A complete full-stack observability platform combines data from multiple sources to gain holistic insights into system behavior. In fact, organizations without a full observability solution could find themselves facing departmental silos since teams with different data sources cannot share insights.
While a key step in observability is the ability to combine logging data with metrics, companies that have developers viewing logging data and their metrics elsewhere are not collaborating on their data in the way observability requires. The solution is an observability data pipeline that best fits your business needs.
What is an Observability Data Pipeline?
An observability data pipeline does two things—centralizes data from a number of different sources and provides teams with the right tools to turn data into business insights. Observability data pipelines are typically user-friendly so that you can easily set up and manage your data.
Observability data pipelines integrate data from a range of sources, including AWS, FluentD and Kubernetes. They have capabilities for searching and viewing log data and notifying engineers through email and even Slack.
An observability pipeline ingests logs, so they can be viewed in a log viewer. Pipelines can also analyze data in real-time, allowing engineers to quickly spot critical security issues before they affect critical systems.
There are a number of full-stack observability platforms, including Coralogix, Splunk, Elastic and even options like Azure Log Analytics and Google Cloud Logging. Coralogix, for example, is a popular observability data pipeline that can analyze data in real-time using Streama and use Kafka Connect to integrate data from a variety of sources and extract trends.
Business Benefits of Observability Data Pipelines
Using observability data pipelines has the potential to transform the way a business does DevOps. Let’s go through a few key benefits of this technology:
● Reducing Cost of Observability Data
Many organizations use systems like Cloudwatch to analyze logging data. However, these often charge per GB of logging data analyzed, raising the question of whether Cloudwatch is really cost-efficient. And it’s not just Cloudwatch. For many systems, logging costs can be increased due to slow query times, noisy logs and the wrong type of storage. Observability data pipelines help organizations manage high volumes of logging data and move away from expensive legacy platforms. This enables enterprises to increase their observability for a modest price.
● Reduced Onboarding, Increased Problem-Solving
Pipelines are straightforward to learn. Unlike options such as ELK, which can be quite specialized and difficult to maintain, observability data pipelines are easy to use and easy to set up. This reduces the time needed to onboard new engineers. Less time onboarding means more time solving problems and serving clients, which increases business revenue.
● Better Data Shaping and Merging
Data from different sources is often stored in multiple formats and structures. Because observability depends so much on well-integrated data, it’s vital to transform this data into standardized formats. Observability data pipelines use log parsing to store key features of logging data for search and analysis.
● Reducing MTTD/MTTR
Having an optimized, user-friendly observability data pipeline reduces downtime by helping engineers solve problems faster.
● Enabling DevSecOps With Observability
We’ve written about the rising prominence of DevSecOps, a paradigm that involves shifting security left in the development life cycle. Key to DevSecOps is the fruitful collaboration between DevOps and Security. This can only be achieved by high levels of observability. Observability data pipelines facilitate this with their ability to integrate data from multiple different sources and present it intuitively. This means security teams can apply their expertise to log analytics that would otherwise have remained siloed.
Wrapping Up
Observability, the ability to use logs, traces and metrics to “picture” the internal state of a system, is vital to any DevOps team. Observability data pipelines can help by integrating and presenting data from different sources. This mitigates the effects of organizational silos and allows DevOps teams to solve problems faster.
