Systems | Development | Analytics | API | Testing

Data privacy laws continue to evolve and expand their reach, touching consumers, businesses, and regions of the world. The European Union’s General Data Protection Regulation (GDPR) has inspired many countries to establish their own regulations and set similar parameters for data collection. The Chile Data Protection Law is one of these regulations. While staying compliant isn’t always simple, it’s necessary for your operations and maintaining customer trust.

Organizations over the years have seen the writing on the wall: The future is cloud. Now, these companies and their DevOps teams areevolving, innovating, and pursuing new technologies, to gain a competitive edge and create new efficiencies. One of the ways they’re doing this is through application migration to cloud. In this blog, I’ll detail the nuances of application migration and how to best manage data during it, including various challenges and their solutions.

According to Perforce's 2026 State of DevOps report, there is a direct correlation between DevOps maturity and AI success. In a highly mature DevOps environment, AI accelerates innovation, optimizes workflows, and enhances security. In an immature environment, it scales chaos, multiplies risk, and inflates costs. So, before we ask ourselves how to make the most of our AI solutions, we must assess if our foundational processes are prepared for the challenge ahead.

Data masking vs. tokenization — which should your organization be using to protect sensitive data? The simplest answer: if you need to easily re-access original data, tokenization is preferable. If you need irreversibly transformed data for development or analytics, masking is the superior choice. This is especially true when it comes to using data for artificial intelligence (AI).

As AI becomes embedded in software delivery, the gap between mature DevOps organizations and those with “Incomplete DevOps” is becoming impossible to ignore, according to Perforce's 2026 State of DevOps report. Characterized by inconsistent workflows, manual processes, and inadequate standardization, "incomplete DevOps" has emerged as the leading obstacle to achieving ROI from AI investments. DevOps maturity is no longer an operational concern. It is an economic one.

Data virtualization can help modern organizations solve the complex challenges that come with managing data. With information scattered across multiple systems, accessing data can lead to operational bottlenecks in your organization.

High-Performance Computing (HPC) has traditionally been an exclusive club. If you wanted to run massive simulations or crunch petabytes of data, you had to leverage the predominant languages used on supercomputing hardware—usually C, C++, or Fortran. Although fast and efficient, these languages demand strict memory management and complex syntax that require strong software development skills. Without them, development time can slow down significantly. But the landscape is shifting.

Provisioning data for software testing requires balancing realism against security. Teams need production-like data to validate applications effectively. But they also have to adhere to strict privacy regulations. Two of the leading methods for creating and securing test data are data masking and synthetic data generation. Data masking de-identifies sensitive production data, preserving its scale, realism and referential integrity.

I've lost count of how many times I've heard some version of this story: A development team needs to spin up a new environment for testing, but the request often sits in a queue for days — sometimes weeks — while infrastructure teams wrestle with storage constraints and provisioning bottlenecks. By the time the environment is ready, priorities have shifted, sprint deadlines have been missed, and the team that requested it is already firefighting the next production issue. The kicker?