Provisioning monitoring checks by hand is slow, too slow when the checks need to keep up with an application that is evolving quickly. This stays true no matter which monitoring service or platform you are using.

Another problem is with documentation. Who documents what the monitoring setup should look like, as a whole and in its parts? What is each check’s configuration? What about the alerting logic for when things catch on fire? It’s up to you to put that all down on paper to avoid the risks that come with having everything live in somebody’s head.

Possibly the worst and often unseen issue, though, is that manual monitoring workflows do not fit in the bigger picture. They do not tie into how software is being built, really. They lie by the wayside, hopefully getting enough of our attention to prevent costly outages while not diverting it too much from our real objective: shipping incredible applications.

Click here to read the full story on ITOps Times.

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These updates are being spread across a number of Sumo Logic’s offerings, including Real User Monitoring, Unified Entity Model, and Intelligent Alert Management.

Real User Monitoring updates include insights related to user actions on a page, long task delay metrics that indicate if the main browser interface has been locked for long periods of time, better dashboard visualizations, and capturing and displaying browser errors in the log index and dashboards. 

Unified Entity Model adds Database Entities, which automatically detects data, delivers a user-friendly grouping of database entities, and displays them, giving developers a more holistic view of data. Sumo Logic Entity Inspector also now displays related APM Entities on the Infrastructure Entity dashboard, which will make it easier to switch between contexts. 

Intelligent Alert Management introduces Intelligent Alert Grouping, which simplifies alert management by allowing developers to specify conditions for which alerts are generated.

“Reliability is a journey for any organization. Our goal is to give product, business leaders and developers a framework to prioritize what will make the most impact based on real data,” said Erez Barak, VP of product development for observability at Sumo Logic. “With these latest investments, we continue our track record of offering tools that bring innovation-driven insight into view and doing our part to transform enterprises for modern, digital business.”

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There are plenty of real-world examples of this taking place every day. Wider access to broadband internet made technology more accessible to everyone. Now without a need to become a technical expert, individuals can leverage technology to stream on their TV with an Amazon Firestick, or become active on social media or YouTube. With access to easily palatable technology, people can build a following, share information, and interact with people across the globe in order to earn a living. This is entirely dependent on complex technology, but the concepts have been made user friendly for everyone.

Democratization of IT

Democratization of IT has taken this concept a step further. It is removing the control and power from the few managers who control an organization’s infrastructure and handing that power to the people who build out software architecture daily.

Why is this taking place? It’s all in the name of innovation and efficiency. The digital transformation is demanding organizations to build and deploy software at increased speeds to stay relevant. Waiting days, or even weeks, for a new server to be spun up is no longer an option. Nowadays, all you need is a credit card and an Amazon Web Services account and you can have a new server to your specifications in minutes.

The bottom line is, as technology becomes more niche and complicated, ease of use is remaining a priority to enable everyone to get access to the IT resources they need to remain efficient. No more bottlenecks.

Further Down the Stack of Tooling: Enter APM

A bit deeper into the stack of IT tooling we find APM. Traditionally, APM solutions have been tools built for a few experts to leverage to monitor the performance of their organization’s applications. But trends catch on, and we’re now seeing the democratization of IT start to extend as far down as these niche, technical APM and Observability platforms.

How can we define the democratization of APM? A variety of boxes must be checked off to help turn this from an idealistic pipe dream into a reality.

Ubiquitous Usage: Instead of only existing in the hands of a few technical experts, the APM platform must be available and accessible to everyone who might benefit from information the solution is monitoring. Moreover, it needs to provide that information in a way that is easy to understand and palatable for different users and be presented in real-time. Making this happen requires:

• Automation – The ability for a platform to automatically discover and monitor all application dependencies, highlighting incidents and flagging them for users proactively, instead of making users search for issues themselves.
• Context – Providing context around data and incidents so that it makes sense to users. This means observing all data, not just samples of it.
• Machine Learning – Whether a simple curated KnowledgeBase or an advanced AI system, applying machine learning to the system of troubleshooting applications makes everybody a “Subject Matter Expert.” That doesn’t mean teaching them about the complex systems that make up the application — rather, it breaks down that complexity and shows users where to start, what to look at, and exactly what steps to take to remediate the situation.

Ease of Use also comes into play:

• Making complicated processes less complicated. For APM to be democratized, it needs to be easy to set up, run, configure, and maintain. Not only do you no longer need to be a technical expert to use an APM platform, but you also don’t need to be an expert to get started and configure the tool to your fitting. This also means no more deep APM tool training sucking hours and hours of people’s time.
• Each user needs to get the data and information they need to do their job specifically. Instead of information overload from a complex array of looking at everything in the architecture that needs to be sorted through, a developer responsible for one application needs to be able to hone in on the data from that application. A QA tester needs to be able to look at only monitoring the code they are working on.

