Search results matching tags 'Concepts', 'Career', 'Azure', and 'Data'

How Does the Cloud Change a Developer's Job?

BuckWoody — Tue, 12 Feb 2013 16:26:51 GMT

I've recently posted a blog on how cloud computing would change the Systems Architect’s role in an organization, another on how the cloud changes a Database Administrator's job, and the last post dealt with the Systems Administrator. In this post I'll cover the changes facing the Software Developer when using the cloud.

The software developer role was the earliest adopter of cloud computing. This makes perfect sense, because the software developer has always used computing "as a service" - they (most often) don't buy and configure servers, platforms and the like, they write code that runs on those platforms. And there's probably not a simpler definition of a software developer to be found, but as with all simple statements, you lose fidelity and detail. I'll offer a more complete list in a moment.

Because the software developer's process involves designing, testing and writing code locally and then migrating it to a production environment, all of the paradigms in cloud computing - from IaaS to PaaS to SaaS - come naturally.

The Software Developer's Role

The software developer has evolved since the earliest days of programming.The software developer not only "writes code" - there are far more tasks involved in modern systems development:

Assisting the Business Role(s) in developing software specifications
Planning software system components and modules
Designing system components
Working in teams writing classes, modules, interfaces and software endpoints
Designing data layouts, architectures, access and other data controls
Designing and implementing security, either programmatic, declarative, or referential
Mixing and matching various languages, scripting and other constructs within the system
Designing and implementing user and account security rights and restrictions
Designing various software code tests - unit, functional, fuzz, integration, regression, performance and others
Deploying systems
Managing and maintaining code updates and changes

Like most of the previous roles, those tasks also unpacks into a larger set of tasks, and no single developer has exactly that same list. And like the DBA, the role is often more, or less of that list based on where the developer works. Smaller companies may include the development platform in the duties so that a developer is also a systems administrator. In larger organizations I've seen developers that specialized on User Interfaces, Engine Components, Data Controls or other specific areas.

How the Cloud Changes Things

The software developer role obviously has the same concerns and impacts of "the cloud" as the Systems Architect. They need to educate themselves on the options within this new option (Knowledge), try a few test solutions out (Experience) and of course work with others on various parts of the implementation (Coordination).

The big changes for a developer include three major areas: Hybrid Software Design, Security, and Distributed Computing.

Hybrid Software Design

After the PC revolution, software developers designed systems that ran primarily on a single computer. From there the industry moved to "client/server", where most of the code still lived on the user's workstation, and various levels of state (such as the data layer) moved to a server over fast connected lines. After than followed the Internet phase, which had less to do with HTML coding than it did with state-less architectures. While no architecture is truly stateless, there are ways of allowing the client to be in a different state than the server of the application at any one time - this is the way the Web works.

Even so, the developer often simply moved one the primary layers (such as Model, View or Controller) to the server, using the User Interface merely as the View or Presentation layer. While technically stateless, this doesn't require a great deal of architecture change - there are various software modules that run on a server, and perhaps that connects to a remote data server. In the end, it's still a single paradigm.

We now have the ability to run IaaS (hardware abstraction), PaaS (hardware, operating system and runtime abstraction) and SaaS (everything abstracted, API calls only) in a single environment such as Windows Azure. A single application might have a Web-based Interface Server with federated processes (using a PaaS set of roles), a database service (using a SaaS provider such as Windows Azure SQL Database), a specialized process in Linux (using an IaaS role in Windows Azure) and a translator API (from the Windows Azure Marketplace). This example involves only one vendor - Microsoft. I've seen applications that use multiple vendors in this same way.

Thinking this way opens up a great deal of flexibility - and complexity. Complexity isn't evil; it's how complicated things get done many times. The modern developer needs to understand how to build hybrid software architectures.

Resources: Hybrid Architectures with step-by-step instructions and examples: http://msdn.microsoft.com/en-us/library/hh871440.aspx and Windows Azure Hybrid Systems: http://msdn.microsoft.com/en-us/library/hh871440.aspx?AnnouncementFeed

Security

Having a single security boundary, such as "everyone who works in my company", is a relatively simple problem to solve. Normally the System Administrators configure and control a security provider, such as Active Directory, and developers can access that security layer programmatically. That allows for good separation of duties and role-based control.

In modern applications, clients, managers, and users both internal and external need various levels of access to the same objects, code and data. A client should be able to enter an order, a store should be able to accept the order, the credit-card company should be able to check the order and authorize payment, and the managers should be able to report on the order or change it if needed. Using role-based security across multiple domains would be impossible to maintain.

