This is a continuation of the books I challenged myself to read to help my career - one a month, for year. You can read my first book review here, and the entire list is here. The book I chose for April 2012 was: Applied Architecture Patterns on the Microsoft Platform. I was traveling at the end of last month so I’m a bit late posting this review here.

Why I chose this book:

I actually know a few of the authors on this book, so when they told me about it I wanted to check it out. The premise of the book is exactly as it states in the title - to learn how to solve a problem using products from Microsoft.

What I learned:

I liked the book - a lot. They've arranged the content in a "Solution Decision Framework", that presents a few elements to help you identify a need and then propose alternate solutions to solve them, and then the rationale for the choice. But the payoff is that the authors then walk through the solution they implement and what they ran into doing it.

I really liked this approach. It's not a huge book, but one I've referred to again since I've read it. It's fairly comprehensive, and includes server-oriented products, not things like Microsoft Office or other client-side tools. In fact, I would LOVE to have a work like this for Open Source and other vendors as well - would make for a great library for a Systems Architect. This one is unashamedly aimed at the Microsoft products, and even if I didn't work here, I'd be fine with that. As I said, it would be interesting to see some books on other platforms like this, but I haven't run across something that presents other systems in quite this way.

And that brings up an interesting point - This book is aimed at folks who create solutions within an organization. It's not aimed at Administrators, DBA's, Developers or the like, although I think all of those audiences could benefit from reading it. The solutions are made up, and not to a huge level of depth - nor should they be. It's a great exercise in thinking these kinds of things through in a structured way.

The information is a bit dated, especially for Windows and SQL Azure. While the general concepts hold, the cloud platform from Microsoft is evolving so quickly that any printed book finds it hard to keep up with the improvements.

I do have one quibble with the text - the chapters are a bit uneven. This is always a danger with multiple authors, but it shows up in a couple of chapters. I winced at one of the chapters that tried to take a more conversational, humorous style. This kind of academic work doesn't lend itself to that style.

I recommend you get the book - and use it. I hope they keep it updated - I'll be a frequent customer. :)

If you want to be wise, watch the actions and outcomes of others. Emulate the successful actions, and avoid the actions that cause failure. That’s true in life in general - and in technology projects in specific. 

I’ve worked with several clients who have created or migrated an application to “the cloud” - meaning using Microsoft Windows Azure or another provider. Although the statement in the title of this post is trite, I cannot over-emphasize how accurate it is. In every case of those who had a great experience with a distributed computing environment (which is thankfully the vast majority of my projects),

What kind of preparation do you need to do? Here are some tips I’ve learned in the successful (and not-so-successful) deployments I’ve seen:

Follow standard recommendations for successful projects in general

You and your organization have probably done a few projects before - this one should have the same general attributes: a well-defined goal, a small, motivated team, a realistic timeline, and an adequate budget. I know, I know, you *never* seem to get those things - but if you don’t, you’ll fail. Simple as that.

Educate yourself

Computing technology started out on a single set of hardware for a single purpose - and realizing the limits of the hardware at hand, systems designers quickly realized that scale-out and virtualization was key. No, that’s not new - mainframes almost always worked on the concept of scale-out and virtual machines. But we switched in the 1980’s to single-user systems again, and we’ve been there ever since. By that I mean you install an OS on the things you work on. Now we move back to distributed system concepts, and there are some real differences. You’ll need to learn how those work, and do things a new way. Hey, we’re IT - we LOVE learning new things, right?

Get a partner if needed

There are a few of us white-haired Gandalf’s around that remember how to work in a distributed system, but if it’s new to you, that’s completely OK. You can save yourself a world of trouble by working with someone who’s done this before - a partner you hire, someone from Microsoft Consulting, whatever.

And don’t forget support - who will handle each issue, what is the escalation model, who are your contacts at Microsoft, and what is your “light’s out” strategy?

“A new broom sweeps clean”, the old adage goes, but the old brooms know where the dirt is.

