It’s hard to believe it’s been 15 years since the global financial crisis of 2007/2008. While this might be a blast from the past we’d rather leave in the proverbial rear-view mirror, in March of 2023 we were back to the future with the collapse of Silicon Valley Bank (SVB), the largest US bank to fail since 2008.

Unlock blockchain data on Google Cloud BigQuery by using Subsquid, analyze usage trends and more across multiple chains.

Generating files for data extracts, reporting and even data migration is a crucial part of any back-end team. A typical data team will have to generate many files to be sent to various clients or consumers inside or outside of the organisation. As with all things, the question is how can the generation of those files be made simpler, and can it be automated to such a degree that files are dynamically generated? The short answer is yes, it can be done dynamically.

It's a fact that the presence of smartphones in our lives has evolved into a globally dominating reality today. According to a recent reports by Statista, 85.74% of the general population, or roughly 6.93 billion people, are using smartphones and over 7.33 billion people are using mobile phones. This huge figure points to the fact that smartphones are being adopted fast and have become essential to our day-to-day lives.

According to Gartner, JIRA is still one of the most popular application lifecycle management (ALM) technologies in many enterprises, with a rating of 4.4 out of 5 for its features. Determining an “issue permission” enabled with the proper security constraints and balancing its project access control is always a difficult challenge in an enterprise JIRA setup.

In the popular 90’s film “Sliding Doors”, the life of the lead character, played by Gwyneth Paltrow, takes two divergent paths based on whether she catches or misses a train. Spoiler alert if you have not watched it: one path ultimately (and surprisingly) leads to a better outcome than the other.

Test data management (TDM) is usually ignored despite its undeniable contribution to the overall success of the testing process. In complex testing projects with many test scenarios to cover, test data management is a highly critical area to optimize for. QA teams need diverse and comprehensive test data to achieve higher test coverage, and that brings up the need to have a separate place where that data is properly stored, managed, maintained, and set up for future testing.

Vector search in BigQuery, or approximate nearest-neighbor search, enables AI use cases like semantic search, similarity detection, and RAG with LLMs.