SQL GUID or Identity in the Cloud
Oh goodness, you have a lot of questions in here. I'll try to unpack them all.

Q: What is "SQL Server in the cloud"?

The term "the cloud" is too generic - you might as well be saying "SQL Server on the network." There are lots of different ways to deploy SQL Server in the cloud, some of which function exactly like on-premises servers (it's just that someone else is managing the server for you.)

If you can, try to avoid generic sweeping statements like "SQL Server in the cloud" because you won't get good answers. The more specific your question can be, the more relevant advice you can get. (Not to mention the less downvotes, ha ha ho ho.)

Q: Is there a universal rule for clustering keys?

Generally, you want your clustering keys to follow the SUN-E principles:


Static - fields that don't change, so you don't have to update your nonclustered indexes
Unique - so SQL Server doesn't have to add a uniqueifier behind the scenes
Narrow - so lots of data isn't copied into your nonclustered indexes
Ever-increasing - creating a hotspot at the end, guaranteeing that where you're inserting will usually be in memory (although this is one rule that folks violate on purpose during extremely heavy concurrency, like tens of thousands of inserts per second)


Identity fields match all 4 (SUN-E), while GUID fields only match the SU portions. One could argue that a single GUID is kinda narrow compared to, say, multiple GUIDs chained together or an NVARCHAR(250), but it's got nothin' on the narrowness of an INT.

Q: Do data modeling guidelines change in IaaS?

Infrastructure-as-a-Service providers like Amazon EC2, Google Compute Engine, and Microsoft Azure VMs are just VMs running on someone else's computer. Data modeling rules do not change here.

Q: Do data modeling guidelines change in PaaS?

Platform-as-a-Service providers are a little different.

Amazon RDS is just SQL Server hosted and managed by someone else, so no data modeling changes required there.

Microsoft Azure SQL DB isn't exactly "just SQL Server hosted and managed by someone else" - it has some strengths and weaknesses that differ from the traditional boxed product. However, if you're only building your app for a single database in Azure SQL DB (not scaling out to multiple databases), then the conventional rules apply.

Q: When does "the cloud" affect data modeling guidelines?

When you want to scale a single application across multiple databases and/or servers. This design pattern is called sharding, splitting a single identical table (or sets of tables) across hosts.

To be clear, you can do this same design pattern on-premises - it's just more trendy these days in the cloud because it's so much easier to spin up multiple servers or databases.

You noted that your database only uses 70GB per year - that's well below the numbers where I'd even remotely consider sharding.

Q: When sharding, what data modeling concerns will I have?

First, your application has to know which shard to hit in order to get the relevant data. (You don't want the app hitting the database to discover this data for every query - after all, you'd be right back up against the scalability limits of a single database server.)

You could shard by customer location - like one shard for North America, another for Europe, another for Asia. However, your customers may move between locations, or they may have locations spread across contents.

You could shard by customer name - like A, B, C, D - but you'll have some shards that are much hotter than others due to load. (Say one of your customers runs a World Cup sale - that server might fall over while the rest are idle.) And of course, customers can change names.

You could shard by activity date - like the date of sale - but of course today's shard will be ridiculously hot compared to the other shards.

By now, you're starting to see now why your question is so broad, and so difficult to answer. Your best bet to get an actionable, useful answer will be to provide as many specifics as you can. However, since you asked for a design for 70GB of data per year, keep it simple: stick with the SUN-E principles.