Build a model

Take some time to do a Proof of Concept on your local system and using your Azure hours from your MSDN account if you have one. Going through this build - and being willing to throw it away and try it a different way - is invaluable.

Test your theories

Three statisticians are walking in a field. They see a rabbit - the first guy raises his gun, firing far in front of the rabbit. The second guy simultaneously raises his gun and fires far behind the rabbit. The third guy yells “We got him!”

Not every theory is correct - not every attempt is the right one. Build in your success tests while you’re building your model. Then check them - don’t leave this step out.

Rinse, lather, repeat

This is advice from a shampoo bottle - which I’ve never used (I don’t really have that much hair - especially now). But in a “Cloud” project, it’s important. It’s an evolving system, that gains new improvements at an amazing rate. As soon as you deploy and stabilize you need to start the process over again. If you created your system in a Services model, with contracts for the APIs and abstracted code, this is far easier.

It’s not hard to do a cloud project right. But it’s really simple to do it wrong. Follow these guidelines and you’ll learn from the successes - and mistakes - of others.

For reasons I don't completely understand, I'm not allowed to call the following advice "Best Practices" - apparently there is some liability or something there. So let's say these are "really good ideas" for developing applications for Windows Azure. (Did you see how I worked it into the title anyway so the search engines find it? Always thinking. :) )

And of course these are by no means comprehensive - just a few notes I've made as I've been involved with projects. So here they are, in no particular order:

• Understand the platform - never stop learning, it changes all the time: SDK and Training Kit are the best resources. http://channel9.msdn.com/Blogs/pdc2008/ES03

It may sound pedantic, but it's just true. You need to learn how the platform works. The most issues I run into are things that are documented - most of the time very clearly.

• Follow standard best practices for your language

Windows Azure code does have some differences, but in large part it's just code. If you write Java to run on Azure, write good Java. Learn the best practices for your codeset and apply those - it will not only make your code faster and less error-prone, it will be cheaper to run.

• Keep a backup of your deployments

Things happen. Make sure you have your latest deployment checked into source code. If someone were to perhaps stop paying the bill and the Role is deleted, Microsoft does not have a secret backup of your application. Do not ask me how I know this.

• Batch up calls - fewer calls are usually more efficient (and cheaper) than lots of smaller calls.

This is typical for distributed computing environments - include the retry logic tip as well to ensure the call is successful.

• Use Cache and max out the instance wherever you can - you're paying for it, use it!

• Use the Windows Azure Diagnostics and monitor those counters accordingly: http://blogs.msdn.com/b/buckwoody/archive/2011/09/06/plan-for-diagnostics-in-cloud-computing-from-the-git-go.aspx

• Implement retry logic: http://www.davidaiken.com/tag/best-practices/

In a distributed computing environment, you may have a lag in time between operations. Make your code less brittle to failures for a given call, and ensure that you include retry logic.

• Use multiple instances: http://blogs.msdn.com/b/buckwoody/archive/2012/03/20/why-do-i-need-two-roles-in-windows-azure.aspx

This article is a must-read.

• Have a backup strategy

Microsoft keeps three copies of your data at all times in a datacenter, and backs up those three copies to another for safety. But that's at the latest moment - if you need point-in-time restore, make sure you code that into your architecture.

• Use affinity groups: http://blogs.msdn.com/b/buckwoody/archive/2011/11/28/windows-and-sql-azure-best-practices-affinity-groups.aspx

Affinity groups are a way of keeping your data and code together. This is usually a great idea from a performance standpoint, although there are applications that can break this rule for centralization or seldom-accessed data.

• Have a lights-out strategy

Anything that a human makes can fail. That includes entire datacenters, routers, the Internet. So make sure you know what to do if everything is unavailable. Perhaps that's just a notification system, a full fallback to an on-premises system, something. But plan.

