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.

In the previous blog post we covered the high availability feature of Cloudera Operational Database (COD) in Amazon AWS. Cloudera recently released a new version of COD, which adds HA support to Microsoft Azure-based databases in the Cloud. In this post, we’ll perform a similar test to validate that the feature works as expected in Azure, too.

Snowflake kicked off 2024 with exciting releases, including Snowpark Model Registry, Streamlit in Snowflake for Azure, and new enhancements around security features in Snowflake Horizon. Read on to learn more about everything we announced in January.

Imagine your software transforming from merely a tool into a strategic partner that can automatically alert your users to trends, provide explanations of data with a click, and help you ask the right questions of your data-sets - in addition to delivering data-led insights. This is the power of AI analytics solutions for independent software vendors (ISV). Today's users demand more than just functionality. They crave intelligent software that analyzes data, surfaces insights, and empowers them to act.

In Cloudera deployments on public cloud, one of the key configuration elements is the DNS. Get it wrong and your deployment may become wholly unusable with users unable to access and use the Cloudera data services. If the DNS is set up less ideal than it could be, connectivity and performance issues may arise. In this blog, we’ll take you through our tried and tested best practices for setting up your DNS for use with Cloudera on Azure.

Today, we are excited to share insights into the strategic vision, product highlights, and the roadmap that will shape the future of data integration, analytics, AI, and machine learning. Click here to check out the webinar on-demand.