Democratization of APM in the wild

Now that we understand how to achieve APM democratization, spreading the value of the APM solution to the “far reaches” of an organization, it’s time to ask the question – is it actually happening? The answer hinges on which generation tool is in place. For newer APM solutions (generation 3) and organizations that have embraced modern application deployment philosophies, it’s not only possible, it’s happening.

In my own personal experience, I’ve seen a retail company go from 10 operations users managing APM with a legacy tool, to 250 developers and other IT members using it during Proof of Concept. This then extended to over 1,000 developers using the APM tool as it was integrated into other software development life cycle processes.

The democratization of APM is an important, and necessary, step to enable all stakeholders, from developers, to architects, to QA, to speed up their cycles. Organizations can become more efficient when stakeholders have the APM accessibility and actionable data they uniquely need to do their job, minus the manual work. Overall, the democratization of APM is a core component of helping organizations shift left.

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“Today, many Enterprises struggle to deliver their business outcomes with traditional monitoring technology as the majority of legacy tools are incapable of providing holistic business-level observability of a hybrid IT world,” says Mehdi Daoudi, CEO of Catchpoint. “These new capabilities are another important milestone towards empowering IT teams so they can manage the visibility challenges associated with everything hybrid.”

Customers can now perform mesh monitoring to diagnose or fault isolate network-related issues between multiple locations, including ones within an enterprise premise. 

Additional details are available here.

Codefresh announces new GitOps features 

Codefresh launched its new GitOps controller and “current state” dashboard, which makes it easier for developers to install and maintain an ArgoCD instance while the GitOps dashboard offers comprehensive support for ArgoCD’s App of Apps.

“This GitOps controller allows our users to really do GitOps at scale,” said Dan Garfield, Codefresh’s chief open-source officer. “To be able to track changes, drift, and trace everything back to tickets, comitters, and changes means identifying regressions is simple. This gives teams the confidence to deploy more often.”

It offers the ability to make quick visual assessments and provides real-time determination of configuration drift to ensure that deployments are in sync. 

Sopheon releases latest version of Accolade

Sopheon’s latest release of the Accolade innovation management platform connects corporate strategy to portfolio planning and execution processes. 

This release adds new integration capabilities with off-the-shelf integrations of over 100+ third-party systems. 

It also includes a complete product enhancement system that simplifies user feedback and features a new Rich Text formatting of information along with the ability to conditionally show data and other improvements as part of an improved user experience by reducing reliance on external documents.

Snowflake announces support of Unified ID 2.0

Snowflake announced support for Unified ID 2.0 to help organizations easily enrich audience data without sharing users’ personally identifiable information.

Snowflake customers will be able to optimize their data-first advertising strategies by directly activating audiences on any platform that has adopted Unified ID 2.0.

Brands will be able to join purchase data and ad exposure data tied to Unified ID 2.0s, publishers will be able to activate their first-party data with Unified ID 2.0s and data partners will be able to augment data assets of brands and publishers. 

Go 1.17 RC

The Go 1.17 Release Candidate 1 adds support of 64-bit ARM architecture on Windows and the 64-bit MIPS architecture on OpenBSD (the openbsd/mips64 port) now supports cgo.

The new version also adds three small enhancements to the language including conversions from slice to array pointer, “unsafe.Add” and “unsafe.Slice.”

The package unsafe enhancements were added to simplify writing code that conforms to unsafe.Pointer’s safety rules, but the rules remain unchanged.

Go 1.17 is expected to be released in August 2021.

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Broadcom DX Application Performance Management, part of the AIOps Platform from Broadcom, delivers mobile-to mainframe observability for user behavior, performance analysis, and code-level diagnostics along with easy-to-use workflows and dashboard to understand the health of any multi-cloud app.  The solution provides advanced analytics based on time, text, topology, and training, so you can pinpoint and resolve performance issues quickly and ensure that every user transaction becomes a loyalty-building interaction.

RELATED CONTENT:
APM: Cutting through the noise
How does your solution help teams manage monitoring?

Akamai  provides application performance management as part of its Ion solution, which is a suite of intelligent performance optimizations and controls for delivering high-quality web iOS and Android app experiences. The solution continuously monitors real user behavior and adapts in real time to context, user behavior and connectivity changes. 

AppDynamics by Cisco is an APM provider that provides customers with information on user experience. Its Experience Journey Mapping feature tracks the application paths most common among users and evaluates performance, enabling customers to see how their users are interacting with their app. Companies can use AppDynamics to optimize customer journeys across devices and quickly identify any issues. 

