survey also notes that an astounding 85% of Symantec’s cloud customers aren’t employing security best practices. Look for businesses to tend to security earlier in the process.
It’s a boom time in serverless, with Google’s Functions, Microsoft’s Azure Functions and IBM’s OpenWhisk all offering their serverless solutions following the 2014 rollout of AWS Lambda. Look for more businesses to adopt a serverless-first approach as they upgrade legacy systems and see the substantial cost saving and application development benefits. Startups, too, will see the cost-cutting upside and the agility that serverless brings. The changes in application development and architecture in the cloud as a result of serverless are hard to overstate. With the vendor solutions now on offer, developers are freed up to do what they most like to do: run code and add functionality to their application, faster, without worrying about scaling and availability. Like microservices, serverless will see a huge upsurge in usage as the age of the monolith fades.
With the demands of cloud computing and far greater enterprise investment in dedicated teams, DevOps in 2020 is poised to make the transition from policies and principles to actual practice. The addition of baked-in security to the DevOps realm—DevSecOps—adds urgency to the automation of building, provisioning and deploying secure software. ML- and AI-driven apps have arrived, and DevOps practices will play a large role in the development workflow. The goal of zero-touch automation looms large, and this adds urgency to the need for clean data, organizational buy-in and more integrated systems. AIOps, though getting plenty of attention, is still young and its benefits to IT operations are still more theoretical than actual–but stay tuned.
As we head into 2020, it will be exciting to watch these trends unfold. DevOps teams can expect the way we develop and deploy applications–in any architecture–to continue to evolve. By keeping a pulse on new developments in Kubernetes, microservices and other new technologies, they’ll be well prepared to bring their businesses into the new decade.
Want to learn more about what to expect in 2020? Join us Jan. 23 for our Predict 2020 Virtual Summit featuring discussions from some of the industry’s best and brightest offering up their visions for the future. Sign up today for this free daylong virtual event.

Cloud computing was one of the hottest topics in the technology and business media throughout 2019. This is no surprise as the cloud sector has been growing rapidly for the last few years. Synergy Research Group recently reported a 37% overall growth year-over-year in public cloud. They also note that it has taken just two years for the public IaaS and PaaS markets to double in size and their forecast shows them doubling again within the next three years.
While steady growth was expected this year, a few trends of note stand out. When the experts look back at 2019, they will likely stress the following cloud computing trends shaped the year.
We recently did a study on ParkMyCloud users which showed that a growing proportion of company technical leads have cloud or the name of their cloud provider–such as AWS–in their job title. This indicates a growing degree of specialization for individuals who manage and architect cloud infrastructure. Whether called enterprise cloud manager, cloud architect or something similar, 2019 saw the rise of a specialized role to oversee and direct cloud operations.
As enterprises large and small continue to adopt and use more than one public cloud, we have seen a rapidly growing need for a consolidated multi-cloud view and ability to take actions across multiple clouds in 2019. Third-party tools that do this provide enterprises the ability to pick a single tool to orchestrate, manage, govern and optimize their hybrid and multi-cloud environments. Many customers also sight the need to simplify operations—native cloud provider tools are not always easy to use.
Multi-cloud truly arrived in 2019 and will only grow in 2020. The majority of organizations now use multiple clouds. In fact, most studies suggest that more than 80% of enterprises have a multi-cloud strategy. Reasons cited include the need to avoid vendor lock-in, the ability to leverage the best in class cloud services from each cloud provider and application efficiency choices.
One of the biggest tech benefits to a DevOps approach is automating away the manual tasks that bog down operations and critical projects. Thus, the trend toward automation continued to expand in 2019.
IT became more and more abstracted in 2019. This means container usage grew, along with models that allow developers to worry less and less about infrastructure (such as serverless and PaaS).
Application containerization is more than just a new buzzword in cloud computing; it is changing the way in which resources are deployed into the cloud. More and more companies utilize container services such as Kubernetes and cloud native services such as EKS, AKS and GKS in 2019,
Business continued to move to the cloud in 2019 and the six trends described above showed that the sector is growing–sometimes in unpredictable ways. Next year will, no doubt, also contain some surprises. But the trend for expansion of the cloud and related services is unmistakable for the foreseeable future.