Observability instruments systems and applications, collects metrics and logs, and enables an understanding of system behavior. Observability goes beyond traditional monitoring approaches to explain system behavior over time and provide accurate operational insights. And, in alignment with the new direction of DevOps, observability examines the sequence of a problem through monitoring, correlating the system data, and automating with ML. Observability provides DevOps with end-to-end system visibility to quickly respond, fix, and prevent problems. Observability helps organizations:
Observability and monitoring are complementary solutions, meaning one does not replace the other. Effective monitoring almost always includes observability.
Monitoring collects metrics and logs that provide information on whether the system is working, and it tells you when something went wrong. Put another way, monitoring is building your systems to collect data, with the goal of knowing when something goes wrong and starting your response quickly.
Observability instruments your systems with tools to gather actionable data that provides not only the when of an error or issue, but—more importantly—the why. Observability typically shortens the duration and reduces the impact of incidents.
While today’s world of accelerated cloud and microservices adoption has greatly advanced innovation and helped organizations reduce time to market, it has also increased operational complexity resulting in increasingly ephemeral environments with unpredictable behavior. Because of constant and dynamic change, siloed solutions that don’t work across platforms, and the scale of the environments, simply monitoring isn’t enough. By instrumenting systems and applications, and collecting metrics and logs that enable the understanding of system behavior, observability allows you to:
Achieve big data observability in the cloud by adopting three key techniques.
Although many big data performance solutions implement AI and ML to automate, many DevOps teams are still manually tuning, and they are spending a lot of time on it. However, the volume, velocity, and variety of the data being collected is fundamentally unmanageable by humans. The scale—thousands of applications per day and a growth rate of dozens of nodes per year—is too large for manual efforts. Even the most experienced IT operations teams and capacity planners can’t manually tune every application and workflow with the required precision and speed.
After understanding the value and benefits of observability, the next is implementing it. Although many monitoring vendors claim to have full observability capabilities, they only offer a portion of the picture and not full observability. Pepperdata big data performance solutions provide you with the observability you need to optimize the performance of your big data deployment and improve collaboration.
Zeotap, a data enrichment company, deployed Pepperdata Application Spotlight for resource optimization and observability into their Spark jobs. They experienced immediate results—a 23.78% increase in big data application performance and over $31,000 savings in just over three months.