Amazon CloudWatch is an application and infrastructure monitoring solution built for DevOps engineers, developers, SREs and IT managers. It provides data and actionable insights to monitor apps, respond to performance changes, optimize resource utilization, and get a unified view of operational health. 

Catchpoint is the enterprise-proven ally that empowers teams with the visibility and insight required to deliver on the digital experience demands of customers and employees. With its combined true synthetic, real user, network, and endpoint monitoring capabilities and the largest, most diverse global monitoring network in the industry, Catchpoint delivers in-depth, accurate, and full-stack performance insights. 

Datadog APM provides end-to-end distributed tracing at scale capabilities for front-end devices and databases. Users can monitor service dependencies, reduce latency, and eliminate errors for the best possible user experience. 

Dynatrace provides software intelligence to simplify enterprise cloud complexity and accelerate digital transformation. With AI and complete automation, our all in-one platform provides answers, not just data, about the performance of applications, the underlying infrastructure and the experience of all users.

InfluxData: APM can be performed using InfluxData’s platform InfluxDB. InfluxDB is a purpose-built time series database, real-time analytics engine and visualization pane. It is a central platform where all metrics, events, logs and tracing data can be integrated and centrally monitored.

Instana is a fully automatic APM solution that makes it easy to visualize and manage the performance of your business applications and services. The only APM solution built specifically for cloud-native microservice architectures, Instana leverages automation and AI to deliver immediate actionable information to DevOps. 

LaunchDarkly is a feature management platform that empowers all teams to safely deliver and control software through feature flags. By separating code deployments from feature releases, LaunchDarkly enables you to deploy faster, reduce risk, and iterate continuously. LaunchDarkly integrates with several observability and APM solutions such as AppDynamics, Datadog, Dynatrace, Honeycomb, New Relic, and SignalFX. These integrations help measure how each feature affects key service metrics such as response times and error rates.

Lightstep‘s mission is to deliver insights that put organizations back in control of their complex software applications. It provides an accurate, detailed snapshot of the entire software system at any point in time, enabling organizations to identify bottlenecks and resolve incidents rapidly.

Microsoft Azure Monitor provides full observability into applications, infrastructure and network. It’s application sights feature provides an APM service for developers and DevOps professionals to monitor live applications, detect performance anomalies, diagnose issues and understand what users are doing. 

New Relic One aims to go beyond traditional monitoring solutions by embracing observability. It provides users with a real-time view of operational data so they can respond faster, optimize better and build great modern software. It includes a telemetry data platform, full-stack observability, and applied intelligence.

Oracle provides a complete end to-end application performance management solution for custom and Oracle applications. Oracle Enterprise Manager is designed for both cloud and on-premises deployments; it isolates and diagnoses problems fast, and reduces downtime, providing end-to-end visibility through real user monitoring; log monitoring; synthetic transaction monitoring; business transaction management and business metrics.

OpsRamp is a modern IT operations management platform that allows enterprise IT teams and MSPs to “control the chaos” of digital infrastructure. OpsRamp does this through hybrid discovery and monitoring, event and incident management, remediation and automation, powered by AIOps. Users can detect and resolve incidents faster, understand resource dependencies and avoid costly performance issues that result in lost revenue and productivity.

OverOps captures code-level insight about application quality in real time to help DevOps teams deliver reliable software. Operating in any environment, OverOps employs both static and dynamic code analysis to collect unique data about every error and exception—both caught and uncaught — as well as performance slowdowns. 

Pepperdata is a leader in the APM space with proven products, operational experience, and deep expertise. It provides enterprises with predictable performance, empowered users, managed costs and managed growth for their big data investments, both on-premise and in the cloud.

Plumbr is a modern monitoring solution designed to be used in microservice-ready environments. Using Plumbr, engineering teams can govern microservice application quality by using data from web application performance monitoring. Plumbr unifies the data from infrastructure, applications, and clients to expose the experience of a user. This makes it possible to discover, verify, fix and prevent issues. 

Riverbed’s application performance solutions provide superior levels of visibility into cloud-native applications—from end users, to microservices, to containers, to infrastructure—to help you dramatically accelerate the application lifecycle from DevOps through production.

Sentry provides code-level observability that is essential for software teams to monitor application health. With Sentry’s error tracking and performance monitoring software, developers can see the most critical issues clearer, solve issues quicker, and learn continuously about their applications—from the front end to the back end. Sentry works across web, desktop, mobile and native platforms, and nearly every framework and language.

SmartBear: AlertSite’s global network of more than 340 monitoring nodes helps monitor availability and performance of applications and APIs, and find issues before they hit end consumers. The Web transaction recorder DejaClick helps record complex user transactions and turn them into monitors, without requiring any coding.