Enter "claims-based" authentication. In this paradigm, the user logs in with whatever security they use - corporate or other Active Directory, Facebook, Google, whatever. The application (using Windows Identity Foundation or WIF) can accept a "claim" from that provider, and the developer can match whatever parts of that claim they wish to the objects, code and data. And example might be useful.

Buck logs in to his corporate Active Directory (AD), and attempts to use a program based in Windows Azure. Windows Azure rejects the login silently, and is configured to check with Buck's AD. Buck's AD says "yes, I know Buck, and he has been granted the following claims: "partner", "manager", "approver". The developer does not need to know about Buck's AD, Buck, his login, or anything else. She simply codes the proper data access to allow "approver" to approve a sale.

This allows a lot of control, at a very fine level, without having to get into the details of each security provider. .

Resources: Overview of using claims-based Azure Security: http://adnanboz.wordpress.com/2011/02/06/claims-based-access-and-windows-azure/

Distributed Computing

Is there a difference between stateless computing, or even the hybrid programming I mentioned earlier, and "Distributed Computing"? Yes - the primary difference is latency. Even stateless code can have too small a tolerance for latency.

Dealing with slow connectivity, or breaks in connections has many impacts. One method of dealing with this is to locate data and computing of that data as closely as possible, even if this means relaxing consistency or duplicating data. Another method is to go back to a great paradigm from the past that is possible underused today is a Service Oriented Architecture. The Windows Azure Service Bus is possibly one of the fastest and easiest way to adopt cloud computing without completely rearchitecting your application.

References: Great breakdown of the thought process around a distributed architecture: http://msdn.microsoft.com/en-us/magazine/jj553517.aspx and using a Windows Azure Relay Service: http://www.windowsazure.com/en-us/develop/net/how-to-guides/service-bus-relay/

How Does the Cloud Change a Database Administrator’s Job?

BuckWoody — Tue, 29 Jan 2013 15:08:32 GMT

I recently posted a blog entry on how cloud computing would change the Systems Architect’s role in an organization. In a way, the Systems Architect has the easiest transition to a new way of using computing technologies. In fact, that’s actually part of the job description. I mentioned that a Systems Architect has three primary vectors to think about for cloud computing, as it applies to what they should do:

Knowledge - Which options are available to solve problems, and what are their strengths and weaknesses.
Experience - What has the System Architect seen and worked with in the past.
Coordination - A system design is based on multiple factors, and one person can't make all the choices. There will need to be others involved at every level of the solution, and the Systems Architect will need to know who those people are and how to work with them.

The Database Administrator Role

But a Database Administrator (DBA) is probably one of the harder roles to think about when it comes to cloud computing. First, let’s define what a Database Administrator usually thinks about as part of their job:

Planning, Installing and Configuring a Database Platform
Planning, designing and creating databases
Planning, designing and implementing High Availability and Disaster Recovery for each database (HADR) based on requirements for its workload
Maintaining and monitoring the database platform
Implementing performance tuning on the databases based on monitoring
Re-balancing workloads across database servers based on monitoring
Securing databases platforms and individual databases based on requirements and implementation

That’s just a short list, and each of those unpacks into a larger set of tasks.

The issue is that I’ve never actually met a DBA that does all of those things, or just all of those things. Many times they do much more, sometimes the systems are so large they specialize on just a few of them.

And as you can see from the list, some of these areas are shared with other roles. For instance, in some shops, the DBA plans, purchases, sets up and configures the hardware for database servers. In others that’s done
by the Infrastructure Team. In some shops the DBA designs databases from software requirements, and in others the developers do that – or perhaps it’s done as a joint effort. The same holds true for database code – sometimes the
DBA does it, other times the developer, and still others it’s a shared task.

In fact, you could argue that there are few other roles in IT where the roles are so intermixed. Also, the DBA works with software the company develops, and software the company buys. They work with hardware, networking, security and software. There are certain aspects of design and tuning that are outside the purview of some of those things, and inside the others.

With all of these variables, simply telling a DBA that they should “use the cloud” is not the proper approach.

How the Cloud Changes Things

To be sure, the DBA has the same vectors as the Systems Architect. They need to educate themselves on the options within this new option (Knowledge), try a few test solutions out (Experience) and of course work with others on various parts of the implementation (Coordination). But it goes beyond that.

There are three big buckets of cloud computing, dealing with simply using a Virtual Machine (IaaS) to writing code without worrying about the virtualization or even the operating system (PaaS) and using software that’s already written and being delivered via an Application Programming Interface (API). Each of these has so many options and configurations that it’s often better to think about the problem you’re trying to solve rather than all of the technology within a given area - although some of that is certainly necessary anyway.

