Operations | Monitoring | ITSM | DevOps | Cloud

Product-led growth (PLG) is on the rise, a discipline that relies on the product itself to drive user acquisition, expansion, conversion and retention. Today, 60% of Cloud 100 companies embrace a PLG strategy, because it’s an efficient method of growth with low customer acquisition costs. Plus, cloud-native companies have the unique opportunity to collect more data that shows exactly how customers are using their products, and where potential friction occurs.

There are several reasons you may want to route to Amazon S3 destinations, including routing to object storage for archival, routing to S3 buckets to utilize Cribl.Cloud’s Search feature, and archiving data that can be replayed later. When setting up Amazon S3 destinations in Cribl, there are three authentication methods: Auto, Manual, and Secret. Using the Auto authentication method paired with Assume Role is the most secure way to connect Amazon S3 to Cribl.

Businesses are generating vast amounts of data from various sources, including applications, servers, and networks. As the volume and complexity of this data continue to grow, it becomes increasingly challenging to manage and analyze it effectively. Centralized logging is a powerful solution to this problem, providing a single, unified location for collecting, storing, and analyzing log data from across an organization’s IT infrastructure.

Logs provide valuable information for troubleshooting application performance issues. But as your application scales and generates more logs, sifting through them becomes more difficult. Your logs may not provide enough context or human-readable data for understanding and resolving an issue, or you may need more information to help you interpret the IDs or error codes that application services log by default.

At observIQ, we use the OTel collector to collect host/container-level metrics and logs from our systems. But to get more detailed monitoring of our applications (APM), we use the OTel SDK and instrumentation libraries. This post aims to provide a quick start to setting up tracing exporting to a local Jaeger instance.

Log data is big data! But that’s not why it’s such a big deal. Log data can be really useful if you know what to do with it — which is where log analysis and analytics comes in. Let’s take a look at this valuable activity, starting with what log data can tell us and moving into how we can use analytics to inform business practices. (This article was written in collaboration with Muhammad Raza.)

At Cribl, we understand precisely what challenges our customers face when running complex searches, and the importance of getting exactly what they need with their queries. Cribl Search’s latest feature, Operator Preview, allows data analysts to test search operators without committing to a full search. It saves time, reduces costs, and streamlines your everyday data analysis.

The explosive growth of interconnected data across distributed systems has disrupted traditional development, DevOps, and ITOps practices and forced many organizations to rethink their cloud strategies. Higher-velocity feature development and more responsive support requests involve developers throughout the delivery cycle and require them to monitor and observe application behavior before releasing it to production.

Logging in Kubernetes can help you track the health of your cluster and its applications. Logs can be used to identify and debug any issues that occur. Logging can also be used to gain insights into application and system performance. Moreover, collecting and analyzing application and cluster logs can help identify bottlenecks and optimize your deployment for better performance.