Splunk APM enables users to innovate faster in the cloud, improve user experience and future-proof applications. It features NoSample full-fidelity trace ingestion so developers never miss an anomaly, AI-driven analytics and directed troubleshooting, high cardinality exploration of traces, and an open standards approach. 

Stackify by Netreo’s APM solution Retrace gives developers straightforward insights into performance bottlenecks. It integrates code profiling, error tracking and application logs; troubleshoots problems and looks for ways to optimize code; and collects detailed snaptops of what code is doing and how long it takes. 

The post A guide to APM solutions appeared first on SD Times.

There are two main ways we help. For large companies that have multiple observability tools, multiple monitoring tools and multiple APM tools, which is basically a majority of the market out there, BigPanda comes in and unifies all of those fragmented domains and teams using those fragmented siloed products. The number one reason why companies choose us is because we are vendor agnostic, we are domain agnostic, we sit in the middle and unify all these APM tools and vendors. 

Secondly, we help with incident management — how you prevent and resolve outages. While APM and observability tools are fantastic at providing the deep, deep visibility businesses need, that forensic data doesn’t become important until later in the process. Teams need a smart detector to connect the dots and find probable causes or culprits, and then they can get into the forensics more.  

RELATED CONTENT:
APM: Cutting through the noise
A guide to APM solutions

When you have an outage or a massive incident that is crippling to your users or system, BigPanda connects all the dots, connects all the signals together and says here is the problem and here is what we think is causing it. BigPanda excels at that root probable cause, and then your APM experts can come in and dive deeper into the issue. BigPanda sits in the front for the detection problem, root cause identification, and the APM and observability tools can come in to surface the data and resolve the problem. 

Amy Feldman, head of AIOps product marketing, Broadcom:

Broadcom’s AIOps solution is based on open source, allowing it to be an open, agnostic platform, easily integrating different data sets such as metrics, logs, wire, performance, transactional and user experience.  A differentiator is that the solution looks at time, text, topology and training in order to get to the root cause of the performance problem. Our APM plugs into our AIOps platform for increased observability.

We analyze data based on those four spectrums — time, text, topology and training. There’s not one single approach that solves all problems; you have to look at it from different angles, and at all the pieces. And because the platform is open and agnostic, we can then incorporate all different kinds of data, which gives you that extra observability, because the more data that you have across the entire landscape, the better insights you can get out of it. 

There is business-related data, user experience data, APM data, Open Tracing information, network data, and third-party data as well. We treat this data as if it was a first-class citizen, so it becomes part of the topology, incorporated into the data models, and incorporated into the platform itself. So that gives you that greater visibility you need to be able to deliver business outcomes.

AIOps from Broadcom includes our full-stack monitoring capabilities — APM, user experience, networking infrastructure, along with AI and ML reducing alarm noise, providing root cause analysis tied with intelligent automation to resolve issues quickly and improve customer experience.  

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“Observability is becoming a bigger focus today, but APM just by design will continue to have a critical role to play in that. Think about observability holistically, but also understand that your applications, your user-face applications and your back-end applications are driving revenue,” said Mohan Kompella, vice president of product marketing at the IT ops event correlation and automation platform provider BigPanda.

Because of the complexity of modern applications that rely on outside services through APIs and comprise microservices running in cloud-native environments, simply monitoring applications in the traditional way doesn’t cover all the possible problems users of those applications might experience.

RELATED CONTENT:
How does your solution help teams manage monitoring?
A guide to APM solutions

“What’s important,” explained Amy Feldman, head of AIOps product marketing at Broadcom, “is to be able to take a look at data from various different aspects, to be able to look at it from the traditional bytecode instrumentation, which is going to give you that deep-level transactionability back into even legacy systems like mainframe or, TIBCO, or even an MQ message bus that a lot of enterprises still rely on.”

Further, as more applications are running in the cloud, Feldman said she’s seeing developers “starting to change the landscape” of what monitoring looks like, and they want to be able to have more control over what the output looks like. “So they’re relying more on logs and relying more on configuring it through APIs,” she said. “We want to be able to move from this [mindset of] ‘I’m just telling you what to collect from an industry and vendor perspective,’ to having the business be more in charge about what to collect. ‘This is the output, I want you to measure it, look at all the data and be able to assimilate that into that entire topological view.'”

APM, observability or AIOps? 

Kompella explained there’s a lot of confusion in the market today because as vendors add more and more monitoring capabilities into their solutions, APM is being blended into observability suites. Vendors are now offering “all-in-one” solutions that provide everything from APM to infrastructure, logging, browser and mobile capabilities. This is making it even harder for businesses to find a solution that works best for them because although vendors claim to provide everything you need to get a deep level of visibility, each tool addresses specific concerns.

