Since I joined Snowflake, I have been asked multiple times what data warehouse modeling approach Snowflake best supports. Well, the cool thing is that Snowflake supports multiple data modeling approaches equally. Turns out we have a few customers who have existing data warehouses built using a particular approach known as the Data Vault modeling approach, and they have decided to move into Snowflake. So the conversation often goes like this.

Apache Impala is used today by over 1,000 customers to power their analytics in on premise as well as cloud-based deployments. Large user communities of analysts and developers benefit from Impala’s fast query execution, helping them get their work done more effectively. For these users performance and concurrency are always top of mind.

It feels like a holy war is brewing in data management. At the heart of these rumblings is something that may seem sacrilege to many data architects: the days of the traditional data warehouse are numbered. For good reason. As data volumes continue to grow exponentially, the industry is united in recognizing we need a faster, more agile way to leverage data to unearth insights and drive actions. But that’s about all the industry agrees on.