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Tag: observability

  • Sumo Logic Eliminates Ingest Fees for Observability Log Data

    Sumo Logic Eliminates Ingest Fees for Observability Log Data

    Sumo Logic this week announced it will no longer charge a fee for ingesting log data into its observability platform to encourage DevOps teams to apply analytics more deeply.

    Michael Cucchi, vice president of product marketing for Sumo Logic, said the Sumo Logic Flex Licensing plan eliminates what amounts to a tax for using an observability platform. Flex Licensing is being made available immediately to new customers and will be offered to existing Sumo Logic customers later this year. There are no hidden monthly charges, feature restrictions, performance tradeoffs or user limitations being applied under the plan.

    The licensing plan only applies to log data, but DevOps teams will be able to ingest all structured, semi-structured and unstructured data for a lower cost that Sumo Logic is working to store more efficiently, said Cucchi. The overall goal is to make available a disruptive approach to licensing that lowers the total cost of observability, he added.

    Storing log data is going to be increasingly critical as organizations begin to apply multiple types of artificial intelligence (AI) models to further automate IT operations, he added. Those AI models promise to make observability platforms more accessible because algorithms will be able to surface issues that need to be addressed. Historically, the value of an observability platform has been tied to how proficient a DevOps team became when using its query language.

    IT teams that have adopted observability platforms have been, to varying degrees, limiting the amount of data they collect and retain to keep storage costs from exploding. The challenge is that as DevOps teams deploy more microservices-based applications, the amount of log data generated has exploded. Determining the root cause of any issue involving those applications can be problematic if log data isn’t readily available. Log data is, after all, the atomic unit for observability, noted Cucchi.

    In general, IT environments are becoming too complex for humans to manage without the aid of observability platforms augmented by AI models, noted Cucchi. Each application environment is fairly unique, so it is pivotal for those AI models to be exposed to as much data as possible to ensure optimal results.

    It’s not clear whether observability platforms might one day replace the need for many of the monitoring tools that IT teams rely on today to track a set of pre-defined metrics. However, one thing that is certain is that observability platforms, at the very least, present an opportunity to rationalize some of those tools. The biggest challenge is finding ways to fund the acquisition of an observability platform in the first place, which then creates an opportunity to consolidate those tools later.

    Given the complexity of highly distributed computing platforms, it’s only a matter of time before most IT teams have some type of observability capability in place. Of course, observability has always been a core DevOps tenet. The trouble is that the tools being used to achieve it are legacy monitoring platforms that don’t provide the level of depth required to successfully manage modern application environments.

    Image source: Joshua Sortino via Unsplash.

  • SEC Introduces Enhanced Climate-Related Disclosure Rules

    SEC Introduces Enhanced Climate-Related Disclosure Rules

    The Securities and Exchange Commission (SEC) has adopted new rules to enhance and standardize climate-related disclosures by public companies and in public offerings. These rules aim to meet investor demands for consistent, comparable, and reliable information on the financial effects of climate-related risks on companies’ operations and their risk management strategies.

    The Importance of Transparency

    According to SEC Chair Gary Gensler, the initiative is based on the principle that investors should be able to make informed decisions based on companies’ complete and truthful disclosures. The regulations introduce comprehensive requirements for disclosure of significant climate-related risks and their efforts to address them.

    Key Aspects of the SEC Rules:

    • Mandatory disclosure of climate-related risks that significantly affect or are likely to affect a company’s strategy, operations, or financial condition.
    • Details on efforts to mitigate or adapt to climate-related risks, including financial expenditures.
    • Disclosure requirements for climate-related targets or goals and their impacts on the company.
    • Requirements for certain companies to report Scope 1 and Scope 2 emissions, with an accompanying assurance report. (Scope 1 emissions are direct emissions from owned or controlled sources, while Scope 2 emissions are indirect emissions from the generation of purchased electricity, steam, heating, and cooling consumed by the reporting company.)

    Phased Implementation and Review Process

    Set to become effective 60 days after publication in the Federal Register, the rules will be implemented in phases based on the filer status of registrants.

    Strategic Implications for DevOps and Cloud Computing

    The new SEC rules could catalyze a strategic pivot in DevOps and cloud computing towards more sustainable practices. Specifically, they may further incentivize the development of tools and methodologies for accurately measuring and managing Scope 1 and Scope 2 emissions. New roles or teams may be dedicated to environmental impact analysis within IT departments. These shifts would ensure sustainability becomes a core consideration in tech deployment strategies.

