Snowflake launched its first installment of integrations with Salesforce due to a recent partnership as well as a slew of new features designed to improve everything from search and queries to third party data ingestion and masking for security. Salesforce Ventures was an investor in Snowflake's latest round of funding. The two parties also agreed to better integrate across products for joint customers. The native integrations between Snowflake and Salesforce include tools to unify and analyze data in Snowflake Cloud Data Platform and visualize it in Tableau and Salesforce. On a press briefing, Snowflake CEO Frank Slootman and Tableau CEO Adam Selipsky said the integrations will close data gaps and provide more actionable information for enterprises.
That much is clear from an interview the actor -- who's one of the highest-paid in world -- did with British tabloid The Daily Star. The Rock said that the snowflakes are taking society in a direction that doesn't align with the ideals of freedom that people have fought for in the past decades with their PC sentiments. "So many good people fought for freedom and equality, but this generation are looking for a reason to be offended," the 46-year-old told The Daily Star. "If you are not agreeing with them then they are offended, and that is not what so many great men and women fought for." While The Rock does think that the world has made a lot of progress in the past decades, he also thinks that the people of "generation snowflake" are standing in the way of positive change.
With Snowflake as a data source for Amazon SageMaker Data Wrangler, you can now quickly and easily connect to Snowflake without writing a single line of code. Additionally, you can now join your data in Snowflake with data stored in Amazon S3, and data queried through Amazon Athena and Amazon Redshift to prepare data for machine learning. Once connected, you can interactively query data stored in Snowflake, easily transform data with 300 pre-configured data transformations, understand data, and identify potential errors and extreme values with a set of robust pre-configured visualization templates. You can also quickly identify inconsistencies in your data preparation workflow and diagnose issues before models are deployed into production. Finally, you can export your data preparation workflow to Amazon S3 for use with other SageMaker features such as Amazon SageMaker Autopilot, Amazon SageMaker Feature Store, and Amazon SageMaker Pipelines.