• Security best practices are here: http://blogs.msdn.com/b/usisvde/archive/2012/03/07/windows-azure-security-best-practices-part-1-the-challenges-defense-in-depth.aspx and here http://www.microsoft.com/download/en/details.aspx?displaylang=en&id=7253

• Denial of Service best practices here: http://blog.ehuna.org/2012/04/how_to_stop_a_denial_of_servic.html

• Blob Storage Best Practices: http://acloudyplace.com/2012/01/8-essential-best-practices-in-windows-azure-blob-storage/

This is a continuation of the books I challenged myself to read to help my career - one a month, for year. You can read my first book review here, and the entire list is here. The book I chose for March 2012 was: The Information: A History, a Theory, a Flood by James Gleick. I was traveling at the end of last month so I’m a bit late posting this review here.

Why I chose this book:

My personal belief about computing is this: All computing technology is simply re-arranging data. We take data in, we manipulate it, and we send it back out. That’s computing. I had heard from some folks about this book and it’s treatment of data. I heard that it dealt with the basics of data - and the semantics of data, information and so on.

It also deals with the earliest forms of history of information, which fascinates me. It’s similar I was told, to GEB which a favorite book of mine as well, so that was a bonus.

Some folks I talked to liked it, some didn’t - so I thought I would check it out.

What I learned:

I liked the book. It was longer than I thought - took quite a while to read, even though I tend to read quickly. This is the kind of book you take your time with. It does in fact deal with the earliest forms of human interaction and the basics of data.

I learned, for instance, that the genesis of the binary communication system is based in the invention of telegraph (far-writing) codes, and that the earliest forms of communication were expensive. In fact, many ciphers were invented not to hide military secrets, but to compress information. A sort of early “lol-speak” to keep the cost of transmitting data low!

I think the comparison with GEB is a bit over-reaching. GEB is far more specific, fanciful and so on. In fact, this book felt more like something fro Richard Dawkins, and tended to wander around the subject quite a bit. I imagine the author doing his research and writing each chapter as a book that followed on from the last one. This is what possibly bothered those who tended not to like it, I think.

Towards the middle of the book, I think the author tended to be a bit too fragmented even for me. He began to delve into memes, biology and more - I think he might have been better off breaking that off into another work. The existentialism just seemed jarring.

All in all, I liked the book. I recommend it to any technical professional, specifically ones involved with data technology in specific. And isn’t that all of us? :)

Microsoft is working on a new Windows Azure service called “Trust Services”. Trust Services takes a certificate you upload and uses it to encrypt and decrypt sensitive data in the cloud. Of course, like any security service, there’s a bit more to it than that. I’ll give you a quick overview of how you can use this product to protect data you send to SQL Azure.

The primary issue with storing data in the cloud is that you are in an environment that isn’t under your control – in fact, that’s the benefit of being in a distributed computing environment in the first place. On premises you’re able to encrypt data you don’t want anyone else to see, using various methods such as passwords (not very strong) or certificates (stronger). When you use a certificate, it’s vital that you create (or procure) and protect it yourself.

When you store data remotely, regardless of IaaS, PaaS or SaaS, you don’t own the machines where the data lives. That means if you use a certificate from the cloud vendor to encrypt the data, you have to trust that the data won’t be accessed by the vendor. In some cases having a signed agreement with the vendor that they won’t access your data is sufficient, in other cases that doesn’t meet the requirements your system has for security.

With the new Trust Services service, the basic process is that you use a Portal to create a Trust Server using policies and other controls. You place a X.509 Certificate you create or procure in that server. Using the Software development Kit (SDK), the developer has access to an Application Layer Encryption Framework to set fields of data they want to encrypt. From there, the data can be stored in SQL Azure as a standard field – only it is encrypted before it ever arrives. The portion of the client software that decrypts the data uses the same service, so the authenticated user sees the data if they are allowed to do so. The data remains encrypted “at rest”. 

