Systems | Development | Analytics | API | Testing

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Perforce's Approach to Open-Source Communities

Perforce has been contributing and working in open source for decades now. We understand that open source is the linchpin for technology that supports businesses today. Our approach to open source is not unique to the industry at large or a company of our size (we have around 1,700 employees and 800 are on my team), but questions of our approach to open source became much more visible when we acquired Puppet – which has a really dedicated open-source community and a long history with open source.
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Simplifying AWS Testing: A Guide to AWS SDK Mock

Testing AWS services is an essential step in creating robust cloud applications. However, directly interacting with AWS during testing can be complicated, time-consuming, and expensive. The AWS SDK Mock is a JavaScript library designed to simplify this process by allowing developers to mock AWS SDK methods, making it easier to simulate AWS service interactions in a controlled environment. Primarily used with AWS SDK v2, AWS SDK Mock integrates with Sinon.js to mock AWS services like S3, SNS, and DynamoDB.

Kubernetes Load Testing: How JMeter and Speedscale Compare

At some point, your development team may be considering implementing load testing (also known as stress testing) as part of your software testing process. Load testing validates that your web app is able to withstand a large number of simultaneous users, decreasing the chance that any traffic spikes will bring down your services once deployed. These stress tests can be highly granular, giving you the opportunity to test run virtually unlimited strategies before they are set into the wild.

Ways to Use Mock Services in Software Development

Mocking APIs is a popular practice in software development. An increasing number of developers are reaping the benefits and no longer using their valuable time to spin up duplicate resources. Many mock services do not require account creation, making them easy to use and privacy-friendly. In the rest of this article, we explain what mock APIs are, when you should think about using them, and what solutions are available within the open-source and proprietary markets.

Organizations leveraging SAP Application Testing solutions by Tricentis gain an average of $5.33 million in annual benefits

Companies that stand still in today’s ever-evolving, fast-paced business landscape are at significant risk of losing competitive advantage. Increasingly, high-quality software is playing a vital role in ensuring that organizations are able to innovate, pivot, and adapt to suit the growing demands of their customer base. However, application testing challenges have significantly hindered the pace at which software can be delivered for too long.

3 Ways to Increase Trust in Your Epicor Data

Epicor’s ability to provide industry-focused, scalable, and customizable ERP solutions has made it a popular choice for organizations across the globe. Epicor’s built-in reporting capabilities are useful for standard reports but can be limiting for organizations that require more advanced analytics. Without deep technical knowledge of Epicor’s data structures, attempting to manually create custom reports can create serious roadblocks to data trust within your organization.

12 Game-Changing Intelligent Automation Use Cases Revolutionizing the Energy & Utilities Sector

According to recent research, scaling intelligent automation could save the Energy & Utilities industry between $237 billion and $813 billion in operational costs. Despite this immense potential, utility providers still grapple with intense competition, increasingly stringent regulations, and aging infrastructure and workforce challenges. The demand for more affordable and cleaner renewable energy only adds to these pressures.

Introducing Container Runtime: Enabling Flexible, Scalable Training and Inference on GPUs from a Snowflake Notebook

Predictive machine learning continues to be a cornerstone of data-driven decision-making. However, as organizations accumulate more data in a wide variety of forms, and as modeling techniques continue to advance, the tasks of a data scientist and ML engineer are becoming increasingly complex. Oftentimes, more effort is spent on managing infrastructure, jumping through package management hurdles, and dealing with scalability issues than on actual model development.