There are many ways to bootstrap tests and mocks within Speedscale. Matt LeRay goes over various ways, eg. by using sidecars, agents, postman collections, or even request response pairs.
Throughout my career in enterprise technology, I've witnessed numerous transformations play out across the Asia-Pacific (APAC) region. But the shift we're seeing now with data streaming is truly unprecedented. What was once a supportive technology is rapidly becoming the very foundation of modern business in our region.
Every device, transaction, and interaction in our digital world generates an endless stream of data. By 2025, the amount of global data is expected to reach a mind-boggling 180 zettabytes. So, how do we extract and make sense of this growing data? That’s exactly where generative AI proves its value. This blog explains generative AI applications for document extraction and how this technology helps cut through the noise and zero in on exactly what you need.
We are now in the final chapter of Apache Kafka’s multi-year journey to remove Apache ZooKeeper and fully transition to self-managed metadata in KRaft. Many Kafka users and customers are beginning to migrate to KRaft and are eager to understand its performance characteristics in production environments.
Boundary Value Analysis (BVA) is a crucial software testing technique that focuses on testing the boundaries or edges of input ranges. It is based on the observation that errors often occur at the edges of input ranges rather than in the middle. By testing the extremes, BVA helps identify potential vulnerabilities and ensures that the system behaves correctly at its limits.
Mocks can be useful, but hard to build. You can use them as backends for development, or even tests (like load and performance testing). Speedscale takes the legwork out of building mocks, by modeling them after real observed traffic. This video covers a real-world example of how to use mocks to backend a JMeter load test.
Equivalence Partitioning, also known as Equivalence Class Testing, is a powerful black-box testing technique designed to streamline testing by minimizing the number of test cases while ensuring comprehensive coverage. This approach is widely used to make testing more efficient without sacrificing effectiveness. Let’s learn more about how it’s done!
As data-driven decision-making becomes a cornerstone of business strategy, managing large volumes of data efficiently and effectively is more critical than ever. Google BigQuery, a serverless, highly scalable, and cost-effective multi-cloud data warehouse, offers unique architecture and unparalleled integration with Google Cloud Platform (GCP) services. However, migrating and optimising data pipelines in BigQuery can present challenges.