Database Platform Architecture

I’ll start with when the DBA should even consider cloud computing for a solution. Once again, it’s not an “all or nothing” paradigm, where you either run something on premises or in the cloud – it’s often a matter of selecting the right components to solve a problem. In my design sessions with DBA’s I break these down into three big areas where they might want to consider the cloud –and then we talk about how to implement each one:

Audiences
HADR
Data Services

Audiences

If the users of your database systems all sit in the same facility, you own the servers and networking, and the application servers are separate from the database server, it doesn’t usually make sense to take that database workload and place it on Windows Azure – or any other cloud provider. The latency alone prevents a satisfactory performance profile, and in some cases won’t work at all. It doesn’t matter if the cloud solution is cheaper or easier – if you’re moving a lot of data every second between an on-premises system and the cloud it won’t work well.

However – if your users are in multiple locations, especially globally, or you have a mix of company and external customer users, it might make sense to evaluate a shared data location. You still need to consider the implications of how much data the application server pushes back and forth, but you may be able to locate both the application server and SQL Server in an IaaS role. Assuming the data sent to the final client will work across public Internet channels, there may be a fit. There are security implications, but unless you have point-to-point connections for your current solution you’re faced with the same security questions on both options.

Your audience might also be developers looking for a way to quickly spin up a server and then turn it down when they are done, paying for the time and not the hardware or licenses. This is also a prime case for evaluating IaaS. And there are others that you'll find in your own organization as you work through the requirements you have.

Resources: Windows Azure Virtual Machines: http://www.windowsazure.com/en-us/manage/windows/tutorials/virtual-machine-from-gallery/ and Windows Azure SQL Server Virtual Machines: http://www.windowsazure.com/en-us/manage/windows/common-tasks/install-sql-server/

HADR

The next possible place to consider using cloud computing with SQL Server is as a part of your High Availability and Disaster Recovery plans. In fact, this is the most common use I see for cloud computing and the Database Administrator. The key is the Recovery Point Objective (RPO) and Recovery Time Objective (RTO). Based on each application’s requirements, you may find that using Windows Azure or even supplementing your current plan is
the right place to evaluate options. I’ve covered this use-case in more detail in another article.

References: SQL Server High Availability and Disaster Recovery options with Windows Azure: http://blogs.msdn.com/b/buckwoody/archive/2013/01/08/microsoft-windows-azure-disaster-recovery-options-for-on-premises-sql-server.aspx

Data Services

Windows Azure, along with other cloud providers, offers another way to design, create and consume data. In this use-case, however, the tasks DBA’s normally perform for sizing, ordering and configuring a system don’t apply.

With Windows Azure SQL Databases (the artist formerly known as SQL Azure), you can simply create a database and begin using it. There are places where this fits and others where it doesn’t, and there are differences, limitations and enhancements, so it isn’t meant as replacement for what you could do with “Full-up” SQL Server on a Windows Azure Virtual Machine or an on-premises Instance. If a developer needs an Relational Database Management
(RDBMS) data store for a web-based application, then this might be a perfect fit.

But there is more to data services than Windows Azure SQL Databases. Windows Azure also offers MySQL as a service, RIAK and MongoDB (among others) and even Hadoop for larger distributed data sets. In addition you can use Windows Azure Reporting Services, and also tap into datasets and data functions in the Windows Azure Marketplace.

The key for the DBA with this option is that you will have to do a little investigation this time, and potentially without a specific workload in mind this time. I think that’s acceptable thing to ask – DBA’s constantly keep up with data processing trends, and most will consider different ways to solve a problem.

References:

Windows Azure SQL Databases: http://www.windowsazure.com/en-us/home/features/data-management/

Windows Azure Reporting Services: http://www.windowsazure.com/en-us/manage/services/other/sql-reporting/

HDInsight Service (Hadoop on Azure): https://www.hadooponazure.com/

MongoDB Offerings on Windows Azure: http://www.windowsazure.com/en-us/manage/linux/common-tasks/mongodb-on-a-linux-vm/

Windows Azure Marketplace: http://www.windowsazure.com/en-us/store/overview/

How Does the Cloud Change a Systems Architect’s Job?

BuckWoody — Tue, 22 Jan 2013 15:43:59 GMT

I know - I said I didn't like the "cloud" term, but my better-phrased "Distributed Systems" moniker just never took off like I had hoped. So I'll stick with the "c" word for now, at least until the search engines catch up with my more accurate term.

I thought I might spend a little time on how the cloud affects the way we work - from Systems Architects to Database Administrators and Developers, and Systems Administrators - a group often referred to as "IT Pro's". But each role within these groups have different aspects when using cloud computing. In this post we'll take a look at the role of the Systems Architect, and in the posts that follow I'll talk more about the other roles in the IT Pro area.

