Systems | Development | Analytics | API | Testing

Being data-driven helps businesses to cut costs and produce higher returns on investments, increasing their financial viability in the fight for a piece of the market pie. But *becoming* data-driven is a more labor-intensive process. In the same way that companies must align themselves around business objectives, data professionals must align their data around data models. In other words: if you want to run a successful data-driven operation, you need to model your data first.

It’s been shown that if you follow a proven collection of practices for developing, designing, testing, implementing, and maintaining your software, you will produce a much higher quality product. Over the past few years, we have seen an increasing number of cases of attacks on the application layer. The Open Web Application Security Project, OWASP, estimates that around one-third of web applications contain security vulnerabilities.

Over the last 25 years, I have an unparalleled front seat to the digital transformation that is now accelerating in the connected manufacturing and automotive industry. Not many people have had the opportunity to witness the transformation and be as active in this area as I have; I consider myself lucky.

Most of us know the story of “The Tortoise and the Hare.” It is one of Aesop’s classic fables in which a speedy, overconfident hare becomes complacent and realizes, all too late, that the tortoise, although outmatched, has managed to beat him in a race. It teaches us lessons about overconfidence and perseverance and has caused phrases like “slow and steady wins the race” to creep into our everyday language.

“You cannot be the same, think the same and act the same if you hope to be successful in a world that does not remain the same.” This sentence by John C. Maxwell is so relevant to rapidly changing cloud hosting technology. Businesses understand the added value and are looking at cloud technologies to handle both operational and analytical workloads.

At Kong, I get a chance to discuss with various organizations their plans and projects to adopt microservices and expose them with APIs. During these discussions, I’ve started to recognize some patterns that appear with regularity – patterns that have less to do with technology than with people. Technologists and engineers like myself usually do not pay too much attention to the “softer” aspects of technology implementations.

Employees today are more mobile than ever. As we saw, due to COVID-19 the majority of organizations moved their employees to a work from home model overnight. This quick change of location forced businesses to implement solutions that would provide their workforces secure remote access to an increasingly complex corporate network.

This blog post is a first of a series on how to leverage PyTorch’s ecosystem tools to easily jumpstart your ML / DL project. The first part of this blog describes common problems appearing when developing ML / DL solutions, and the second describes a simple image classification example demonstrating how to use Allegro Trains and PyTorch to address those problems.

In 2009 Node.js opened up a door for front-end developers to dip their toes into the world of servers without having to leave the comfort of their language. It’s almost effortless to get started with Node. You can basically copy-paste an entire HTTP server into existence and then install an ODM and you’ve got your CRUD app ready to roll! However, if we’ve learned anything from the amazing Spider-Man, it’s that with great power, comes great responsibility.

There are two ways you can view the current mappings on your Logit ELK Stacks. One way is to use dev tools in Kibana. You can access Kibana from any of your dashboards by choosing from your dashboard Stack settings > Access Kibana. You can also search for a specific mapping of an Index name. For example if we wanted to see the mappings for the a Filebeat index name we can run the following to return only the desired mappings.