You can learn more about this product and check it out in the SQL Azure labs at Microsoft Codename "Trust Services"

Windows Azure is a Platform as a Service – a PaaS – that runs code you write. That code doesn’t just mean the languages on the .NET platform – you can run code from multiple languages, including Java. In fact, you can develop for Windows and SQL Azure using not only Visual Studio but the Eclipse Integrated Development Environment (IDE) as well.

 Although not an exhaustive list, here are several links that deal with Java and Windows Azure:

(As with all of these types of posts, check the date of the latest update I’ve made here. Anything older than 6 months is probably out of date, given the speed with which we release new features into Windows and SQL Azure)

I don’t normally like to discuss things in terms of tools. I find that whenever you start with a given tool (or even a tool stack) it’s too easy to fit the problem to the tool(s), rather than the other way around as it should be.

That being said, it’s often useful to have an example to work through to better understand a concept. But like many ideas in Computer Science, “Big Data” is too broad a term in use to show a single example that brings out the multiple processes, use-cases and patterns you can use it for.

So we turn to a description of the tools you can use to analyze large data sets. “Big Data” is a term used lately to describe data sets that have the “Four V’s”  as a characteristic, but I have a simpler definition I like to use:

Big Data involves a data set too large to process in a reasonable period of time

I realize that’s a bit broad, but in my mind it answers the question and is fairly future-proof. The general idea is that you want to analyze some data, and using whatever current methods, storage, compute and so on that you have at hand it doesn’t allow you to finish processing it in a time period that you are comfortable with. I’ll explain some new tools you can use for this processing.

Yes, this post is Microsoft-centric. There are probably posts from other vendors and open-source that cover this process in the way they best see fit. And of course you can always “mix and match”, meaning using Microsoft for one or more parts of the process and other vendors or open-source for another. I never advise that you use any one vendor blindly - educate yourself, examine the facts, perform some tests and choose whatever mix of technologies best solves your problem.

At the risk of being vendor-specific, and probably incomplete, I use the following short list of tools Microsoft has for working with “Big Data”. There is no single package that performs all phases of analysis. These tools are what I use; they should not be taken as a Microsoft authoritative testament to the toolset we’ll finalize for a given problem-space. In fact, that’s the key: find the problem and then fit the tools to that.

Process Types

I break up the analysis of the data into two process types. The first is examining and processing the data in-line, meaning as the data passes through some process. The second is a store-analyze-present process.

Processing Data In-Line

Processing data in-line means that the data doesn’t have a destination - it remains in the source system. But as it moves from an input or is routed to storage within the source system, various methods are available to examine the data as it passes, and either trigger some action or create some analysis.

You might not think of this as “Big Data”, but in fact it can be. Organizations have huge amounts of data stored in multiple systems. Many times the data from these systems do not end up in a database for evaluation. There are options, however, to evaluate that data real-time and either act on the data or perhaps copy or stream it to another process for evaluation.

The advantage of an in-stream data analysis is that you don’t necessarily have to store the data again to work with it. That’s also a disadvantage - depending on how you architect the solution, you might not retain a historical record. One method of dealing with this requirement is to trigger a rollup collection or a more detailed collection based on the event.

StreamInsight - StreamInsight is Microsoft’s “Complex Event Processing” or CEP engine. This product, hooked into SQL Server 2008R2, has multiple ways of interacting with a data flow. You can create adapters to talk with systems, and then examine the data mid-stream and create triggers to do something with it. You can read more about StreamInsight here: http://msdn.microsoft.com/en-us/library/ee391416(v=sql.110).aspx 

BizTalk - When there is more latency available between the initiation of the data and its processing, you can use Microsoft BizTalk. This is a message-passing and Service Bus oriented tool, and it can also be used to join system’s data together than normally does not have a direct link, for instance a Mainframe system to SQL Server. You can learn more about BizTalk here: http://www.microsoft.com/biztalk/en/us/overview.aspx 

