Systems | Development | Analytics | API | Testing

Databricks helps companies process large-scale data, and Databricks data masking ensures high-quality, compliant data. If your organization relies on data for AI/ML or analytics, it’s essential to ensure you are implementing a robust, compliant data masking strategy. Let’s explore how data masking works natively in Databricks. Then, we’ll go over challenges teams might run into in using native masking capabilities.

With the release of WSO2 Identity Server 7.1, WSO2 continues to lead with innovation in the open-source identity space by introducing AI-powered development capabilities. The new features introduced, enable developers to secure applications more efficiently using natural language instructions, reducing time, effort, and the required identity domain expertise.

Google just released A2A (Agent2Agent)—a new open protocol for agent to agent communication—and it left people wondering if they just started a protocol war against MCP (Model Context Protocol).

BigQuery’s evolution as an autonomous data-to-AI platform relies on new features and services to automate and optimize data analytics and management.

New intelligent unified governance capabilities in BigQuery enable organizations to simplify data management and unlock actionable insights.

Migrating to BigQuery, the data to AI platform from Google Cloud, just got simpler, with AI-powered BigQuery Migration Services enhancements.

Man-in-the-middle attacks are elegantly mitigated in TLS, with TLS1.3 introducing more robust encryption protocols and a streamlined handshake process that significantly reduces the vulnerability to these types of attacks. By employing stricter encryption standards and eliminating outdated cipher suites, TLS 1.3 enhances security measures to effectively counter potential interception and unauthorised data decryption.

CI/CD performance testing seamlessly integrates performance validation directly into your continuous integration and deployment pipeline. Instead of treating performance as an afterthought, this approach makes it an automated part of your development workflow. By testing performance with every build, you can catch issues early, ensure consistent performance across releases, and maintain high quality standards for your application.