    Driving Innovation for Greener Technology

    Software solutions that automate the tracking of energy consumption and emissions data across cloud and on-premises environments may see increasing demand. Particularly as organizations seek actionable insights for reducing carbon footprints.

    This emphasis on detailed climate-related disclosures may also serve to drive innovation in cloud infrastructure design and management. For example, cloud service providers might introduce new features or services that enable customers to more easily assess and optimize the environmental impact of their cloud usage.

    This could lead to the adoption of more energy-efficient computing resources, such as serverless architectures. Dynamically allocating resources could be prioritized to reduce unnecessary energy consumption

    Advancing Eco-friendly Solutions with AI

    These new rules amplify the global lean into AI for faster and more effective solutions. Utilizing predictive analytics and machine learning, AI can accurately forecast computing demand and make changes in real-time. This ensures that energy consumption closely matches actual usage, minimizing waste and reducing both operational costs and environmental impact.

    Balancing AI’s Energy Demand

    The flip side, though, is the high energy demands of AI. The new SEC rules will likely place an increasing focus on creating and implementing AI algorithms that are both potent and energy-efficient.

    Look for greater focus on AI-driven cooling systems for data centers and the use “lightweight” AI models that require less data and computing power for training and

    operation. Such models aim to maintain high efficiency while drastically cutting down the energy needed for their training and inference phases. All striving towards the broader goal of making AI more environmentally friendly.

    Sustainability and Innovation: The Path Ahead

    With the implementation of the SEC’s new rules, the landscape of corporate reporting and technology development is poised for significant change, emphasizing the importance of sustainability and environmental considerations in business strategies and operational practices.

  • 7 API Observability Anti-Patterns to Avoid

    7 API Observability Anti-Patterns to Avoid

    Observability has become a buzzword, and the API world is slowly catching up. But be careful when looking for content about API observability- there are many outdated best practices and irrelevant pieces of content.

    I was recently invited as a guest to Stephen Townshend’s SlightReliability podcast, and his brilliant blog post on “Bad Observability” inspired me to start ranting about the topic.

    Here is a follow-up to our discussion: The observability anti-pattern to avoid when working with APIs. Thanks, Stephen, for the inspiration!

    Anti-Pattern: You Forgot Your Users

    It’s easy to lose sight of the fact that APIs are built for people to use. APIs are not just a technical interface; they are a bridge that connects digital experiences and enables people to accomplish tasks efficiently.

    There are two different kinds of users for your API:

    • The developers who are integrating your API into their system.
    • Your customers’ users, who will ultimately interact with the applications or services that rely on your API.

    If you can’t understand how they interact with your APIs, you’re missing half the picture.

    When developers integrate with your APIs, how long does it take them to make their first successful API call? What are typical errors slowing them down? Observability plays an essential role in surfacing insights to improve developer experience.

    Once your customers’ applications or services relying on your APIs are in production, can you track how your releases impacted their requests? Which version of your APIs are they using? What are the most popular endpoints? Are all customers getting the same performance? Same error rates?

    Anti-Pattern: Relying Only on API Monitoring

    API monitoring typically involves sending automated test requests to API endpoints and verifying the responses. It helps you keep track of the health of your APIs even when there is no customer activity at all, and can be used to report uptime.

    But API monitoring is nothing more than black box testing; you don’t see what is happening inside. You just know that your requests reach the API and receive responses, but you can only create and maintain tests for some possible use cases. You will miss something.

    Anti-Pattern: Using API Access Logs for Troubleshooting

    API access logs are records of requests and responses made to an API, capturing details such as the requestor’s IP address, HTTP method, requested endpoint, status codes and other relevant information for monitoring, debugging and security purposes.

    Using access logs to troubleshoot API issues is like searching for a needle in your microservices haystack. One of our users had a performance issue with their APIs; they spent days searching for the root cause using access logs. Is it the gateway? Does it need more CPU? Is it a configuration issue? They enabled distributed tracing and, within seconds, they could see what was happening: All the time was spent in the authentication server issuing JWT tokens.

    To really understand what is happening in your system, you need to be able to track a single user request from the API gateway to the upstream services and their dependencies for effective troubleshooting.

    As Martin Thwaites puts it: “Logs are a poor man’s observability tool.” You need distributed tracing.

