Operations | Monitoring | ITSM | DevOps | Cloud

With all the buzz around ChatGPT and the rapid mainstreaming of generative AI, 2024 was predicted to be the year of AI. While the market certainly talks a lot about AI this year, we’ve yet to see much of it in production environments. Events are a great chance for tech companies to showcase or announce new innovations to the market.

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.

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.

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.

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.

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.

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.