The COVID-19 pandemic has disrupted organizations around the world in many ways. Changing government regulations have created challenges for businesses and customers as they try to comply with mandates. As a result, customers have shifted their demands to digital channels for accessing goods and services. Business operations have also hit road bumps as their workforces may have decreased and/or have limited capacity working from home.

In response to the many disruptions across supply chains over the past year, new trends are emerging and others are gaining more traction to help bolster resilience in this space. Organizations are pivoting operations, leveraging different resources, and taking advantage of new technologies to streamline their supply chain orchestration moving forward.

Heroku is a cloud platform as a service (PaaS) for efficiently building, deploying, monitoring, and scaling applications. Originally created to work with the Ruby programming language, Heroku is now part of the Salesforce platform and supports languages such as Java, Node.js, PHP, Python, and Scala. While Heroku makes it easy to develop production-ready applications fast, one question remains: how can you integrate your Heroku app data with the rest of your data infrastructure and workflows?

Apache Spark is now widely used in many enterprises for building high-performance ETL and Machine Learning pipelines. If the users are already familiar with Python then PySpark provides a python API for using Apache Spark. When users work with PySpark they often use existing python and/or custom Python packages in their program to extend and complement Apache Spark’s functionality. Apache Spark provides several options to manage these dependencies.

In this blog post we are going to look at several JMeter Controllers, specifically: This is not an exhaustive list of controllers that JMeter offers but these once will give you a clear insight into how controllers are integral in defining load testing scenarios and how without them you will struggle to build complex and indicative load tests.

Change is the primary cause of service reliability issues for agile engineering teams. In this post, I’ll cover how you can limit the impact of a buggy change, making it past your quality gates with Kong Gateway and Spinnaker for canary deployment.

Whether you're transitioning away from a monolith or building a green-field app, opting for a microservice architecture brings many benefits as well as certain challenges. These challenges include namely managing the network and maintaining observability in the microservice architecture. Enter the service mesh, a valuable component of modern cloud-native applications that handles inter-service communication and offers a solution to network management and microservice architecture visibility.

Our research finds that leaving Jenkins behind results in a higher return on investment, thanks to predictability of resource requirements and freed-up capacity. Read our new report focusing on the business impact of switching to cloud-based CI.

Working from home, remote-first, asynchronous work. There are many ways to describe our new way of professional life. Much has been explored from an employees point of view, but how do leaders and managers deal with the new situation? We have a look at the challenges and needs of software engineering leadership.

Helix ALM is an application lifecycle management tool, which includes requirements management, test case management, and issue management. It is applicable across a broad range of methodologies, including modern agile practices, traditional waterfall development, or a hybrid approach. Because it encompasses a broad scope of records and team activities, Helix ALM specializes in end-to-end traceability and configurable workflows.