    Anti-Pattern: Different Teams, Different Tools, Different Data

    Different teams have different needs. The tool that works for your DevOps team might not fit your product team best. By forcing everyone to use the same tool, you’re not promoting unity but inefficiency.

    However, while promoting tool diversity might be the right move for your organization, there should be some coordination and integration between different tools and data sources. Open standards like OpenTelemetry help ensure that information can be shared and leveraged across teams and tools.

    The DevOps and the product teams should rely on the same error rate when assessing the performance and reliability of the newest API product launched.

    Anti-Pattern: One Size Fits All

    Different API architecture styles have different needs when it comes to observability.

    REST APIs might be today’s default, but newer styles like GraphQL and gRPC are gaining popularity. Each of these styles has distinct characteristics and requirements regarding observability. Just think about GraphQL’s way of handling errors, which differs significantly from REST APIs. Web Socket and event-driven APIs have their own unique observability challenges, as well.

    Different API architectures have different needs, and your observability strategy needs to reflect that to improve the overall reliability of your application.

    Anti-Pattern: Observability for Production Only

    Why aren’t you using observability in pre-production?

    By using distributed tracing during the API development life cycles, developers can trace the flow of requests and responses across different services, providing a comprehensive view of how the API behaves under various scenarios.

    With observability, they can identify performance bottlenecks and even detect architectural problems like N+1 problems in GraphQL queries.

    For teams leveraging APIOps practices, observability data can be used as an additional validation before promoting an API to the production stage.

    Anti-Pattern: Starting the Trace at the API Gateway is Overrated

    Tutorials about observability usually start the instrumentation process at a microservice level. It is great to have detailed insights into your microservices. But a lot will happen at the API gateway level when dealing with APIs.

    You want to capture all your user transactions, including those that never reach your microservices because of rate-limiting rules, an authentication problem or a caching mechanism.

    Starting the trace at the API gateway gives you a clear entrance point and a complete picture of the journey of all your users. That’s why it’s important to use a modern API gateway with built-in support for OpenTelemetry.

    So there you have it: Seven API observability pitfalls to avoid. Keep your APIs observable and your users happy!

    To hear more about cloud-native topics, join the Cloud Native Computing Foundation, Techstrong Group and the entire cloud-native community in Paris, France at KubeCon+CloudNativeCon EU 2024 – March 19-22, 2024.

  • Logz.io Leverages AI to Identify Anomalies in Real-Time

    Logz.io Leverages AI to Identify Anomalies in Real-Time

    Logz.io this week revealed it has added a real-time anomaly detection capability to its observability platform that makes it simpler to correlate the impact IT events will have on business processes.

    This capability is based on artificial intelligence (AI) models trained using historical data collected via the Logz.io Open 360 platform.

    Logz.io CTO Asaf Yigal said this addition to the Open 360 platform enables DevOps teams to employ machine learning algorithms to identify anomalies in mission-critical processes that warrant further investigation. Armed with insights that go beyond simple IT infrastructure consumption metrics, it becomes possible to significantly reduce mean time to resolution, he added.

    Logz.io has been embedding AI models within its platform since last year. Still, this latest capability will enable organizations to further bridge a divide between IT and the business that has long plagued many organizations, noted Yigal.

    The ultimate goal is to make it simpler for DevOps teams to instantly identify any anomalous application behavior that represents a risk to the business, including cybersecurity events, he added.

    The AI capabilities that Logz.io has been steadily adding to its observability platforms are largely transparent to DevOps teams, said Yigal. Instead, Logz.io is embedding multiple types of AI models into its platform that can be invoked in much the same way any other capability is provided, he added. DevOps teams are not necessarily going to need to build their own models to take advantage of AI to observe application behavior at scale, noted Yigal.

    The goal is to surface those capabilities consistently at a level of cost organizations can afford, added Yigal.

    It’s not likely AI will replace the need for DevOps teams any time soon, said Yigal. After all, there is a reason AI models are referred to as co-pilots versus an automatic pilot, he noted.

    It’s not clear to what degree organizations today can correlate the impact an IT event is going to have on the business. But as organizations continue to realize how dependent they are on applications to drive digital processes, the need to bridge the historic divide between IT and the rest of the business has become more urgent. In fact, many of the product and brand champions that organizations now rely on to manage a business unit often have a background in DevOps.

    Of course, business professionals that are steeped in IT are still relatively rare but as advances in AI continue to make it simpler to understand how complex application environments function the number of users of an observability platform will naturally expand beyond IT. The challenge is presenting each stakeholder with a view into the IT environment that meets their specific requirements.