The Systems Architect Role

What does a "Systems Architect" do? Like most IT roles, it depends on the company or organization where they work. In fact, the term isn't even specific to technology, but I'll use it in that context here. In general, a Systems Architect takes the requirements for a given system, and assembles the relevant technology areas that best fulfill those requirements. That's a single-sentence explanation, and needs further unpacking.

As an example, a Systems Architect at a medical firm is presented with a set of requirements for tracking a patient through the entire care cycle. The Systems Architect first looks at all of the requirements for the data that needs to be collected based on business, financial, regulations, and other requirements, and then how that data needs to flow from one system to another. They check the security requirements, performance, location and other aspects of the system. They then check to see which options are available for processing that data, and which parts they should "build or buy".

For instance, the requirements might be so specific that only custom code is the proper solution - but even there, choices still exist, such as which language(s) to use, what type of data persistence (a Relational Database Management System or or other data storage and processing) will be used, what talent within the company is available for the system and a myriad of other decision.

All of this boils down to three primary vectors:

Knowledge - Which options are available to solve problems, and what are their strengths and weaknesses.
Experience - What has the System Architect seen and worked with in the past.
Coordination - A system design is based on multiple factors, and one person can't make all the choices. There will need to be others involved at every level of the solution, and the Systems Architect will need to know who those people are and how to work with them.

How the Cloud Changes Things

From the outset, it doesn't seem that using a distributed system would change anything in the Systems Architect role. Isn't the cloud simply another option that the Systems Architect needs to learn and apply? Yes, that is true - but it goes a bit deeper. Let's return to those vectors a moment to see what a Systems Architect needs to take into account.

Knowledge

The first and probably most obvious impact is learning about cloud technologies. But the important part of that knowledge is to learn when and where to use each service. It's a common misconception that the cloud should be an "all or nothing" approach. That's just not true - every Windows Azure project I work on has some element of on-premises interaction, and in some cases only one small part of a solution is placed on the Windows Azure architecture. Since Windows Azure contains IaaS (VM's) PaaS (you write code, we run it) and even SaaS (Such as Hadoop or Media Services), a given architecture can use multiple components even within just one provider. And I've worked on several projects where the customer used not only Windows Azure and On-Premises environments, but also components from other providers. That's not only acceptable, but often the best way to solve a given problem.

As part of the learning experience, it's vital to keep in mind what you need to pick as key decision points. In your organization, cost could be ranked higher than performance, or perhaps security is the highest decision point.

To stay educated, there are various journals, websites and conferences that Systems Architects use to keep current. Almost all of those are talking about "cloud" - but there is no substitute for learning from the vendor about their solution. I'm speaking here of the technical information, not the marketing information. The marketing information is also useful, at least from a familiarity standpoint, but the technical information is what you need.

Resource: For Windows Azure, the Systems Architect can start here: http://blogs.msdn.com/b/buckwoody/archive/2012/06/13/windows-azure-write-run-or-use-software.aspx

Experience

Cloud computing is relatively new - it's only been out a few years, and the main competitors are only now settling in to their respective areas. It might not be common for a Systems Architect to have a lot of hands-on experience with cloud projects.

Even so, there are ways to leverage the experience of others, such as direct contact or even attending conferences where customers present findings from their experiences.

You can also gain hands-on experience by setting up pilots and proof-of-concept projects yourself. Most all vendors - Microsoft included - have free time available on their systems. The key to an experiment like this is choosing some problem you are familiar with that exercises as many features in the platform as possible. There is no substitute for working with a platform when you want to design a solution.

Coordination

Probably one of the largest changes in the Systems Architect role that the cloud brings is in the area of coordination. When a Systems Architect deals with the business and other technical professionals, there is a 20+ year history of technology that we are all familiar with. When you mention "the cloud", those audiences may not have spent the time you have in understanding what that means - and often they think it means the "all or nothing" approach I mentioned earlier.

I've found that a series of "lunch and learns" for the technical staff is useful to explain to each role-group how the cloud is used in their area is useful. In the posts that follow this one, I'll give you some material for those. For managers and business professionals, you'll want to go a different route. I've found that an "Executive Briefing" e-mail, consisting of about a page, with headings that are applicable to your audience.

Resource: Writing Executive Summaries: http://writing.colostate.edu/guides/guide.cfm?guideid=76

The Data Scientist

BuckWoody — Tue, 15 Nov 2011 15:00:18 GMT

A new term - well, perhaps not that new - has come up and I’m actually very excited about it. The term is Data Scientist, and since it’s new, it’s fairly undefined. I’ll explain what I think it means, and why I’m excited about it.