“Every vendor has certain areas within observability they do exceedingly well and you have to be really clear about the problem you’re trying to solve before making a vendor selection. You don’t want to end up with a suite that claims to do everything, but only gives you mediocre results in the one area you really care about,” Kompella said. 

When looking to invest in a new observability tool, businesses and development teams need to ask themselves what the specific areas or technologies that they are interested in monitoring are and where they are located. Are they on-premises or are they in the cloud? “That is a good starting point because it helps you understand if you need an application monitoring tool that’s built for microservices monitoring and therefore in the cloud, or if you still have a large number of on-premise Java-based applications,” Kompella explained. 

Much of monitoring applications in the cloud is reliant upon the providers giving you the data you need. Feldman said cloud providers could give you information through an API, or deliver it through their monitoring tool. The APM solution has to be able to assimilate that information too. 

While Feldman said the cloud providers haven’t always provided all the data needed for monitoring, she believes they’re getting better at it. “There’s definitely an opportunity for improvement. And in a lot of areas, you do see APM vendors also provide their own way to instrument the cloud… being able to install an agent inside of the cloud service, to be able to give you additional metrics,” she said. “But we’re seeing, I think, a little bit more transparency than we had before in the past. And that’s because they have to be able to provide that level of service. And being able to have that trend, a little bit of transparency, helps to increase communications between the service and the provider.”

BigPanda’s Kompella said the overarching driver of monitoring is to not just “stick your finger in the wind” and decide to measure whichever way the wind blows. You really have to understand your systems to figure out what metrics are going to matter to you. One way to do that is by analyzing what is generating revenue. Kompella went on to explain that you have to look at where you’ve had outages or incidents in the last couple of months, how they’ve impacted your revenue and rating, and then that will lead you to the right type of APM or observability tools that can help you solve those problems. 

Additionally, businesses need to look at their services from the evolution of their technology stack. For instance, a majority of their applications may be on-premises today, but the company might have a vision to migrate everything to the cloud over the next three years. “You want to make sure that whatever investments you make in APM tools are able to provide you the deep visibility your team needs. You don’t want to end up with a legacy tool that solves your existing problems, but then starts to break down over the next few years,” said Kompella. “Technology leaders should judiciously analyze both what’s in the bag today versus what’s going to happen in the next few years, and then make a choice.”

Getting the big picture

Broadcom’s Feldman explained that a monitoring solution should give you perspective and context around what is happening, so having the traditional inside-out view of APM coupled with an outside-in perspective can aid in resolving issues when they arise. Such things as synthetic monitoring of network traffic, and real user monitoring of how applications are used can provide invaluable insight to an application’s performance. She also noted if the application is running in the cloud, you could use Open Tracing techniques to get things like service mesh information to understand what the user experience is for a particular cloud service.

Kompella added that log management and network performance monitoring (NPM) can help extend your monitoring capabilities. While APM tools are good at providing a deep dive of forensics or metrics, log traces help you go even deeper into what’s going on with your applications and services and help improve performance, he said.

Network performance monitoring is also extremely important because most large enterprises are working in very hybrid environments where some parts of their technology stacks live on-premises and in the private or public cloud. Additionally, applications tend to have a multi-cloud strategy and are distributed across multiple cloud providers. 

“Your technology stack is extremely fragmented and distributed across all these on-prem and cloud environments, which also means that understanding the performance of your network becomes super critical,” said Kompella. “You might have the most resilient applications or the best APM tools, but if you’re not closely understanding network traffic trends or understanding the potential security issues impacting your network, that will end up impacting your customer experience or revenue generating services.” 

What is to come? 

The reason monitoring strategies are becoming so important is because the pressure for digital transformation is just that much greater today. A recent report from management consulting company McKinsey & Company found the COVID-19 crisis has accelerated digital transformation efforts by seven years.

“During the pandemic, consumers have moved dramatically toward online channels, and companies and industries have responded in turn. The survey results confirm the rapid shift toward interacting with customers through digital channels. They also show that rates of adoption are years ahead of where they were when previous surveys were conducted,” the report stated. 

This means that the pressure to move or migrate to the cloud quickly is that much greater, according to Mohan Kompella, vice president of product marketing at BigPanda, and as a result APM solutions have to be built for the cloud. 

“Enterprises can no longer afford to look for APM tools or observability tools that just don’t work in a cloud-native environment,” he said. 

Kompella also sees more intelligent APM capabilities coming out to meet today’s needs to move to the cloud or digitally transform. He went on to explain that APM capabilities are becoming very commoditized, so the differences between vendors are getting smaller and smaller. “Getting deep visibility into your applications has been largely solved by now. Companies need something to make sense of this tsunami of APM and observability data,” he said. 

The focus is now shifting to bringing artificial intelligence and machine learning into these tools to make sense of all the data. “The better the AI or the machine learning is at generating these insights, the better it is at helping users understand how they’re generating these insights,” said Kompella.

