Troubleshooting customer issues in production is a difficult job. These are the issues that impact the business the most, so consequently, stress levels are almost always at a high. And it’s never fun to be measured against an SLA, which feels like you’re stuck in a losing battle. And it’s especially hard in the world of microservices and Kubernetes, because it’s so difficult to recreate a reliable replica of production in your local development environment.

The Google Cloud Public Datasets program recently published the Python Package Index (PyPI) dataset into the marketplace. PyPI is the standard repository for Python packages. If you’ve written code in Python before, you’ve probably downloaded packages from PyPI using pip or pipenv. This dataset provides statistics for all package downloads, along with metadata for each distribution. You can learn more about the underlying data and table schemas here.

Imagine, you are developing Java software and suddenly you encounter an error? Where could you have possibly gone wrong? There are many types of errors that you will encounter while developing Java software, but most are avoidable. Some errors are minor lapses when writing codes but that is very much mendable. If you have an error monitoring tool such as Stackify Retrace, you can write codes with ease.

The Fundamental Review of the Trading Book (FRTB), introduced by the Basel Committee on Banking Supervision (BCBS), will transform how banks measure risk. FRTB is designed to address some fundamental weaknesses that did not get addressed in the post-2008 financial crisis regulatory reforms. In order to help make banks more resilient to drastic market changes, it will impose capital requirements that are more closely aligned with the market’s actual risk factors.

If you already have some brand awareness, so people know who you are and what you do, there are three ways that you can accelerate your sales in a virtual environment.

Data is often compared to oil – it powers today’s organizations, just like the fossil fuel powered companies of the past. Just like oil, the data that companies collect needs to be refined, structured, and easily analyzed in order for it to really provide value in the form of gaining actionable insights. Every organization today is in the process of harnessing the power of their data using advanced analytics, which is likely running on a modern data stack.

As you may or may not know, Atlassian is accelerating their Journey to the Cloud. This has some important implications to anyone who uses Atlassian Server based products, including apps. Read on to learn how we support customers of Zephyr for Jira and Zephyr Scale as they transition to Cloud.

Use database connectors to uncover insights about your operations.

Getting a spot in SERP features takes study & strategy. Learn how experts optimize their content to rank in featured snippets and other SERP elements.

Setting up a proper monitoring overview over your application’s performance is a complex task. Normally, you’d first need to figure out what you need to monitor, then instrument your code, and finally make sense of all the data that has been emitted. However, with a few things set in place, and an APM that natively supports Ruby, it’s easier than ever to take this step. In this post, we’ll show you how you can do it too.