In general, I’ve found the term deals at its most basic with analyzing data. Of course, we all do that, and the term itself in that definition is redundant. There is no science that I know of that does not work with analyzing lots of data. But the term seems to refer to more than the common practices of looking at data visually, putting it in a spreadsheet or report, or even using simple coding to examine data sets.

The term Data Scientist (as far as I can make out this early in it’s use) is someone who has a strong understanding of data sources, relevance (statistical and otherwise) and processing methods as well as front-end displays of large sets of complicated data. Some - but not all - Business Intelligence professionals have these skills. In other cases, senior developers, database architects or others fill these needs, but in my experience, many lack the strong mathematical skills needed to make these choices properly.

I’ve divided the knowledge base for someone that would wear this title into three large segments. It remains to be seen if a given Data Scientist would be responsible for knowing all these areas or would specialize. There are pretty high requirements on the math side, specifically in graduate-degree level statistics, but in my experience a company will only have a few of these folks, so they are expected to know quite a bit in each of these areas.

Persistence

The first area is finding, cleaning and storing the data. In some cases, no cleaning is done prior to storage - it’s just identified and the cleansing is done in a later step. This area is where the professional would be able to tell if a particular data set should be stored in a Relational Database Management System (RDBMS), across a set of key/value pair storage (NoSQL) or in a file system like HDFS (part of the Hadoop landscape) or other methods. Or do you examine the stream of data without storing it in another system at all?

This is an important decision - it’s a foundation choice that deals not only with a lot of expense of purchasing systems or even using Cloud Computing (PaaS, SaaS or IaaS) to source it, but also the skillsets and other resources needed to care and feed the system for a long time. The Data Scientist sets something into motion that will probably outlast his or her career at a company or organization.

Often these choices are made by senior developers, database administrators or architects in a company. But sometimes each of these has a certain bias towards making a decision one way or another. The Data Scientist would examine these choices in light of the data itself, starting perhaps even before the business requirements are created. The business may not even be aware of all the strategic and tactical data sources that they have access to.

Processing

Once the decision is made to store the data, the next set of decisions are based around how to process the data. An RDBMS scales well to a certain level, and provides a high degree of ACID compliance as well as offering a well-known set-based language to work with this data. In other cases, scale should be spread among multiple nodes (as in the case of Hadoop landscapes or NoSQL offerings) or even across a Cloud provider like Windows Azure Table Storage. In fact, in many cases - most of the ones I’m dealing with lately - the data should be split among multiple types of processing environments. This is a newer idea. Many data professionals simply pick a methodology (RDBMS with Star Schemas, NoSQL, etc.) and put all data there, regardless of its shape, processing needs and so on.

A Data Scientist is familiar not only with the various processing methods, but how they work, so that they can choose the right one for a given need. This is a huge time commitment, hence the need for a dedicated title like this one.

Presentation

This is where the need for a Data Scientist is most often already being filled, sometimes with more or less success. The latest Business Intelligence systems are quite good at allowing you to create amazing graphics - but it’s the data behind the graphics that are the most important component of truly effective displays.

This is where the mathematics requirement of the Data Scientist title is the most unforgiving. In fact, someone without a good foundation in statistics is not a good candidate for creating reports. Even a basic level of statistics can be dangerous. Anyone who works in analyzing data will tell you that there are multiple errors possible when data just seems right - and basic statistics bears out that you’re on the right track - that are only solvable when you understanding why the statistical formula works the way it does.

And there are lots of ways of presenting data. Sometimes all you need is a “yes” or “no” answer that can only come after heavy analysis work. In that case, a simple e-mail might be all the reporting you need. In others, complex relationships and multiple components require a deep understanding of the various graphical methods of presenting data. Knowing which kind of chart, color, graphic or shape conveys a particular datum best is essential knowledge for the Data Scientist.

Why I’m excited

I love this area of study. I like math, stats, and computing technologies, but it goes beyond that. I love what data can do - how it can help an organization. I’ve been fortunate enough in my professional career these past two decades to work with lots of folks who perform this role at companies from aerospace to medical firms, from manufacturing to retail.

Interestingly, the size of the company really isn’t germane here. I worked with one very small bio-tech (cryogenics) company that worked deeply with analysis of complex interrelated data.

So watch this space. No, I’m not leaving Azure or distributed computing or Microsoft. In fact, I think I’m perfectly situated to investigate this role further. We have a huge set of tools, from RDBMS to Hadoop to allow me to explore. And I’m happy to share what I learn along the way.

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.