Azure, GCP, BigQuery, Snowflake & Cloud Competition

This is a snippet of the transcript, sign up to read more.

Isn’t there a physical infrastructure required to build the datacenters and offer the latency effectively? Let's say that we've got Amazon, Microsoft, Google, and maybe one more, potentially, but those three are definitely going to have the capital and the capabilities to build the physical infrastructure. Is there any competitive advantage that one of those can earn in infrastructures of service?

That probably goes to a follow up question; what are the real advantages of AWS versus Azure versus GCP? Like I said, infrastructure is table stakes. But I would say the biggest thing that Azure has over AWS is hybrid capabilities. Amazon's go-to market strategy and the technical architecture has always been cloud; let's do cloud native, as much as possible. Since Microsoft started its roots in Windows Server and SQL on premise, they realized that customers were still in the very early innings or stages of moving their critical systems to the cloud. They wanted to provide a path to cloud and so they have a stronger hybrid offering. I think that's a key distinction that people need to realize. From infrastructure, they're all common but you have to think in terms of what my application footprint is going to be like? What is my data footprint is going to be like? Then you're going to have AI and machine learning. Then you have to think about how you are going to interoperate that on premise? It's not like, I'm going to wake up tomorrow, and I'm going move 50% of my SAP applications to the cloud; that takes years. I think having that flexibility of giving people that hybrid choice is a huge advantage.

This is a snippet of the transcript, sign up to read more.

How do you compare BigQuery to the other offerings?

Everybody was trying to get into Data Warehouse. You have Data Warehouse, Redshift, and you have Synapse or SQL, Azure SQL. It was hard, because Redshift probably has the largest base; they've been around the longest. What we looked for around BigQuery was a high amount of transactions. NASDAQ is a good example, where you have a whole bunch of real time transactions coming in. Another one could be sensor data that could be coming in. When you look at these different types of data warehouse, you have to look at the four Vs. The volume of the data, the veracity of the data, the velocity or speed and you also look at the value of the data. Do you need millisecond response? Is it emitted response? Customers can decide, and then you say, if you really want to run data, then the tools are important, the analytic tools are important. We had a hard time competing with Microsoft, because the world leader at that time was Microsoft BI, because everybody was familiar with those tools. Then you have Tableau but Microsoft BI is by far the most entrenched and most user friendly because it stemmed from Power BI, Excel and Power BI.

This is a snippet of the transcript, sign up to read more.