“Every large company has similar problems, but when you start to dive in deeper, you realize that every company’s IT stack is set up a little bit differently. You absolutely need to be able to factor in that understanding of your unique topology in your unique ID stack into these machine learning models,” said Kompella.

The trouble with alerts

Alarms are a critical way to inform organizations of performance breakdowns. But alarm overload, and the number of false positives these systems kick off, has been a big pain point for those responsible for monitoring their application systems. 

Amy Feldman, head of AIOps product marketing at Broadcom, said this problem has existed since the beginning of monitoring. “This is a problem we’ve been trying to sell for at least 20 years, 20 plus years … we’ve always had a sea of alarms,” she said. “There have always been tickets where you’re not sure where the root cause is coming from. There’s been lengthy war rooms, where customers and IT shops spend hours trying to figure out where the problem is coming from.”

Feldman believes the industry is at a point now where sophisticated solutions using new algorithmic approaches to datasets have given organizations the capability to understand dependencies across an infrastructure network. Then, using causal pattern analysis, you understand the cause and effect of certain patterns that go on to be able to determine where your root cause is coming from. 

“I think we’re at a really exciting point now, in our industry, where those challenges that we’ve always seen for the last 20 years, are something that we truly can accomplish today,” she said. “We can reduce the noise inside of the Event Stream to be able to show what really has the biggest impact on your business and your end users. We’re able to correlate the data to be able to recognize and understand patterns. ‘I’ve seen this before, therefore, this problem is a recurring problem, this is how you fix the problem.'”

AI and ML are key, Feldman said. “I think APM was probably one of the first industries to kind of adopt that. But now we’re seeing that evolution of where it’s taking off across multiple data sets, whether that’s the cloud observability, data sets, networking, data sets, APM data sets, even, mainframe and queuing type information, all of that now is getting normalized in and then used your experience too. So all the information now is coming together is giving us a great opportunity.

The post APM: Cutting through the noise appeared first on SD Times.

Bugsnag offers full-stack stability and error monitoring technology that prioritizes application stability.

“An accelerated shift to DevOps, among other existing digital transformation initiatives, has really taken off over the past year,” said Vineeta Puranik, the senior vice president of engineering at SmartBear. “Those shifts require testing to be performed earlier and by developers alongside testers. Bugsnag empowers development teams to make data-driven decisions around app quality, and the solution’s ease of use and seamless integrations make it a perfect fit for the SmartBear portfolio.”

DevArt offers support for OAuth 2.0 Authentication and SOQL Queries
DevArt announced that it now supports the common authorization standard OAuth 2.0, which requires that users only log in once to authorize a driver to access data and generate a refresh token.

The authorization standard is now supported in the ODBC drivers for Dynamics 365 and Salesforce. 

Also, Salesforce Object Query Language (SOQL) syntax is now available in ODBC driver for Salesforce to query Salesforce data. 

Mendix 9 released
Mendix 9 extends the capabilities of low code to data integration, intelligent workflows, mobile development, AI and more. 

The new Workflow Editor extends the capabilities of low code to process automation with which developers can access any data source, incorporate AI services and create interfaces. 

Also, the new Mendix Data Hub enables citizen and professional developers to use a curated catalog that enables IT to control data access and use.

Additional details are available here.

Uno Platform 3.7 released
Uno Platform 3.7 expands support for the newest WinUI, Project Reunion and Linux.

WinUI support was extended with support for XAML Behaviors in WinUI 3, which enables developers to add common and reusable interactivity to Windows applications with minimal code. 

SwipeControl was ported to Uno Platform to enhance UX and GTK/Linux TextBox support was also added. 

Sysdig raises $188M for modern cloud apps 
Sysdig announced $188 million in a Series F funding round, bringing its total valuation to $1.19 billion. 

Sysdig’s Secure DevOps Platform provides security and visibility to run containers, Kubernetes and apps on the cloud. 

The company also created the open-source projects sysdig and Falco to offers users deep visibility for container and cloud threat detection and incident response. 

The post SD Times news digest: SmartBear acquires Bugsnag, DevArt offers support for OAuth 2.0 Authentication and SOQL Queries, Mendix 9 appeared first on SD Times.

“Right now there’s a lot of buzz around observability, observability tools, but it’s not just the tool,” said Mehdi Daoudi, CEO of digital experience monitoring platform Catchpoint. “That’s the key message. It’s really about how can we combine all of these data streams to try to paint a picture.”

RELATED CONTENT:
The Foundations of Enterprise Observability
Observability: It’s all about the data

If you go back to where observability came from — like many other processes, it originated at Google — its original definition was about measuring “how well internal states of a system can be inferred from knowledge of its external outputs,” said Daoudi. 