    It may take a yet before the divide between IT and the rest of the business is permanently bridged but with each passing year it gets narrower. The challenge and the opportunity now is making sure everyone on both sides of that divide understands the art of the possible today that only a few short years ago would have seemed impossible to achieve.

  • New Relic Streamlines Observability Data Management

    New Relic Streamlines Observability Data Management

    New Relic this week made available a live archives capability that provides instant access to historical logs for up to seven years and eliminates the need to rehydrate, reload, re-index or move data.

    At the same time, the company also announced its observability platform is now integrated with infrastructure and observability tools provided by Sentry Software to help IT teams reduce costs and collect additional sustainability metrics alongside logs, metrics and traces.

    Jemiah Sius, senior director of developer relations for New Relic, said overall, New Relic is moving to make it simple to analyze and store telemetry data regardless of when it was collected. For example, rather than requiring historical data to be accessed via a cold storage service in the cloud, the New Relic platform now makes that data available directly within the database that is at the core of its observability platform.

    That approach also provides the added benefit of not incurring ingress and egress fees for moving data in and out of cloud storage services, noted Sius.

    IT teams can simply create a New Relic Query Language (NRQL) to identify which logs to route to a live archive in as little as 30 seconds without having to add an additional log collector.

    The overall goal is to make it simpler to investigate incidents faster, given the urgency that is created whenever there is an outage or audit request that needs to be immediately addressed, noted Sius. That’s critical because DevOps teams are always for time, he added.

    In general, New Relic is reducing the overall toil associated that many DevOps teams encounter today when managing telemetry data. Rather than being confronted with a complex set of data engineering tasks, telemetry data from multiple applications and infrastructure platforms can automatically be collected, analyzed and stored.

    It’s still early days as far as the adoption of observability platforms is concerned, but there is already no shortage of options. The challenge is that in addition to logs, metrics and traces, IT teams are now starting to also track cost and energy consumption metrics. Observability platforms provide the means to centralize telemetry data that is only going to increase in volume as more applications are deployed. The challenge has been framing the queries needed to surface meaningful insights. Fortunately, with the rise of generative artificial intelligence (AI) tools that make it simpler to create queries, the overall level of skill required to invoke an observability platform is dropping. In effect, generative AI is making it possible to democratize DevOps best practices.

    Ultimately, existing DevOps teams should be able to manage highly distributed application environments without having to hire and retain a small army of additional software engineers. The issue is that, in many cases, the size of the overall code base is increasing rapidly as developers take advantage of various generative AI tools to help them write code faster. Hopefully, advances in AI will arrive soon enough to enable DevOps teams to cope with all that code before DevOps teams are inevitably overwhelmed.

  • Observe Inc. Adds Tool for Exploring Distributed Traces

    Observe Inc. Adds Tool for Exploring Distributed Traces

    Observe Inc. this week added a Trace Explorer tool to its namesake observability platform that makes it simpler to search, analyze and visualize billions of traces.

    Traces are being used with greater frequency to track how resources are being invoked across increasingly distributed computing environments. That capability provides DevOps teams with a level of insight into workflows that goes well beyond the logs and metrics that almost every IT team routinely collects.

    Observe Inc. CEO Jeremy Burton said the challenge now is providing IT teams with tools that make it simpler to affordably manage traces at scale to reduce mean time to remediation (MTTR). The Observe platform enables IT teams to store the traces they run in a data lake from Snowflake for 13 months versus other platforms that only store a trace for a few days, which then requires IT teams to rerun the same trace multiple times each time they launch an investigation.

    The overall goal is to encourage DevOps teams to launch more investigations to improve application performance by making traces readily available using low cost S3 storage from Amazon Web Services (AWS) that is accessed via the Snowflake data lake that makes it simpler to apply artificial intelligence (AI) to telemetry data.

    That approach makes it economically feasible for DevOps teams to not just resolve an incident but also provide the level of investigation required to accurately identify its root cause, said Burton. Because the cost of storing telemetry data is so high, DevOps teams that employ other observability platforms are not able to thoroughly investigate incidents because they lack access to telemetry data that their organization decided not to store because of cost concerns, he added.

    Most organizations are still mastering the nuances of observability platforms that enable them to query telemetry data that, in addition to being easier to collect using open source OpenTelemetry agent software, is also increasing in volume thanks to the rise of microservices-based applications. Each microservice now generates its own telemetry data that needs to be analyzed and stored. As microservices make application environments more complex, the need for tools to analyze all that telemetry data becomes more pronounced.

