As businesses generate, capture, and seek to analyze more data than ever before, they often find themselves limited by high data storage costs, expensive data processing fees, and high management overhead. For organizations who wish to expand their log analytics programs and become more data-driven, maximizing cost efficiency has become a critical operational objective.

In my last blog post, Building a Cost-Effective Full Observability Solution Around Open APIs and CNCF Projects, we introduced using ChaosSearch in combination with the most popular open source front- and back-ends in the application observability space. In case you missed it, the TL;DR version is that you can use a variety of open source projects and open API-based components to build the best-of-breed observability stack of your choice rather than relying on expensive, all-in-one solutions.

As high-scale Financial Technology (FinTech) companies build on their data-driven capabilities and establish product-market fit, they often experience rapid growth that creates challenges for IT and DevOps teams.

Building, testing, and monitoring applications in the cloud is a unique challenge. While many organizations have embraced a DevOps methodology, their DevOps machine is still not at the level of maturity they might like it to be. According to a recent survey, 53% work on a team with a 'low level' of DevOps based on maturity factors.

A full Observability stack has the goal of providing full centralized visibility to Development, Operations and Security teams into all of the Metrics, Logs and Traces generated by the applications and services under their domain. Many companies address these observability needs by buying a complete application performance management (APM) solution from a single vendor, like DataDog.

Global data, analytics and technology companies such as Equifax, and their Engineering teams, depend on log analytics for a variety of operational analytics use cases, from application troubleshooting to streamlining cloud operations and regulatory compliance management. ChaosSearch is uniquely positioned to help companies like Equifax significantly reduce the time, cost, and complexity of log analytics.

Leading a team of data and analytics professionals isn’t easy; it takes more than just understanding the goal. It’s about the journey and how the people on the journey collaborate. Wes Gelpi, Director of Research & Development at SAS, joins us in a special 2-part episode. Gelpi has a rich history of taking challenging situations and running with them.

Datadog has made a name for itself as a popular cloud-native application performance monitoring tool, measuring a system’s health and status based on the telemetry data it generates. This telemetry includes machine-generated data, such as logs, metrics and traces. Cloud based applications and infrastructure generate millions (even billions) of logs – and analyzing them can generate a wealth of insights for DevOps, security, product teams and more.

Is your organization currently relying on Amazon CloudWatch for log management and log analytics in the cloud? While CloudWatch delivers on many promises for AWS infrastructure monitoring, it isn’t the only log analytics solution – and may not even be your best option. Fast-growing organizations should consider supplementing CloudWatch with innovative alternatives offering better performance at scale, superior cost economics, reduced complexity and enhanced data access in the cloud.

Armed with a Ph.D. from UC San Diego, our guest started off with internships at Google and Microsoft before gaining valuable experience as a VP and a highly sought-after consultant for startups and SMBs. Now he’s one of the world’s foremost experts on wrangling vast data sets and maximizing efficiency.