Systems | Development | Analytics | API | Testing

Sifting through vast amounts of data for usable information is both challenging and time-consuming for independent software vendors (ISV) in today’s fast-paced market. But without a continuous search for timely insights, you risk your end-users missing critical business opportunities, or failing to address emerging issues in their data promptly, potentially leading to dissatisfaction and churn.

Nearly every facet of society has felt the impact of AI since it burst into the mainstream in late 2022 with the public launch of ChatGPT. In 2024, the retail and consumer goods industry is expected to experience massive upheaval due to the proliferation of generative AI (gen AI) tools as well as changes in customer engagement and the general manner in which products are now sold.

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

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

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.

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.

At Snowflake, we believe in empowering our customers to harness the full potential of their data while maintaining robust compliance standards and safeguarding data privacy. We recognize the critical importance of quickly identifying and safeguarding sensitive data objects, and we consistently strive to provide solutions that help achieve these goals — from advancements such as classification and tag-based policies to the intuitive Data Governance UI.

Apache Iceberg has recently grown in popularity because it adds data warehouse-like capabilities to your data lake making it easier to analyze all your data—structured and unstructured. It offers several benefits such as schema evolution, hidden partitioning, time travel, and more that improve the productivity of data engineers and data analysts. However, you need to regularly maintain Iceberg tables to keep them in a healthy state so that read queries can perform faster.

Generative AI’s (gen AI) capabilities seemed startlingly novel a year ago, when ChatGPT’s release led to an explosion of public usage and, simultaneously, intense debate about its potential societal and business impacts. That period of initial amazement and suspicion has given way to business urgency, as companies scramble to adopt gen AI in ways that leverage its potential for maximizing workforce productivity and profitability.