Cloud Computing and Data Warehousing: Part 1 – The Architectural Issues

My apologies… I was playing with the iPad version of WordPress and accidentally published a very rough outline/first draft of this post. I immediately un-published it… but not before subscribers were notified that there was a new post.

I wonder about the idea that data warehousing is suited to operate in the cloud? This was prompted by Paraccel‘s venture to deploy on the Amazon EC2 cloud infrastructure. Lets work through the architectural implications…

Here are the assumptions I’ll take into this exploration:

  1. A shared-nothing architecture is required to scale.
  2. Cloud infrastructure is cost-effective when the infrastructure is under-utilized and workloads can be consolidated to achieve full utilization… and not so cost-effective when the infrastructure is highly utilized. This is because applications can easily share underutilized resources in the Cloud.
  3. Cloud infrastructure is justified when the workload is inconsistent and either CPU or storage requirements fluctuate widely over the business cycle. This is because a Cloud is elastic and can easily flex as the requirements fluctuate. Cloud computing may not be well suited to static workload requirements.

You can probably see where I’m going with this from the assumptions.

In the end I’ll suggest that there is a database architecture that is suited to warehousing and cloud computing… but let me build to that.

Before I start let me also be clear that I am talking about the database infrastructure… not the application/BI infrastructure required for data warehousing. The BI and ETL components are perfectly suited to cloud computing… they reflect a workload that, in general, runs on under-utilized hardware with BI running during the day and ETL running at night. I have suggested this to my current employer… but alas, I am neither King nor a member of Court.

So in Part 1 let me discuss my first two assumptions and the implications… In Part 2 I’ll discuss data warehousing and elasticity… In Part 3 I’ll consider the Paraccel/Amazon collaboration and in Part 4 I’ll wrap up and consider several new things coming that may change the equations.
I’ll not work too hard to justify my first assumption… I think that it is well-understood that a shared-nothing architecture provides the best possible approach to scale out. Google and others use this approach to scale to hundreds of petabytes of data and Teradata, Greenplum, Netezza, Paraccel, SAP HANA, and others use it in the data warehouse space. Exadata uses a hybrid approach that scales I/O in a shared-nothing-like storage subsystem… but fails to scale as it passes data to the RAC layer (see Kevin Closson here on the subject).

But the implications are significant for our cloud discussion. First, cloud infrastructure is designed to support general client-server or web-server based commercial computing requirements. A shared-nothing database cluster is a specialized infrastructure optimized for database processing. Implementing the specialized problem on the generalized infrastructure is possible, but sub-optimal. Next, cloud computing requires, more or less, a shared storage subsystem. A shared-nothing architecture shares nothing. Implementing a shared-nothing database on a shared storage subsystem is possible, but sub-optimal.

I believe that the second assumption is also pretty straightforward. The primary rationale for cloud computing comes from the recognition that many data centers deployed applications on servers that were not fully utilized. By virtualizing the hardware on a cloud platform the data center could better service the applications with fewer hardware resources and therefore less cost.

So… in order for cloud computing to be a perfect fit we need to observe a data warehouse database workload with underutilized hardware infrastructure… You might ask yourself… are there underutilized hardware resources upon which my EDW is built? In most cases I believe that the answer to this question will be “no”. Almost every EDW I’ve seen is over-burdened… stretched… with users demanding more and more resource… more data, more users, more queries, deeper queries drive the resource requirements up exponentially. The database is swamped all day with queries and swamped all night by ETL and reporting tasks.

So let’s end this blog concluding that there is a problematic architectural mismatch between a shared cloud and a shared-nothing implementation… and that if your warehouse database platform is highly utilized then there may be little benefit from implementing a warehouse in the cloud.

See Part 2 here

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