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Tag: data fabric

  • Apica Acquires LOGIQ.AI to Unify Observability

    Apica Acquires LOGIQ.AI to Unify Observability

    Apica, a provider of a synthetic monitoring and observability platform, has just raised an additional $10 million in funding and has agreed to acquire LOGIQ.AI. LOGIQ.AI is a provider of a data fabric infused with machine learning algorithms.

    Jason Haworth, chief product officer for Apica, said the data fabric developed by LOGIQ.AI will be incorporated into the Apica Ascent platform in the third quarter. The LOGIQ.AI addition will apply artificial intelligence (AI) in a way that both optimizes and enriches data collected by the company’s observability platform.

    At the core of the Apica Ascent platform is an indexing engine built on top of a Kubernetes platform that aggregates data such as logs, traces and network packets from multiple sources. As part of that process, the platform reduces storage costs by trimming excess data that can be stored in a data lake it provides or a third-party data lake a customer prefers.

    At the same time, Apica also enables DevOps teams to both normalize data collected from multiple sources and enrich it to add additional context, said Haworth.

    The overall goal is to provide a unified view of all IT data to accelerate faster root cause analysis and reduce the total cost of collecting the data required, he added.

    Apica is making a case for an observability platform that goes beyond collecting data from DevOps platforms. As IT environments become more complex, IT teams need to be able to correlate data collected from a wide range of data sources to accurately determine the root cause of an issue, said Haworth.

    The challenge is managing the massive amount of data that needs to be continuously collected, stored and analyzed to achieve that goal, he added. The acquisition of LOGIQ.AI will enable Apica to embed a data fabric that employs machine learning algorithms and other data science techniques to make it feasible to observe complex IT environments at scale, noted Haworth.

    It’s still early days as far as observability is concerned, but it’s clear that IT teams need to move beyond monitoring a pre-determined set of metrics to manage complex and dynamic application environments. The rate of change to application environments as updates are made requires an ability to query data to determine the root cause of issues that could be caused by any number of dependencies.

    The issue is that even when IT teams have access to an observability platform, they may not have the knowledge and expertise required to craft the queries needed to determine the root cause of an issue. Machine learning algorithms will play a major role in enabling IT teams to proactively discover issues that they can investigate using the suggested queries.

    It’s only a matter of time before most IT organizations unify observability data within a single platform. That will make it simpler for them to collaborate across teams and should result in fewer disruptions as IT issues are discovered more quickly. After all, the best kind of IT incident is the one that never happened or that was too trivial for anyone outside of IT to even notice.

  • Data: The Fabric of Developers’ Lives

    Data: The Fabric of Developers’ Lives

    We’re living in the golden age of application development. Developers can tap into more resources and enjoy greater flexibility than ever before. Multi-cloud environments offer them the freedom to pick and choose from a wide range of services based on the strengths and offerings of different cloud providers. They have unparalleled opportunities to achieve their development goals.

    In this environment, developers must build applications to work across different hosted providers, each with their own unique administrators and requirements. The easiest way to deal with this is to forget about storage, at least in the traditional sense. Instead, developers and the organizations they work for should deploy an underlying data fabric that supports everything they’re trying to do, and every cloud provider they need to do it with. 

    What Is a Data Fabric?

    Put simply, a data fabric is an underlying, cloud-agnostic infrastructure that can enable developers to more easily port data between different clouds. An ideal data fabric is highly scalable—supporting an organization’s big data needs—and reliable. 

    For a visual representation, think of a big Thanksgiving when you invite a bunch of different people over and you need to pull two dining tables of different shapes and sizes together. You spread a tablecloth over the two tables to make them become one. That’s kind of what a data fabric achieves; it brings two different things together to create a single platform.

    Why Is this Important?

    Storage-as-a-Service—we hardly knew about it. Thanks in large part to containers, which offer exceptional scalability, simplicity and high availability, the speed of application development has increased dramatically. Developers need to be able to quickly provision their own data, in just the right amounts, to match that velocity. And, like containers, that data needs to be portable.

    Provisioning quickly means no more going through storage administrators to get the services they need, which can be a cumbersome and time-consuming process. Solutions like Kubernetes’ on-demand clusters enable developers to procure the data they need when they need it.

    The abstraction layer provided by a data fabric can empower developers even further. They can write their own APIs, provision data services as needed and move that data between clouds with ease. 

    This is particularly important when dealing with cloud providers that offer different services. Sometimes a developer may need a service that exists in one cloud but not another. It’s critical to have an underlying storage infrastructure that enables applications and their data to be transferred as needs require.

    What Does this Mean for the Role of Developers?

    Today, developers must be IT versatilists and wear many hats. They have to manage storage, understand their businesses’ goals—and, of course, they have to develop. That’s a lot to juggle, so it’s understandable why developers would want something that helps simplify things.

    Indeed, having a standardized data fabric can make all of this a bit easier. Developers don’t have to bother their storage administrators with requests; they can quite easily provision storage themselves.

    Everyone wins in this scenario. Developers save time, become more empowered and have the freedom to create applications and datasets and more easily apply them to different clouds. Storage administrators can focus less on provisioning and more on other value-added projects. Organizations as a whole can also benefit, since developers will be able to create applications more quickly, allowing companies to bring services to market at a faster pace.

    Speaking of the Bottom Line — What Is It?

    Deploying a standard infrastructure for data services is critical not just to developers but to enterprises themselves. It supports organizations’ initiatives for scalability, flexibility and agility—all of the reasons that companies are doing containerized application development in the first place. For developers working in multi-cloud or hybrid cloud environments, it makes perfect sense.