Cribl is a customer first company. Building high value, secure-by-design software for security and IT teams has been by far the most gratifying experience of my professional career. As a security professional that deeply believes in Cribl’s product and mission, I share the excitement of changing forever how our customers operate and enabling them to protect their organizations; working at Cribl has been my greatest calling.

Logging in a Python application is straightforward. When you have good logs, you have better visibility into application health. You can monitor performance and track user activity. You’re better equipped to debug errors. Life is good. The challenges come when your application grows more complex. Perhaps your Python code is part of a broader application, or you have services distributed across multiple machines or clouds.

Being a Managed Security Service Provider (MSSP) or delivering a Managed Detection and Response (MDR) service is hard. You’re doing the jobs that are so hard that large swaths of organizations turn to you to handle those complex jobs for them. MSSP/MDR tech stacks are dynamic and highly customized, allowing for competitive offerings at competitive prices.

Content delivery networks (CDNs) such as Amazon CloudFront generate a flood of log files. In today’s world where your customers are all around the globe, it's important to make sure that your websites’ application assets are as close to the users as possible.

Tool consolidation outcomes have driven some customers to drop Splunk and consolidate their log analytics use cases on Sumo Logic. Long-term Splunk customers with many dashboards, saved searches and monitors understandably want to retain a consistent experience for end users. As a result, a replacement strategy requires migration.

Load Balancing is the process of distributing network traffic among multiple server resources. The objective of load balancing is to optimize certain network operations. Ensuring that a workload is spread evenly among the computing resources, this “balanced load” improves application responsiveness and accommodates unexpected traffic spikes — all without compromising application performance. Let’s take a deeper look at this important networking function.

In most situations, you will have several sources of telemetry data that you want to send to multiple destinations, such as storage locations and observability tools. In turn, the data that you are sending needs to be optimized for its specific destination. If your data contains Personally Identifying Information (PII) for example, this data will need to be redacted or encrypted before reaching its destination.

The more customers I talk to, the more I see a trend toward wanting a low-cost vendor-agnostic data lake. Customers want the freedom to store their data long-term and typically look to object stores from AWS, Azure, and Google Cloud. To optimize for data access, users will partition their data into directories to optimize for use cases such as Cribl Replay and Cribl Search. Only relevant files will be accessed for rehydration or search by partitioning data.

The business of observability is all about data: what you’re observing in the data, how you’re visualizing it, what it indicates about the state of your environment, and how to address issues that may occur. Creating your own perspective for observability, and understanding what you’re seeing, can be difficult.

K-NN (k-nearest neighbors) is a widely used machine learning (ML) recommendation algorithm, it is used to locate nearby documents based on vector dimensions. The algorithm can be and has been applied to numerous different use cases including image recognition, fraud detection, image recognition, and ‘other songs you might like’ feature in a music application. KNN uses proximity to provide classifications and predictions regarding the grouping of an individual data point.