In the continuous quest to raise the bar on software quality, product teams often find it difficult to avoid trade-offs between release speed and test coverage. One of the more significant sources of trade-off tension is associated with test automation runtimes. Achieving the necessary device coverage during testing often requires teams to seek out multiple device cloud providers with different strengths across security, geolocation, device types, and support for alternative test automation frameworks.

Exploratory testing, a dynamic and versatile approach to software testing, has gained significant traction in recent years. In previous blog posts we explained what's Exploratory Testing and why it's so helpful. Also, we have discussed How to develop a Structured Exploratory testing strategy and we introduced why it is the real asset to exceptional functionality and superb UX if you want your QA to go above and beyond basic expectations.

Twelve years ago, a Wakefield Research survey revealed that 1 in 3 Americans thought cloud computing was somehow related to the weather. Fast forward to today, 67% of enterprise infrastructure in the US is cloud-based. Given that 92% of enterprises already have a multi-cloud strategy in place or in the works, it’s evident that embracing cloud migration is no longer just an option but a strategic necessity.

We are here to deliver another two-month dose of Loadero updates! These updates will cover changes implemented in Loadero during July and August 2024.

In software development, load testing plays a critical role in ensuring that applications perform optimally under any imaginable load condition. To do this, developers subject applications to several types of load tests, including scalability, spike, endurance, and stress testing. The ultimate goal of these performance tests is to pinpoint potential bottlenecks and ensure the reliability of the overall system where the software application runs before reaching production.

Mergers and acquisitions (M&A) have become a stepping stone to corporate growth strategies. Companies worldwide are actively turning to these deals to expand market reach and drive financial performance. The latest data from EY-Parthenon confirms this trend, with M&A activity projected to surge by 12% in 2024. While the idea of combining companies is undeniably exciting, a critical yet often overlooked factor that can either make or break a deal is data governance.

In the finance industry, data is the lifeblood that powers everything from daily operations to strategic decision-making. Financial institutions manage vast amounts of data, ranging from transaction records and market feeds to customer information and regulatory reports. Efficiently processing and analyzing this data is crucial for maintaining competitiveness and compliance in a fast-paced, highly regulated environment.

Gemini in BigQuery features are now generally available, including SQL and Python code generation and explanation, and the BigQuery data canvas.

Mergers and acquisitions (M&As) are strategic business activities where two or more companies join forces by combining their assets, operations, and management structures, often resulting in a unified entity or allowing one company to absorb another. These transactions are typically pursued to enhance competitive advantage, expand market share, or achieve operational efficiencies.