In today’s complex IT environments, logs are the unsung heroes of infrastructure management. They hold a wealth of information that can mean the difference between reactive firefighting and proactive performance tuning. Log analysis is a process in modern IT and security environments that involves collecting, processing, and interpreting log information generated by computer systems. These systems include the various applications and devices on a business network.

Datadog and Splunk are both leading tools for monitoring and observability. Each offers a range of features designed to help you understand and manage your data. Datadog provides tools for tracking application performance and analyzing logs in real-time. Splunk, meanwhile, is known for its powerful log analysis and search capabilities. In this post, we will compare Datadog and Splunk on important aspects like APM, log management, search capabilities, and more.

Organizations generate, collect, and store vast amounts of telemetry data. With this data comes the growing responsibility to ensure compliance with various regulations, from GDPR to HIPPA. Data compliance ensures data is handled, stored, and processed according to laws and standards protecting personal information. But what makes compliance regulations scary is that it’s ever-changing and rules vary across industries, making it complex to manage.

As organizations continue to become more reliant on distributed and hybrid workforces, the need for comprehensive data collection across every endpoint—servers, applications, desktops, and laptops—has never been more critical. But let’s be real: agents can be a total headache. That’s where Cribl Edge comes in, now with support for desktops and laptops (in preview)!

For over a decade, we’ve worked closely with AWS to help our joint customers ensure the health and security of their mission-critical applications. That’s why we’re so excited to have recently renewed our Strategic Collaboration Agreement (SCA) with AWS and to announce five new AWS competencies across multiple industries.

You’ve likely noticed how some pizza places promise delivery in 30 minutes, or they’ll give you your money back. But what are they really promising? They’re setting a clear performance goal and backing it up with confidence. How do they measure their performance? They track how long each delivery takes. And why do they make this promise? Because fast service is key to keeping their business thriving.

Get a clear understanding of log analytics—what it is, why it matters, and how it helps you keep your systems running efficiently by analyzing key data from your infrastructure.

At Honeycomb, we know how important it is for organizations to have a unified observability platform. This is why we’re launching Honeycomb Telemetry Pipeline and Honeycomb for Log Analytics: to enable engineering teams to send and analyze data—including logs—into a single, unified platform. For too long, teams have had to wrangle large volumes of logs, their context scattered across multiple teams and tools, leading to knowledge silos.

With the precision of search and the intelligence of AI, Cisco uses Elastic on Google Cloud to create richer search experiences, so support engineers can quickly find the answers they need. Scaling from this success, Cisco's Search team added AI models, semantic search, and vector search to more than 50 internal- and external-facing apps, helping them innovate more quickly and increase overall operational efficiency.