Operations | Monitoring | ITSM | DevOps | Cloud

June 2023

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Logs vs. Events: Exploring the Differences in Application Telemetry Data

What is the difference between logs and events in observability? These two telemetry data types are used for different purposes when it comes to exploring your applications and how your users interact with them. Simply put, logs can be used for troubleshooting and root cause analysis, while events can be used to gain deeper application insights via product analytics. Let's review some application telemetry data definitions for context, then dive into the key differences between logs and events and their use cases. Knowing more about these telemetry data types can help you more effectively use them in your observability strategy.

Data-Led Growth: How FinTechs Win with App Event Analytics

In the rapidly shifting world of financial technology (FinTech), acquiring and retaining new customers to achieve long-term business growth requires a proactive approach to user experience and application performance optimization. As FinTech companies compete against rivals to grow a user base and revolutionize how consumers manage their finances, they increasingly depend on data-driven insights to optimize their mobile applications and deliver exceptional user experiences.

Unleash the Potential of Your Log and Event Data, Including AI's Growing Impact

In this Techstrong Learning Experience, Techstrong Research GM Mike Rothman and André Rocha, VP Product & Operations from ChaosSearch, will share insights from a recent Techstrong audience poll on this topic, and discuss the most pressing challenges and solutions, including the inevitable and significant impact of Generative AI.

Stile Education's Best-of-Breed Observability Strategy

"One of the best things we’ve gotten out of ChaosSearch is the ability to keep all of our data in S3. It’s cheap and easy to keep all of our data available and indexed. We can search through it at any time to dig deeper into problems that crop up." Learn more about how the Stile's team can now retain log data indefinitely, versus saving only a week or two of data in Elasticsearch. That change has increased the team’s capacity to use log data to solve business problems, and unlocked new opportunities to discover deeper product insights.

Data Lake Architecture & The Future of Log Analytics

Organizations are leveraging log analytics in the cloud for a variety of use cases, including application performance monitoring, troubleshooting cloud services, user behavior analysis, security operations and threat hunting, forensic network investigation, and supporting regulatory compliance initiatives. But with enterprise data growing at astronomical rates, organizations are finding it increasingly costly, complex, and time-consuming to capture, securely store, and efficiently analyze their log data.

10 AWS Data Lake Best Practices

A data lake is the perfect solution for storing and accessing your data, and enabling data analytics at scale - but do you know how to make the most of your AWS data lake? In this week’s blog post, we’re offering 10 data lake best practices that can help you optimize your AWS S3 data lake set-up and data management workflows, decrease time-to-insights, reduce costs, and get the most value from your AWS data lake deployment.