    As businesses become more dependent on software to operate, it’s now more than apparent that legacy approaches to monitoring IT metrics are no longer sufficient, said Burton.

    It’s not clear how quickly organizations are adopting observability platforms, but there is already no shortage of options. Given the current state of funding for startup companies in the IT industry, IT teams would be well-advised to research the level of funding the providers of these platforms have before deciding which vendor to rely on, noted Burton. Last year, Observe Inc. raised an additional $50 million in funding.

    Regardless of the observability platform selected, the one thing that is certain is that observability platforms infused with AI are changing the way IT is managed. The issue IT teams often encounter now is convincing the organization to fund the acquisition of these platforms at a time when budget resources in many organizations are already being stretched thin.

  • Cisco Extends Scope and Reach of Observability Platform

    Cisco Extends Scope and Reach of Observability Platform

    Cisco this week expanded the scope of its observability platform by adding predictive and generative artificial intelligence (AI) capabilities along with log analytics and a Core Web Vitals tool to make it simple to ensure web pages don’t lose their search engine ranking because of poor user experience.

    In addition, Cisco is now leveraging extended Berkeley Packet Filters (eBPF) in the kernel of Linux operating systems to provide visibility into Kubernetes workloads and a session replay capability for cloud-native applications. In addition, Cisco has allied with Komodor, a provider of a Kubernetes management platform, to gain additional visibility into these environments.

    Cisco also added digital experience monitoring (DEM) capabilities based on real user monitoring (RUM) and the full-stack observability it provides following its recent acquisition of Accedian, a provider of an analytics platform for monitoring end-user experiences.

    Finally, Cisco added a Data Security Posture Management (DSPM) Observability module to the company’s Business Risk Observability platform based on the same core platform.

    Announced at a Cisco Live EMEA event, these latest updates are part of an ongoing Cisco effort to extend the reach of its observability platform well beyond DevOps workflows.

    Gregg Ostrowski, executive CTO for Cisco AppDynamics, said as part of that effort, Cisco is making it simpler to aggregate telemetry data for the AI for IT operations (AIOps) capabilities it provides by pulling directly from platforms such as Cisco ThousandEyes and Cisco DNA Center as well as from third-party partners such as VMware, Zabbix and ServiceNow. Similarly, Cisco is now leveraging eBPF to collect data from Kubernetes environments.

    Ostrowski added that the overall goal is to reduce the amount of instrumentation effort that IT teams would have to undertake to instrument highly distributed IT environments.

    There will still be a need to instrument applications, but the collection of telemetry data from the underlying IT infrastructure environment is going to be increasingly automated, he noted. In the meantime, it’s become less expensive to instrument applications using open source OpenTelemetry agent software, added Ostrowski.

    In general, Cisco, in addition to enabling organizations to rationalize existing tools, is also making a case for improving collaboration by, for example, making use of natural language capabilities enabled by generative AI to launch queries and provide summarizations.

    At the same time, Cisco is partnering to add additional capabilities, such as the ability to monitor machine learning algorithms via an alliance with Aporia and AS/400 systems via an alliance with Perform IT.

    Ultimately, Cisco is betting that observability across the entire enterprise IT environment is going to become more unified as organizations increasingly look to correlate business processes with specific IT events, including indications of cybersecurity breaches. It’s not clear how far down that path enterprise IT organizations are just yet, but given the total cost of observing and monitoring IT environments today, there is more pressure than ever to find more cost-effective approaches to holistically managing IT. The challenge, as always, is unifying all the existing fiefdoms in IT organizations that have already embraced a wide variety of tools and platforms they are hesitant to give up.

  • Dynatrace Extends Data Reach and Scope of Observability Platform

    Dynatrace Extends Data Reach and Scope of Observability Platform

    At its Perform 2024 event today, Dynatrace unfurled a Dynatrace OpenPipeline that makes it possible to apply analytics to multiple types of data sources in real time.

    The company also unveiled a Data Observability offering that can be used to vet the quality and lineage of data being exposed to the Davis artificial intelligence (AI) engine at the core of the Dynatrace observability platform to reduce false positives that otherwise might generate an alert in addition to helping reduce the volume of data that might need to be stored.

