According to the Stackoverflow survey of 2019, Python programming language garnered 73.1% approval among developers. It ranks second to Rust and continues to dominate in Data Science and Machine Learning(ML). Python is a developers’ favorite. It is a high-level language known for its robustness and its core philosophy―simplicity over complexity. However, Python application’s performance is another story. Just like any other application, it has its share of performance issues.

A few years ago, we got an evaluation form from a prospective customer wanting to understand more about what we do. In the comments they wrote... “Please don't chase me down like those bastards at Sisense” That little nugget of gold told me a lot about the way our buyers want to purchase software - everyone hates talking to a salesperson too early. So we decided to turn our sales process on its head and put all of our technical evaluation content online.

In our last post, we looked at the multiple layers of testing and where UI tests fit into your overall architecture. In case you didn’t read it, here’s a TLDR: Testing architecture can be grouped into 3 “layers”: Layer 1 tests tiny chunks of code in complete isolation. Layer 2 tests larger pieces of code in partial isolation.

The landscape of software testing is changing. In the hyper-competitive world of technology, speed and quality are often seen as opposing forces. We are told to “move fast and break things” if we are to succeed in getting our products into the hands of users before our competition beats us to the punch. This often times means sacrificing quality and confidence in the name of getting new features out the door.

Data scientists and engineers at the online data science education platform focus on attribution, customer lifetime value and the ideal customer profile.

Make enterprise data more accurate, and instantly actionable, by adding automated data integration to your stack. Today’s enterprises and medium-sized companies are looking to ensure that critical business decisions are guided by rigorous data analysis. They have scaled up their analytics teams (composed of data engineers, data scientists and data analysts), and their IT departments have tried to meet the needs of those teams.

What’s the best way to measure help desk performance? Nearly 25 IT and Support professionals weigh in with their preferred metrics.

With 25+ Facebook Ads action type metrics that are now available in Databox’s Query Builder, you’ll have fewer questions and more answers.

These 10 Databox features will allow you to make your performance data more accessible and insightful so you and your team can focus on what matters most.