Daoudi shared an example of observability in action where one of Catchpoint’s customers was seeing a trend where customers complained a lot on Mondays and Tuesdays, but not on Sundays. The server load was the same, but the services were slower. Through observability, the company was able to determine that backup processes that only run on weekdays were the culprit and were impacting performance. 

“Observability is about triangulation,” said Daoudi. “It’s about being able to answer a very, very complex question, very, very quickly. There is a problem – where is the problem? The reason why this is important is because things have gotten a lot more complex. You’re not dealing with one server anymore, you’re dealing with hundreds of thousands of servers, cloud, CDNs, a lot of moving parts where each one of them can break. And so not having observability into the state of those systems, that makes your triangulation efforts a lot harder, and therefore longer, and therefore has an impact on the end users and your brand and revenue, etc.”

This is why Daoudi firmly believes that observability isn’t just a set of tools. He sees it as a way of working as a company, being aligned, and being able to have a common way to collect data that is needed to answer questions. 

The industry has standardardized on OpenTelemetry as the common way of collecting telemetry data. OpenTelemetry is an open source tool used for gathering metrics, logs, and traces — often referred to as the three pillars of observability. 

The three pillars are often referenced in the industry when talking about observability, but Ben Sigelman, CEO and co-founder of monitoring company Lightstep, believes that observability needs to go beyond metrics, logs, and traces. He compared the three pillars to Steve Jobs announcing the first iPhone back in 2007. Jobs started off the presentation by announcing a widescreen iPod with touch controls, a “revolutionary” mobile phone, and a breakthrough internet communications device, making it seem as though they were three separate devices. 

“These are not three separate devices,” Jobs went on to clarify. “This is one device, and we are calling it iPhone.”  Sigelman said the same is true of telemetry. Metrics, logs, and traces shouldn’t be known as the three pillars because you get all three at once and it’s one thing: telemetry.

Michael Fisher, group product manager at AIOps company OpsRamp, broke observability data down further into two signals: symptomatic signals and causal signals. Symptomatic signals are what an end user is experiencing, such as page latency or a 500 Internal Server Error on a website. Causal signals are what cause those symptomatic signals. Examples include CPU, network, and storage metrics, and “things that may be an issue, but you’re not sure because they’re not being tied to any symptom that an end user might be facing.” 

Monitoring tools tend to focus mostly on the causal signals, Fisher explained, but he recommends starting with symptomatic signals and working towards causal signals, with the end state being a unit of the two. 

“When something is going wrong [the developer] can search that log, they can search that trace and they can tie it back to the piece of code that’s having an issue,” said Fisher. “The operations team, they may just see the causal symptoms, or maybe there is no causal symptom. Maybe the application is running fine but users are still complaining. Tying those two together is kind of a key part of this shift towards observability. And that’s why I talk about observability as a development principle because I think starting with the symptomatic signals with the people who actually know is a huge paradigm shift for me because I think some of the people you talk to or ITOps teams you talk to is that monitoring is their wheelhouse, whereas many modern shops, OpsRamp included, much more monitoring actually happens on the development team side now.”

Providing good end user experience is the ultimate goal of observability. With monitoring, you might only be focusing on those causal signals, which might mean you miss out on important symptomatic signals where the end user is experiencing some sort of service degradation or trouble accessing your application. 

“When I talk about using observability to drive end-user outcomes, I’m really talking about focusing on observing the things that would impact end users and taking action on them before they do because traditionally this focus on monitoring has been at a much lower level, layer 3, I care about my network, I care about my switches,” said Fisher. “I’ve talked to customers where that’s all they care about, which is fine but you start to realize those things really matter less once you move up the stack and you have a webpage or you have a SaaS application. The end user will never tell you that their CPU is high, but they will tell you that your webpage is taking 10 seconds to load and they couldn’t use your tool. If an end user can’t use your tool who gives a damn about anything else?”

It’s important that observability not just stay in the hands of developers. In fact, Bernd Greifeneder, CTO of monitoring company Dynatrace, believes that if developers just do observability on their own, then it’s nothing more than a debugging tool. “The reason then for DevOps and SREs needs to come into play is to help with a more consistent approach because these days multiple teams create different microservices that are interconnected and have to interplay. This is sort of a complexity challenge and also a scale challenge that needs to be solved. This is where an SRE and Ops team have to help with standing up proper observability tooling or monitoring if you will, but making sure that all the observability data comes together in a holistic view,” he said. 

SRE and Ops teams can help make sure that the observability data that the developers are collecting has the proper analytics on top of it. This will enable them to gain insights from observability data and use those insights to drive automation and further investments into observability. “IT automation means higher availability, it means automatic remediation when services fail, and ultimately means better experiences for customers,” Greifeneder said. 