.NET and the Windows Azure Service Bus - Along the same lines as BizTalk but with a more programming-oriented design are the Windows and Windows Azure Service Bus tools. The Service Bus allows you to pass messages as well, and opens up web interactions and even inter-company routing. BizTalk can do this as well, but the Service Bus tools use an API approach for designing the flow and interfaces you want. The Service Bus offerings are also intended as near real-time, not as a streaming interface. You can learn more about the Windows Azure Service Bus here: http://www.windowsazure.com/en-us/home/tour/service-bus/ and more about the Event Processing side here: http://msdn.microsoft.com/en-us/magazine/dd569756.aspx 

Store-Analyze-Present

A more traditional approach with an organization’s data is to store the data and analyze it out-of-band. This began with simply running code over a data store, but as locking and blocking became an issue on a file system, Relational Database Management Systems (RDBMs) were created. Over time a distinction was made between data used in an online processing system, meant to be highly available for writing data (OLTP) and systems designed for analytical and reporting purposes (OLAP).

Later the data grew larger than these systems were designed for, primarily due to consistency requirements. In analysis, however, consistency isn’t always a requirement, and so file-based systems for that analysis were re-introduced from the Mainframe concepts, with new technology layered in for speed and size.

I normally break up the process of analyzing large data sets into four phases:

1. Source and Transfer - Obtaining the data at its source and transferring or loading it into the storage; optionally transforming it along the way
2. Store and Process - Data is stored on some sort of persistence, and in some cases an engine handles the acquisition and placement on persistent storage, as well as retrieval through an interface.
3.  Analysis - A new layer introduced with “Big Data” is a separate analysis step. This is dependent on the engine or storage methodology, is often programming language or script based, and sometimes re-introduces the analysis back into the data. Some engines and processes combine this function into the previous phase.
4. Presentation - In most cases, the data wants a graphical representation to comprehend, especially in a series or trend analysis. In other cases a simple symbolic representation, similar to the “dashboard” elements in a Business Intelligence suite. Presentation tools may also have an analysis or refinement capability to allow end-users to work with the data sets. As in the Analysis phase, some methodologies bundle in the Analysis and Presentation phases into one toolset.

Source and Transfer

You’ll notice in this area, along with those that follow, Microsoft is adopting not only its own technologies but those within open-source. This is a positive sign, and means that you will have a best-of-breed, supported set of tools to move the data from one location to another. Traditional file-copy, File Transfer Protocol and more are certainly options, but do not normally deal with moving datasets.

I’ve already mentioned the ability of a streaming tool to push data into a store-analyze-present model, so I’ll follow up that discussion with the tools that can extract data from one source and place it in another.

SQL Server Integration Services (SSIS)/SQL Server Bulk Copy Program (BCP) - SSIS is a SQL Server tool used to move data from one location to another, and optionally perform transform or other processes as it does so. You are not limited to working with SQL Server data - in fact, almost any modern source of data from text to various database platforms is available to move to various systems. It is also extremely fast and has a rich development environment. You can learn more about SSIS here: http://msdn.microsoft.com/en-us/library/ms141026.aspx BCP is a tool that has been used with SQL Server data since the first releases; it has multiple sources and destinations as well. It is a command-line utility,and has some limited transform capabilities. You can learn more about BCP here: http://msdn.microsoft.com/en-us/library/ms162802.aspx 

Sqoop - Tied to Microsoft’s latest announcements with Hadoop on Windows and Windows Azure, Sqoop is a tool that is used to move data between SQL Server 2008R2 (and higher) and Hadoop, quickly and efficiently. You can read more about that in the Readme file here: http://www.microsoft.com/download/en/details.aspx?id=27584 

Application Programming Interfaces - API’s exist in most every major language that can connect to one data source, access data, optionally transforming it and storing it in another system. Most every dialect of  the .NET-based languages contain methods to perform this task.

Store and Process

Data at rest is normally used for historical analysis. In some cases this analysis is performed near real-time, and in others historical data is analyzed periodically. Systems that handle data at rest range from simple storage to active management engines.

