Operations | Monitoring | ITSM | DevOps | Cloud

As enterprises move towards a digital-first strategy, they rely much more on their IT infrastructures. An organization’s infrastructure drives the entire business and thus must be aligned with the organization’s business goals. This crucial task of managing an enterprise’s infrastructure brings its own share of difficulties to the table. However, the primary concern is to ensure the infrastructure’s scalability and optimal performance.

As two of the most popular and practical languages out there, should you choose Java or Python for your next project? Is one of these languages a clear-cut better option? The answer is a long one. According to GitHub’s annual Octoverse report, Python has now climbed to the second most popular language in usage, pushing Java down to third place.

To fully capitalize on the promises of digital transformation, IT leaders have come to recognize that a mix of cloud and data center infrastructure provides several business advantages, including increased agility, cost efficiencies, global availability, and, ultimately, better customer experiences.

Sentry helps every developer diagnose, fix, and optimize the performance of their code, and we need to deliver high quality stack traces in order to do so. You might have noticed a significant improvement in Sentry JavaScript stack traces recently. In this blog post, we want to explain why source maps are insufficient for solving this problem, the challenges we faced, and how we eventually pulled it off by parsing JavaScript.

Today Codecov is joining the Sentry family. Codecov began as a code coverage reporting tool in 2014 and has since emerged as a market leader in the test analytics space. Codecov makes coverage actionable for over two dozen test frameworks, and has helped over a million software developers improve their approach to testing, coverage, and code reliability. You might be asking, what do test analytics have to do with application monitoring?