    Finally, Dynatrace announced it is starting today extending the reach of its observability platform to the large language models (LLMs) that are used to create generative AI platforms. That capability will, for example, make it simpler to monitor the consumption of tokens that are used to provide access to these models.

    Steve Tack, senior vice president for product for Dynatrace, said Dynatrace OpenPipeline will enable organizations to streamline the collection of data in a way that will enable them to apply observability more broadly by applying stream processing algorithms to petabytes of data.

    Scheduled to be available in 90 days, the Dynatrace OpenPipeline capability enables IT teams to ingest and route observability, security, and business events data from any source and in any format at the point of ingestion, including unstructured data that is automatically converted into a usable format. That data can then be enriched to enable deeper analytics.

    It also provides IT teams with more control over which data they analyze, store or exclude from analytics, which in turn helps reduce the total cost of observability, noted Tack.

    Finally, it presents IT teams with the ability to apply more customizable security and privacy controls to how that data is employed, he added.

    Dynatrace is pursuing a multimodal approach to AI that spans predictive, causal and generative models. Collectively, that approach makes it simpler to identify the root cause of issues, identify anomalies that might disrupt services and streamline incident management by, for example, providing summarizations of events in a natural language format.

    It’s not clear how broadly observability will be applied beyond the data collected to manage DevOps workflows and other IT processes, but it’s clear there is a high correlation between IT events and business outcomes as organizations become more dependent on software. As AI becomes more pervasively employed, it should become simpler to apply analytics to a much wider range of types of data to surface the relationship between IT events and business processes. In effect, Dynatrace is making it simpler to apply best data engineering practices to collect, manage and analyze that data, noted Tack.

    In the meantime, DevOps teams should revisit how their pipelines are currently constructed to make it simpler to capture all relevant data. As critical as telemetry data collected from the DevOps platform is to ensure application development and delivery occurs as fast as possible, that data is only one factor in a larger equation. The challenge and the opportunity now is determining how best to apply AI to correlate it all.

  • Chronosphere Acquires Calyptia to Process Log Data More Efficiently

    Chronosphere Acquires Calyptia to Process Log Data More Efficiently

    Chronosphere today announced it has acquired Calyptia to add additional pipeline management capabilities to its observability platform.

    Calyptia created Fluent Bit and Fluentd, a set of open source log processors that are now being advanced under the auspices of the Cloud Native Computing Foundation (CNCF).

    Chronosphere CEO Martin Mao said these log processors will be embedded into the company’s namesake observability platform to enable the routing, transformation and optimization of log data at scale. That issue has become problematic because cloud-native applications based on microservices create exponentially more telemetry data than legacy monolithic applications. If it becomes too costly to analyze that data, then DevOps teams will begin to limit the number of applications they will observe, noted Mao.

    The Calyptia platform achieves that goal using filters at the data source to reduce the volume of data that might otherwise be shared by 30% or more.

    The shared data can then be analyzed in real time rather than waiting for it to be stored and indexed. In addition, DevOps teams can enrich that data in flight to include additional context.

    Calyptia cofounder Anurag Gupta added that the primary mission for the company has always been to make log data more accessible to developers as they build and deploy applications.

    The acquisition of Calyptia comes on the heels of a log management alliance between Chronosphere and CrowdStrike through which a log storage and virtualization capability, dubbed Logs powered by CrowdStrike, has been added to the observability platform. The overall goal is to simplify the management and analysis of logs via a single click of a graphical tool or within the context of a larger DevOps workflow based on a command line interface (CLI) without requiring data engineering expertise, said Mao.

    It’s not clear how fully DevOps teams have embraced observability, but it’s apparent the legacy monitoring tools that collect a set of predefined metrics are not able to keep pace with the complexity of cloud-native application environments. An observability platform makes it simpler to query log data and discover the root cause of an issue—hopefully before a catastrophic event occurs. The challenge, of course, is providing that capability without incurring massive increased storage costs for log data.

    Despite those concerns, the need for observability is becoming a more pressing issue with each passing day. Organizations have never been more dependent on applications to drive revenue. A degradation in performance is usually an early indicator of a disruption that often has a direct impact on revenue and profitability.

    There is, of course, already no shortage of observability platforms. The issue most DevOps teams encounter is finding the funding needed to employ one at a time when organizations have become much more sensitive to IT costs. There may be an opportunity to rationalize some monitoring tools to help pay for the cost of a more modern observability platform, but regardless of approach, an ounce of prevention is always going to be worth a pound of proverbial cure.