With 85 of their customers listed among the Fortune 100 companies, Splunk is undoubtedly one the leading machine data platforms on the market. In addition to its core capability of consuming unstructured data, Splunk is one of the top SIEMs on the market. Splunk, however, costs a fortune to operate – and those costs will only increase as data volumes grow over the years. Due to these growing pains, technologies have emerged to control the increasing costs of using Splunk.

When it comes to handling and deriving insights from massive volumes of data, Splunk is a force to be reckoned with. Its ability to index, search, and analyze machine-generated data has made it an essential tool for organizations seeking actionable intelligence. However, as the volume and complexity of data continue to grow, optimizing the Splunk experience becomes increasingly important. This is where the power of telemetry pipelines, like Mezmo, comes into play.

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!