Observability data, and especially log data, is immensely valuable for modern business. Making the right decision—from monitoring the bits and bytes of application code to the actions in the security incident response center—requires the right people to generate insights from data as fast as possible.
Alongside the rise of machine data is a widespread cultural and operational change across enterprises to DevOps (and DevSecOps). It’s a working style that brings together different teams and shifts the development approach left, empowering developers with greater accountability over the operations and security of these applications. With a greater DevSecOps orientation, teams are accelerating with autonomy, aligned around innovation, and expected to have greater access to data.
LogDNA’s rapid growth illustrates just how valuable data is and our unique ability to remove the friction of how autonomous teams use it for application troubleshooting and debugging. With LogDNA’s logging platform and software solution firmly embedded in enterprise DevOps teams, our customers are turning to us to solve a deeper level of business and technology issues, including the need to:
Log data is the largest, and arguably most important, observability data. It underpins all applications and systems. Yet, despite the perceived value of all of this data and the hype around observability, the vast majority of observability data remains dark. It’s wasted and expensive.
What’s keeping this data in the dark? Our customers tell us that the scale, complexity, wide variety of data consumers, and runaway cost make it impossible to get value out of all of their machine data. A number of technical and organizational challenges are also holding them back, including:
As the amount of log data grows, the value of it hasn’t historically kept pace. We call this the machine data cost-curve problem. It’s the problem we intend to tackle head-on and solve.
The value of log data in addressing the multitude of enterprise needs—from developer productivity to cybersecurity—requires the ability to ingest data in a way that is agnostic to source; the ability to process and route that data, regardless of the destination; and the ability to store and analyze that data in a way that meets the requirements of each of its consumers.
LogDNA has always believed that optimizing the ingestion pipeline was essential for solving the industry need for true observability. Our innovations in this area form a powerful foundation to meet today’s critical scale, storage, and routing requirements.
Already, these platform strengths allow some of our customers to leverage data from LogDNA in new machine data pipeline use cases. For example:
You can join these companies to overcome machine data management headaches and unlock more value from your log data across the enterprise.
LogDNA Streaming lets enterprises ingest all of their log data to a single platform, and then route it for any enterprise use case. This new feature takes full advantage of LogDNA’s unparalleled ability to quickly ingest massive amounts of structured and unstructured data, normalize it, and have granular control over storage to control costs and meet compliance needs. It’s ideally suited for use cases in cybersecurity and enterprise-level application delivery where more data can deliver dramatic outcomes if it’s accessible in real time.
For too long, enterprises have had to make difficult choices around how to use all of their machine data while controlling skyrocketing costs. We will continue to build a comprehensive platform that enables anyone to ingest, process, route, analyze, and store all of their log data in a way that makes sense for them.
I’m excited to continue sharing the progress we are making in this space and looking forward to adding even more value that enables you to build capabilities on top of your data in motion.