Recent advances in artificial intelligence (AI), particularly in generative AI with the release of large language models (LLM) such as OpenAI’s GPT 3.5 and 4.0, Google’s Gemini, and Meta’s Llama in 2023, have had a profound effect on business procedures and practices in several industries, including software development, operation, and quality engineering (QE).

Apache Kafka has become the de facto standard for data streaming, used by organizations everywhere to anchor event-driven architectures and power mission-critical real-time applications. However, this rise has also sparked discussions on improving Kafka operations and cost-efficiency—streaming data is naturally prone to bursts and often unpredictable, resulting in inevitable variations in workloads and demand on your Kafka cluster(s).

Quality Assurance (QA) teams must run extensive tests to ensure that a website or application performs as intended before release. However, rather than manually running these tests, which can hinder productivity, modern teams are using QA automation. By automating QA testing workflows, teams can rapidly execute these tests using standardized processes, frameworks, and software. Developers can quickly fix any errors and deliver high-quality code so that product teams can speed up release times.

Data governance covers processes, roles, policies, standards, and metrics that help an organization achieve its goals by ensuring the effective and efficient use of information. It sets up the processes and responsibilities necessary to maintain the data’s quality and security across the business. Data governance manages the formal data assets of an organization.

Are you aware of the fact that India’s overall fintech market opportunity is estimated to be $1.3 Tn by 2025, growing at a CAGR of 31% during 2021-2025? This one statement paved the way for the FinTech revolution in India and is still growing… In just a few years, the emergence of fintech-powered technologies has revolutionized the financial sector, affecting how both large and small firms deal with their finances and transactions.

Have you ever wondered how mobile apps are becoming more personalized and providing enriching experiences? Well, one of the key trends shaping the future of mobile app development is the integration of Artificial Intelligence. With AI, apps can now interact, function, and adapt in new ways to create truly unique experiences for users. As a consequence, Flutter, a popular framework for mobile app development, is becoming a go-to choice for many developers when it comes to integrating AI into their apps.

It's essential to test web applications to ensure reliability, functionality, and a good user experience. That's why robust testing frameworks have become so important for web developers. Among the plethora of available tools, Cypress and Playwright have emerged as two of the most popular choices for automating end-to-end testing. In this Cypress versus Playwright comparison guide, we'll explore the strengths and weaknesses of the two tools, their features, and their differences. Cypress or Playwright?

Enterprises are encountering an ever-increasing number of threats to their digital assets. Attack Surface Management (ASM) has emerged as a crucial practice to mitigate these risks and enhance overall cybersecurity. By understanding and effectively managing the attack surface, enterprises can proactively identify and address vulnerabilities, reducing the likelihood of successful attacks.

As a contract software developer, I am exposed to oodles of Ruby code. Some code is readable, some obfuscated. Some code eschews whitespace, as if carriage returns were a scarce natural resource, while other code resembles a living room fashioned by Vincent Van Duysen. Code, like the people who author it, varies. Yet, it's ideal to minimize variation. Time and effort are best spent on novel problems.

For data engineers, the Generative AI (Gen AI) era is a transformative shift in how we approach data architecture and analytics. Professionals at the forefront of this shift will be gathering in San Francisco, at the Data+AI Summit June 10-13. Attendees will be exploring tools that integrate with Databricks Intelligent Data Platform that decrease data management costs and improve data's impact on business outcomes.