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Tag: log analytics

  • LogDNA Extends Log Management Reach

    LogDNA Extends Log Management Reach

    LogDNA has extended the reach of its log management platform to add support for a Node.js platform from Vercel and a feature flagging tool from LaunchDarkly.

    In addition, LogDNA has expanded an existing alliance to IBM to add support for instance of Red Hat OpenShift running on VMware platforms hosted on the IBM Cloud, which enables IT teams to centralize log management using LogDNA and IBM Log Analysis.

    Chris Nguyen, chief strategy officer for LogDNA, said the goal is to continue to establish partnerships that will enable DevOps teams to manage larger swaths of the IT environment via a single pane of glass.

    Vercel has been gaining  traction as a provider of an open source reactive programming framework that is being employed to build more interactive front ends for web and mobile applications.

    LaunchDarkly is a feature management platform that serves more than 6 trillion feature flags a day spanning 14 million mobile devices and 3 million servers. The integration between LogDNA and LaunchDarkly enables developers to rapidly iterate on feature flags using logging data surfaced by LogDNA.

    Nguyen said that as IT environments continue to become more complex, more organizations are relying on log analytics to reduce the noise level being created by all the tools and platforms that make up a modern IT environment. The challenge IT organizations face is most of them can’t find or retain a site reliability engineer (SRE), so they need tools that enable developers to make better-informed decisions.

    LogDNA is making the case for a log management platform deployable in the cloud or on-premises, designed for DevOps teams as an alternative to existing log management platforms that were designed for IT administrators who rely on graphical tools. LogDNA instead provides developers access to log analytics that are accessed via command-line interface (CLI) or a user interface.

    It’s not clear whether or how much IT organizations may be willing to replace legacy log management platforms. However, as more organizations embrace best DevOps practices that include observability as a core tenet, LogDNA is betting those organizations will prefer an approach to log management aimed at developers rather than IT operations staff.

    As DevOps teams assume more responsibility for the management of IT on an end-to-end basis, a seismic shift in IT tooling is underway. Providers of legacy IT tools are quickly shifting toward making APIs and CLIs available in response to a range of startups that are making available tools designed from the ground up to appeal more to DevOps teams. It’s too early to say how this battle for control over observability will play out, but the number of providers of tools for DevOps teams has never been greater.

    In the meantime, many DevOps teams are still sorting out when best to employ log analytics, metrics and distributed tracing to optimize IT environments. The challenge these days isn’t so much being able to discover what’s occurring in an IT environment as much as it is making sense of it all.

  • Sumo Logic Expands Observability Platform Scope, Reach

    Sumo Logic Expands Observability Platform Scope, Reach

    Sumo Logic announced today it has extended the reach of its monitoring platform to provide deeper insights across application and infrastructure logs, metrics, traces and metadata in real-time.

    The latest additions to the company’s suite of software-as-a-service (SaaS) offerings include Sumo Logic AWS Observability Solution, which focuses exclusively on AWS services, and additions to the Sumo Logic Software Development Observability Solution that provide support for GitHub, Jenkins and PagerDuty alongside existing support for Jira, Bitbucket and OpsGenie. The Sumo Logic Software Development Observability Solution is based on a key performance indicator (KPI) methodology developed by the DevOps Research and Assessment (DORA) organization, an arm of Google.

    observabilityIn addition, Sumo Logic has launched a closed beta for a Distributed Transaction Tracing service based on the OpenTelemetry standard that will integrate logs, metrics and metadata. Finally, the company has added more tracing capabilities to its existing Microservices Observability Solution.

    Sumo Logic got its start by providing a cloud service for analyzing logs as an alternative to Splunk. Since then, the company has extended the scope of its services to address a wider range of observability requirements for DevOps teams.

    Ben Newton, director of field evangelism for Sumo Logic, said the company is trying to engage IT organizations at any level of maturity by providing access to a range of offerings from log analytics to observability tools for various platforms including Kubernetes. Those offerings now span the application life cycle from coding to grave, he said.

    Sumo Logic also differentiates a pricing model based on data tiers that enables IT teams to analyze logs, metrics and traces across a variety of use cases without monthly limits, peak provisioning or on-demand charges for data volume overages or exceeding custom metrics limits.

    In the wake of the economic downturn brought on by the COVID-19 pandemic, the rate at which DevOps tools have been shifting into the cloud has accelerated. With more DevOps teams working from home, cloud-based services are more flexible. At the same time, more organizations prefer to treat IT costs as an operating expense at a time when the overall economic outlook remains uncertain. The entire IT organization needs to be able to respond to rapidly changing business conditions in a more agile manner, noted Newton.

    Less clear is whether the economic downturn might also drive consolidation across what today is a spectrum of observability tools spanning log analytics to application performance management (APM) platforms used primarily to sample application traffic. It’s even less clear whether the rise of distributed tracing capabilities might reduce dependency on application sampling altogether.