SQL Server - Microsoft’s flagship RDBMS can indeed store massive amounts of complex data. I am familiar with a two systems in excess of 300 Terabytes of federated data, and the Pan-Starrs project is designed to handle 1+ Petabyte of data. The theoretical limit of SQL Server DataCenter edition is 540 Petabytes. SQL Server is an engine, so the data access and storage is handled in an abstract layer that also handles concurrency for ACID properties. You can learn more about SQL Server here: http://www.microsoft.com/sqlserver/en/us/product-info/compare.aspx 

SQL Azure Federations - SQL Azure is a database service from Microsoft associated with the Windows Azure platform. Database Servers are multi-tenant, but are shared across a “fabric” that moves active databases for redundancy and performance. Copies of all databases are kept triple-redundant with a consistent commitment model. Databases are (at this writing - check http://WindowsAzure.com for the latest) capped at a 150 GB size limit per database. However, Microsoft released a “Federation” technology, allowing you to query a head node and have the data federated out to multiple databases. This improves both size and performance. You can read more about SQL Azure Federations here: http://social.technet.microsoft.com/wiki/contents/articles/2281.federations-building-scalable-elastic-and-multi-tenant-database-solutions-with-sql-azure.aspx 

Analysis Services - The Business Intelligence engine within SQL Server, called Analysis Services, can also handle extremely large data systems. In addition to traditional BI data store layouts (ROLAP, MOLAP and HOLAP), the latest version of SQL Server introduces the Vertipaq column-storage technology allowing more direct access to data and a different level of compression. You can read more about Analysis Services here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/business-intelligence/analysis-services.aspx and more about Vertipaq here: http://msdn.microsoft.com/en-us/library/hh212945(v=SQL.110).aspx

Parallel Data Warehouse - The Parallel Data Warehouse (PDW) offering from Microsoft is largely described by the title. Accessed in multiple ways including using Transact-SQL (the Microsoft dialect of the Structured Query Language), This is an MPP appliance scaling in parallel to extremely large datasets. It is a hardware and software offering - you can learn more about it here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/data-warehousing/pdw.aspx

HPC Server - Microsoft’s High-Performance Computing version of Windows Server deals not only with large data sets, but with extremely complicated computing requirements. A scale-out architecture and inter-operation with Linux systems, as well as dozens of applications pre-written to work with this server make this a capable “Big Data” system. It is a mature offering, with a long track record of success in scientific, financial and other areas of data processing. It is available both on premises and in Windows Azure, and also in a hybrid of both models, allowing you to “rent” a super-computer when needed. You can read more about it here: http://www.microsoft.com/hpc/en/us/product/cluster-computing.aspx 

Hadoop - Pairing up with Hortonworks, Microsoft has released the Hadoop Open-Source system -  including HDFS and a Map/Reduce standardized software, Hive and Pig - on Windows and the Windows Azure platform. This is not a customized version; off-the-shelf concepts and queries work well here. You can read more about Hadoop here: http://hadoop.apache.org/common/docs/current/ and you can read more about Microsoft’s offerings here: http://hortonworks.com/partners/microsoft/ and here: http://social.technet.microsoft.com/wiki/contents/articles/6204.hadoop-based-services-for-windows.aspx

Windows and Azure Storage - Although not an engine - other than a triple-redundant, immediately consistent commit - Windows Azure can hold terabytes of information and make it available to everything from the R programming language to the Hadoop offering. Binary storage (Blobs) and Table storage (Key-Value Pair) data can be queried across a distributed environment. You can learn more about Windows Azure storage here: http://msdn.microsoft.com/en-us/library/windowsazure/gg433040.aspx 

Analysis

In a “Big Data” environment, it’s not unusual to have a specialized set of tasks for analyzing and even interpreting the data. This is a new field called “data Science”, with a requirement not only for computing, but also a heavy emphasis on math.