When looking into the tools to put on top of your observability data to do those analytics, Tyler McMullen, CTO of edge cloud platform Fastly recommends constantly experimenting to see what works for your team. He explained that often these observability vendors charge a lot of money, and teams might fall into the trap of buying a solution, putting too much observability data into it, and being shocked when they’re charged a lot of money to do so. 

“Are the pieces of information that we’re plugging into our observability, are they actually working for us? If they’re not working for us, we definitely shouldn’t have them in there,” said McMullen. “On the other hand, you only really find out whether or not something is useful after it becomes useful. Figuring out what you need in advance is I think, one of the biggest problems with this thing. You don’t want to put too much in. On the other hand, if you put too little in you don’t know whether or not it is useful.” As a result, your team will need to do lots of experimenting to discover the right process and the right balance. 

Daoudi added that it’s also important to answer the question of why you’re doing observability before looking into products. “Like every new thing that when a company goes and decides to implement something, you start with why? Why do you need to implement observability? Why do you need to implement SREs? Why do you need to implement an HR system? If you don’t define the ‘why’ then what typically happens is first it’s a huge distraction to your company and also a lot of resources being wasted and then the end result might not be what you’re looking for,” he said.  

And of course, it’s important to remember that observability is more of a process, so looking for a tool that will do observability for you won’t work. The tooling is really about analytics on the observability data you’ve gathered. 

“I really don’t think observability is a tool,” said Daoudi. “If there was such a thing as go to Best Buy, aisle 5, or Target, or Walmart and buy an observability tool for like $5 million, it ain’t going to work because if your company is not functioning and aligned, and your processes and everything isn’t aligned around what observability is supposed to do, then you’re just going to have shelfware in your company.”

The post Observability: A process change, not a set of tools appeared first on SD Times.

“Competitive companies don’t want to simply reduce incidents and keep their services running, they want to do so while simultaneously releasing exciting products that customers will love. Traditionally, this has been a hard balance to strike—the faster and more innovative teams try to be, the more likely they are to break existing services or overlook factors that impact the customer experience in production,” said Doug Peete, chief product officer at xMatters. “Without the proper tools to support their desired velocity, development and operations teams are hamstrung dealing with technical issues that divert time and resources from core product initiatives. We’re excited to launch new xMatters features that automate and simplify the toolchains our customers use to manage the growing network of microservices underpinning every modern business, while empowering effective cross-team communication and collaboration.”

SolarWinds unveils new APM Integrated Experience
The new APM Integrated Experience provides consolidated access to metrics, traces, logs and user experience.

“As the personas using APM continue to expand to include developers, support teams, product managers, infrastructure teams, and more, there is a need for an APM solution that caters to these broader audiences and brings together all the data necessary to understand and validate application performance,” said Denny LeCompte, senior vice president, products of SolarWinds. “Through the APM Integrated Experience, we are maximizing customers’ observability and ease of use. This convergence reflects our commitments to making tech pros’ lives easier and to broadening APM access for tech pros everywhere.”

Key features include a high-level view of changes and issues, dashboards, code-level APM data, advanced searching capabilities, ability to explore and alert on metrics, real-user monitoring and synthetic monitoring. 

Atlassian acquires visualization and analytics solution provider Chartio
The new acquisition will help Atlassian expand data visualization capabilities across all its products. 

“Atlassian products are home to a treasure trove of data, and our goal is to unleash the power of this data so our customers can go beyond out-of-the-box reports and truly customize analytics to meet the needs of their organization. Chartio is an important component of this vision as it will serve as our new analytics and data visualization engine across our products, starting with the Jira family,” Zoe Ghani, head of product at Atlassian, wrote in a post. 

Google Cloud and MongoDB on application modernization
The two companies are expanding their partnership to provide deeper integration between Google Cloud products and MongoDB’s cloud database. According to the companies, this will help accelerate application modernization.

“With this expanded partnership, MongoDB is enabling developers to integrate Atlas with Google Cloud products, including Pub/Sub, BigQuery, Dataproc, Dataflow, Cloud Run, App Engine, Cloud Functions, Google Kubernetes Engine (GKE), and Tensorflow. Additionally, Google Cloud’s mainframe modernization solutions now support MongoDB Atlas and help customers convert legacy COBOL code on mainframes into modern Java-based applications built on MongoDB. Together, G4 and MongoDB Atlas accelerate the modernization and migration process for organizations moving their business-critical workloads to the cloud,” according to the companies’ announcement. 

The post SD Times news digest: xMatters’ data-driven DevOps approach to incident resolution, SolarWinds APM integrated experience, and Atlassian acquires Chartio appeared first on SD Times.