With digital technologies transforming industries, companies adopt Agile and other practices to gain a competitive advantage. DevOps is one such method that has changed software development as we know it. The new-age approach helps accelerate productivity and also enables better collaboration between the development and operation teams. But the biggest benefit of adopting DevOps is faster time-to-market. It has helped around 83% of organizations unlock business value and deliver customer value faster.

Data management in the modern enterprise requires skill and stamina. With the mountainous rise in data volume and the complexity of managing disparate data sources across a wide variety of legacy systems and hybrid cloud environments, it can seem virtually impossible to successfully climb past enterprise data obstacles to success. However, data fabric technology eliminates some of those obstacles. Is it the answer for your data strategy?

Deciding where to run your enterprise workloads is one of the biggest business decisions you’ll make. That’s because your chosen deployment model influences how you access software and systems, scale your business, and safeguard sensitive data. There are three main deployment methods you need to know about: Self-hosted, on-premises, and cloud. All three models have pros and cons, so it’s important to do your research before investing in new infrastructure and/or services.

In software development, integration testing is essential in ensuring that all system components function correctly and work together seamlessly. Critical bugs can go unnoticed without proper integration testing, leading to significant issues in production environments. This blog talks about Integration testing and Types of Integration testing. One example of a bug found in production due to the absence of integration testing is the infamous Equifax data breach in 2017.

BigQuery data clean rooms can help organizations create and manage secure environments for privacy-centric data sharing, analysis, and collaboration.

From DIY opportunity cost and pipeline maintenance to moving and transforming data, here’s how to judge the cost-effectiveness of ELT.

Customizing exceptions is usually not a common concern during software development. But if you catch an error in an observability tool and can't correctly and quickly identify the problem, you may need more information and details about an exception. This article will show you how to customize exceptions in Ruby and mitigate potential future problems due to a lack of error information. Let's dive straight in!

Every engineering leader wants to build a high-performance team that can set the standard for velocity, quality, and innovation. Yet, to do that, you need to focus on creating a culture of continuous improvement where everyone in the team is committed to learning and growing.

We are announcing BigQuery ML inference engine, which allows you to run predictions on a broad range of models hosted across multiple locations.