Telemetry data can provide businesses with valuable insights into how their applications and systems are performing. However, leveraging this data optimally can be a challenge due to data quality issues and limited resources. Our recent webinar, "Unlocking Business Performance with Telemetry Data", addresses this challenge.
Datadog is a powerful observability platform. However, unlocking it’s full potential while managing costs necessitates more than just utilizing its platform, no matter how powerful it may be. It requires a strategic approach to data management. Enter telemetry pipelines, a key to elevating your Datadog experience. Telemetry pipelines offer a toolkit to achieve the essential steps for maximizing the value of your observability investment. The Mezmo Telemetry Pipeline is a great example of such.
Telemetry pipelines are a modern approach to monitoring and analyzing systems that collect, process, and analyze data from different sources (like metrics, traces, and logs). They are designed to provide a comprehensive view of the system’s behavior and identify issues quickly. Data transformation is a key aspect of telemetry pipelines, as it allows for the modification and shaping of data in order to make it more useful for monitoring and analysis.
Observability has become a critical part of the digital economy and software engineering, enabling teams to monitor and troubleshoot their applications in real-time. Properly managing logs, metrics, traces, and events generated from your applications and infrastructure is critical for observability. A telemetry pipeline can help you gather data from different sources, process it, and turn it into meaningful insights.
As a Senior Product Manager at Mezmo, I understand the challenges businesses face in managing data complexity and the higher costs that come with it. The explosion of data in the digital age has made it difficult for IT operations teams to control this data and deliver it across teams to serve a range of use cases, from troubleshooting issues in development to responding quickly to security threats and beyond.
In today's digital landscape, organizations rely on software applications to meet the demands of their customers. To ensure the performance and reliability of these applications, observability pipelines play a crucial role. These pipelines gather, process, and analyze real-time data on software system behavior, helping organizations detect and solve issues before they become more significant problems. The result is a data-driven decision-making process that provides a competitive edge.
Today, businesses are generating more data than ever before. However, with this data explosion comes a new set of challenges, including increased complexity, higher costs, and difficulty extracting value. With this in mind, how can organizations effectively manage this data to extract value and solve the challenges of the modern data stack?
We are using data wrong. In today’s data-driven world, we have learned to store data. Our data storage capabilities have grown exponentially over the decades, and everyone can now store petabytes of data. Let’s all collectively pat ourselves on the back. We have won the war on storing data! Congratulations!
In data management, numerous roles rely on and regularly use telemetry data. The security engineer is one of these roles. Security engineers are the vigilant sentries, working diligently to identify and address vulnerabilities in the software applications and systems we use and enjoy today. Whether it’s by building an entirely new system or applying current best practices to enhance an existing one, security engineers ensure that your systems and data are always protected.
In today’s fast paced and constantly evolving digital landscape, observability has become a critical component of effective software development. Companies are relying more on and using machine and telemetry data to fix customer problems, refine software and applications, and enhance security. However, while more data has empowered teams with more insights, the value derived from that data isn’t keeping pace with this growth. So how can these teams derive more value from telemetry data?