Transact-SQL - T-SQL is the dialect of the Structured Query Language used by Microsoft. It includes not only robust selection, updating and manipulating of data, but also analytical and domain-level interrogation as well. It can be used on SQL Server, PDW and ODBC data sources. You can read more about T-SQL here: http://msdn.microsoft.com/en-us/library/bb510741.aspx 

Multidimensional Expressions and Data Analysis Expressions - The MDX and DAX languages allow you to query multidimensional data models that do not fit well with typical two-plane query languages. Pivots, aggregations and more are available within these constructs to query and work with data in Analysis Services. You can read more about MDX here: http://msdn.microsoft.com/en-us/library/ms145506(v=sql.110).aspx and more about DAX here: http://www.microsoft.com/download/en/details.aspx?id=28572 

HPC Jobs and Tasks - Work submitted to the Windows HPC Server has a particular job - essentially a reservation request for resources. Within a job you can submit tasks, such as parametric sweeps and more. You can learn more about Jobs and Tasks here: http://technet.microsoft.com/en-us/library/cc719020(v=ws.10).aspx 

HiveQL - HiveQL is the language used to query a Hive object running on Hadoop. You can see a tutorial on that process here: http://social.technet.microsoft.com/wiki/contents/articles/6628.aspx 

Piglatin - Piglatin is the submission language for the Pig implementation on Hadoop. An example of that process is here: http://blogs.msdn.com/b/avkashchauhan/archive/2012/01/10/running-apache-pig-pig-latin-at-apache-hadoop-on-windows-azure.aspx 

Application Programming Interfaces - Almost all of the analysis offerings have associated API’s - of special note is Microsoft Research’s Infer.NET, a new language construct for framework for running Bayesian inference in graphical models, as well as probabilistic programming. You can read more about Infer.NET here: http://research.microsoft.com/en-us/um/cambridge/projects/infernet/ 

Presentation

Lots of tools work in presenting the data once you have done the primary analysis. In fact, there’s a great video of a comparison of various tools here: http://msbiacademy.com/Lesson.aspx?id=73 Primarily focused on Business Intelligence. That term itself is now not as completely defined, but the tools I’ll show below can be used in multiple ways - not just traditional Business Intelligence scenarios. Application Programming Interfaces (API’s) can also be used for presentation; but I’ll focus here on “out of the box” tools.

Excel - Microsoft’s Excel can be used not only for single-desk analysis of data sets, but with larger datasets as well. It has interfaces into SQL Server, Analysis Services, can be connected to the PDW, and is a first-class job submission system for the Windows HPC Server. You can watch a video about Excel and big data here: http://www.microsoft.com/en-us/showcase/details.aspx?uuid=e20b7482-11c9-4965-b8f0-7fb6ac7a769f and you can also connect Excel to Hadoop: http://social.technet.microsoft.com/wiki/contents/articles/how-to-connect-excel-to-hadoop-on-azure-via-hiveodbc.aspx

Reporting Services - Reporting Services is a SQL Server tool that can query and show data from multiple sources, all at once. It can also be used with Analysis Services. You can read more about Reporting Services here: http://www.microsoft.com/sqlserver/en/us/solutions-technologies/business-intelligence/reporting-services.aspx 

Power View - Power View is a “Self-Service” Business Intelligence reporting tool, which can work with on-premises data in addition to SQL Azure and other data. You can read more about it and see videos of Power View in action here: http://www.microsoft.com/sqlserver/en/us/future-editions/business-intelligence/SQL-Server-2012-reporting-services.aspx 

SharePoint Services - Microsoft has rolled several capable tools in SharePoint as “Services”. This has the advantage of being able to integrate into the working environment of many companies. You can read more about  lots of these reporting and analytic presentation tools here: http://technet.microsoft.com/en-us/sharepoint/ee692578 

This is by no means an exhaustive list - more capabilities are added all the time to Microsoft’s products, and things will surely shift and merge as time goes on. Expect today’s “Big Data” to be tomorrow’s “Laptop Environment”.