Big Data and the Cloud - More Hype or a Real Workload?

BuckWoody — Tue, 18 Oct 2011 13:57:36 GMT

Last week Microsoft announced several new offerings for “Big Data” - and since I’m a stickler for definitions, I wanted to make sure I understood what that really means. What is “Big Data”? What size hard drive is that? After all, my laptop has 1TB of storage - is my laptop “Big Data”?

There are actually a few definitions for this term, most notably those involving the “Four V’s” Volume, Velocity, Variety and Variability. Others disagree with this definition. I tend to try and get things into their simplest form, so I’m using this definition for myself:

Big data is defined as a large set of computationally expensive data that is worked on simultaneously.

Let me flesh that out a little. To be sure, “Big Data” has a larger size than say a few megabytes. The reason this is important is that it takes special hardware to be able to move large sets of data around, store it, process it and so on. (large set)

If you store a LOT of data, but only use a small portion of it at a time, that really isn’t super-hard to do. It’s mainly a storage issue at that point. But, if you do need to work with a large portion of the data at one time, then the memory, CPU and transfer components of the system have to adapt to be responsive - new ways to work with that data (game theory, knot-algorithms, map-reduce, etc.) need to be brought into play. (computationally expensive)

Once that data is loaded into the processing area (memory or whatever other mechanism is used) it must be worked on in parallel to come back in a reasonable time. You have two options here - you can scale the system up with more internal hardware (CPU’s, memory and so on) or you can scale it out to have multiple systems work on it at the same time using paradigms such as map/reduce and so on. Actually, when you lay this out in an architecture diagram, scale up or out doesn’t actually change the logical structure of the process - in scale out the network becomes the bus, and the nodes become more RAM and computing power. Of course, there are changes in code for how you stitch the workload back together. (worked on simultaneously)

So back to the original question. Is Big Data, as I have defined it here, a workload for Windows and SQL Azure? Absolutely! In fact, it’s probably one of the main workloads, and I believe it represents the latest, and perhaps also the earliest frontier of computing. Jim Gray, a former researcher here at Microsoft and a hero of mine, was working on this very topic. I believe as he did - all computing is simply an interface over data.

Microsoft has multiple offerings on the topic of Big Data. In posts that follow from myself and my co-workers, we’ll explore when and where you use each one. Whether you are a data professional or a developer, this is the new frontier - don’t wait to educate yourself on how to leverage Big Data for your organization.

Hadoop on Windows Azure and SQL Server - Microsoft’s partnership to include Hadoop workloads on Windows Azure and SQL Server/Parallel Data Warehouse (PDW)

LINQ to HPC - Microsoft’s High-Performance Computing SKU of HPC is now in Azure

Windows Azure Table Storage - A key/value pair type storage with full partitioning that is immediately consistent, able to handle huge loads of data and works with any REST-compatible language

Other offerings - Including the new Data Explorer, Project Daytona (with a Big Data Toolkit for Scientists and researchers), Power View and more.

The era of Big Data is here. And you can use Windows and SQL Azure to bring it to your organization.

SQL Azure Use Case: Shared Storage Application

BuckWoody — Tue, 26 Apr 2011 13:33:50 GMT

This is one in a series of posts on when and where to use a distributed architecture design in your organization's computing needs. You can find the main post here: http://blogs.msdn.com/b/buckwoody/archive/2011/01/18/windows-azure-and-sql-azure-use-cases.aspx

Description:

On-premise data will be a part of computing for quite some time – perhaps permanently. Bandwidth requirements, security, or even financial considerations for large data sets often dictate that relational (on non-relational) systems will be maintained locally in many organizations, especially in enterprise computing.

But distributed data systems are useful in many situations. Organizations may wish to store a portion of data off-site, either for sharing the data with other applications (including web-based applications) or as a supplement to a High-Availability and Disaster Recovery (HADR) strategy.

Implementation:

SQL Azure can be used to add an additional option to an HADR strategy by copying off portions (or all) of an on-premise database system.

In this arrangement, on-premise systems remain as they are. Data is replicated using many technologies, such as SQL Server Integration Services (SSIS), scripts, or Microsoft’s Sync Framework to a SQL Azure database. This data can be kept “cold”, meaning that a manual process is required to bring the data back, or as a “warm” standby using connection string management in the application.

Recently we architected a solution where a company kept a rolling two-week window of data replicated to SQL Azure using the Sync Framework. The application, a compiled EXE running on user’s systems, had a “switch connections” button, that allowed the users to take a laptop to another location, select that option, and continue working from anywhere they had Internet connectivity. This required forethought and planning, and did not replace their primary HADR systems, but it did allow them to continue operations in the case of a severe outage at multiple sites. Since they are an emergency services provider, this gave them the highest redundancy.

Another option is to amalgamate data from disparate sources.

In this arrangement, two or more data services (one of which is SQL Azure) are accessed by a single program. The program queries each system independently, and using LINQ a single query can work across all of the data, assuming there is some sort of natural or artificial “key” that can join the data sets together. The user programs simply view this single data set as a single data source, unaware of the underlying data sets. This allows great flexibility and agility in the downstream program. The upstream data sources can change as long as the elements are kept consistent.

There are performance and security implications to amalgamated data systems, but if architected carefully they provide multiple benefits. A few of of these are that other systems can access the individual data sources, reporting is simplified and standardized, and multiple copies of data are eliminated.

Resources:

You can read more about the Sync Framework and SQL Azure here: http://social.technet.microsoft.com/wiki/contents/articles/sync-framework-sql-server-to-sql-azure-synchronization.aspx

If you are new to LINQ, you can find more resources on it here: http://msdn.microsoft.com/en-us/library/bb308959.aspx

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Big Data and the Cloud - More Hype or a Real Workload?

SQL Azure Use Case: Shared Storage Application