    What is apparent is IT teams have never had more visibility into IT environments that increasingly are more complex. The challenge and opportunity now is determining how best to apply a wide range of observability tools to turn all that insight into actionable intelligence.

  • Scalyr Alters Log Analytics Economics

    Scalyr Alters Log Analytics Economics

    Scalyr today announced that its log analytics cloud service is now capable of ingesting over 200TBs of data per customer per day in real-time.

    Company CEO Christine Heckart said the goal is to make as much log data available at a price point that will continue to be less than $5 per GB at scale.

    Heckart said Scalyr expects to be able to continue to expand the amount of data it makes available to customers because its namesake platform is built on top of a columnar data store rather than an index engine. The goal of a project dubbed Sonic Boom is to eventually make a petabyte of data available to customers of the cloud service, she said.

    One of the paradoxes of log analytics is that while IT organizations are encouraged to use this data to proactively identify issues before they become a major problem, the cost of storing that data often results in IT organizations limiting the amount of log data they analyze. As a result, they often miss outlier events that could have major implications for the IT environment.

    As a cloud service that is compatible with the S3 storage interface defined by Amazon Web Service (AWS), the columnar store engine employed by Scalyr makes it easier to pass the economics of cloud storage back to the end customer, she said. IT costs in the wake of the COVID-19 pandemic are, of course, going to be a much bigger issue going forward. Almost every aspect of IT, including log analytics, is likely to be subject to a cost review in the week and months ahead as organizations adjust to an economy that has changed for the worse overnight.

    Heckart said the issue many organizations will face when it comes to logs is that data is the lifeblood of any digital process. Organizations may not need to store that data, forever, but analyzing log data to identify anomalies indicative of, for example, a cybersecurity breach is now almost a daily routine. As such, how much log data to make available where at what cost becomes a subject of debate.

    Of course, it may be less costly to store log data locally. However, someone on the IT staff will need to manage those logs. Scalyr and other providers of SaaS-based platforms for log analytics contend it’s more cost-effective to rely on a cloud service to manage log data that also is more accessible via shared analytics tools.

    Regardless of the amount of IT resources made available, the one thing that is certain is neither the amount of data nor the rate at which it needs to be analyzed will be slowing down anytime soon. In fact, many organizations are likely to accelerate their transition to digital processes that are ultimately both more flexible and cost-efficient than existing processes. It will be up to DevOps leaders to figure out how best to keep pace with that rate of change with whatever resources are at hand.

    — Mike Vizard

  • Logz.io Extends Scope of Log Analytics Service

    Logz.io Extends Scope of Log Analytics Service

    Log analytics is all the rage these days among IT and security professionals trying to discover the root cause of any performance issue or security breach. But there’s little consensus about how best to go about analyzing logs. Many IT organizations have made major investments in commercial platforms such as Splunk, while others prefer to rely on the less expensive, open-source tools Elasticsearch, Logstash and Kibana, collectively known as the ELK stack.

    As a provider of a cloud service based on the ELK stack, Logz.io has been providing organizations that prefer to rely on open-source software an alternative to deploying and maintaining their own ELK stack. Now Logz.io is extending that service to include Logz.io Application Insights, which employs machine learning algorithms to create a model of normal IT operations that can be used to more easily identify anomalies, and Logz.io Data Optimizer, which helps identify which logs are worth keeping long-term.

    Fresh off raising an additional $23 million in capital, Logz.io CEO Tomer Levy says his company is building up a pool of machine data to drive advanced analytics fueled by machine learning algorithms. Those insights then can be shared proactively with other Logz.io customers to head off production issues before they even occur. At present, Logz.io has more than 400 customers.

    Levy says the Logz.io Data Optimizer should enable IT organizations to substantially reduce their costs. Most providers of log management tools charge based on the amount of data collected. By helping organizations identify extraneous log data, the cost of implementing analytics of machine data drops considerably, Levy says.

    Logz.io Application Insights, meanwhile, provides a simple way to identify the last changes made to the environment that might have created a performance issue or introduced a security vulnerability.

    Levy says Logz.io, via application programming interfaces (APIs), can be integrated with multiple continuous integration/continuous development (CI/CD) platforms, including Jenkins. In fact, log analytics is now a fundamental element of any mature set of DevOps processes, he notes.

    Platforms for analyzing machine data is one of the fastest growing sectors of enterprise IT. New research from Zion Market Research forecasts that the IT operations analytics market is expected to grow on a 23.7 percent compound annual basis to reach $9.2 billion by 2020.

    Most DevOps issues only take a few minutes to resolve. But identifying the root cause of an issue can take weeks, sometimes even months, to identify. In more advanced IT organizations, centers of excellence around log analytics are now being created to identify not only potential IT and security problems, but also issues impacting, for example, the amount of revenue being generated via a specific application.

    It still may be a while before log analytics become pervasive across the enterprise. But every event in the IT environment generates a log, so the real challenge and opportunity is correlating all those log events into something that manifests itself as a truly actionable intelligence.

    — Mike Vizard