Category Archives: cloud

Data Science Perspectives: Q&A with Microsoft Data Scientists Val Fontama and Wee Hyong Tok

You can’t read the tech press without seeing news of exciting advancements or opportunities in data science and advanced analytics. We sat down with two of our own Microsoft Data Scientists to learn more about their role in the field, some of the real-world successes they’ve seen, and get their perspective on today’s opportunities in these evolving areas of data analytics.

If you want to learn more about predictive analytics in the cloud or hear more from Val and Wee Hyong, check out their new book, Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes.

First, tell us about your roles at Microsoft?

 [Val] Principal Data Scientist in the Data and Decision Sciences Group (DDSG) at Microsoft

 [Wee Hyong] Senior Program Manager, Azure Data Factory team at Microsoft

 And how did you get here? What’s your background in data science?

[Val] I started in data science over 20 years ago when I did a PhD in Artificial Intelligence. I used Artificial Neural Networks to solve challenging engineering problems, such as the measurement of fluid velocities and heat transfer. After my PhD, I applied data mining in the environmental science and credit industry: I did a year’s post-doctoral fellowship before joining Equifax as a New Technology Consultant in their London office. There, I pioneered the application of data mining to risk assessment and marketing in the consumer credit industry. I hand coded over ten machine learning algorithms, including neural networks, genetic algorithms, and Bayesian belief networks in C++ and applied them to fraud detection, predicting risk of default, and customer segmentation.    

[Wee Hyong] I’ve worked on database systems for over 10 years, from academia to industry.  I joined Microsoft after I completed my PhD in Data Streaming Systems. When I started, I worked on shaping the SSIS server from concept to release in SQL Server 2012. I have been super passionate about data science before joining Microsoft. Prior to joining Microsoft, I wrote code on integrating association rule mining into a relational database management system, which allows users to combine association rule mining queries with SQL queries. I was a SQL Server Most Valuable Professional (MVP), where I was running data mining boot camps for IT professionals in Southeast Asia, and showed how to transform raw data into insights using data mining capabilities in Analysis Services.

What are the common challenges you see with people, companies, or other organizations who are building out their data science skills and practices?

[Val] The first challenge is finding the right talent. Many of the executives we talk to are keen to form their own data science teams but may not know where to start. First, they are not clear what skills to hire – should they hire PhDs in math, statistics, computer science or other? Should the data scientist also have strong programming skills? If so, in what programming languages? What domain knowledge is required? We have learned that data science is a team sport, because it spans so many disciplines including math, statistics, computer science, etc. Hence it is hard to find all the requisite skills in a single person. So you need to hire people with complementary skills across these disciplines to build a complete team.

The next challenge arises once there is a data science team in place – what’s the best way to organize this team? Should the team be centralized or decentralized? Where should it sit relative to the BI team? Should data scientists be part of the BI team or separate? In our experience at Microsoft, we recommend having a hybrid model with a centralized team of data scientists, plus additional data scientists embedded in the business units. Through the embedded data scientists, the team can build good domain knowledge in specific lines of business. In addition, the central team allows them to share knowledge and best practices easily. Our experience also shows that it is better to have the data science team separate from the BI team. The BI team can focus on descriptive and diagnostic analysis, while the data science team focuses on predictive and prescriptive analysis. Together they will span the full continuum of analytics.

The last major challenge I often hear about is the actual practice of deploying models in production. Once a model is built, it takes time and effort to deploy it in production. Today many organizations rewrite the models to run on their production environments. We’ve found success using Azure Machine Learning, as it simplifies this process significantly and allows you to deploy models to run as web services that can be invoked from any device.

[Wee Hyong] I also hear about challenges in identifying tools and resource to help build these data science skills. There are a significant number of online and printed resources that provide a wide spectrum of data science topics – from theoretical foundations for machine learning, to practical applications of machine learning. One of the challenges is trying to navigate amongst the sea of resources, and selecting the right resources that can be used to help them begin.

Another challenge I have seen often is identifying and figuring out the right set of tools that can be used to model the predictive analytics scenario. Once they have figured out the right set of tools to use, it is equally important for people/companies to be able to easily operationalize the predictive analytics solutions that they have built to create new value for their organization.

What is your favorite data science success story?

[Val] My two favorite projects are the predictive analytics projects for ThyssenKrupp and Pier 1 Imports. I’ll speak today about the Pier 1 project. Last spring my team worked with Pier 1 Imports and their partner, MAX451, to improve cross-selling and upselling with predictive analytics. We built models that predict the next logical product category once a customer makes a purchase. Based on Azure Machine Learning, this solution will lead to a much better experience for Pier 1 customers.

[Wee Hyong] One of my favorite data science success story is how OSIsoft collaborated with the Carnegie Mellon University (CMU) Center for Building Performance and Diagnostics to build an end-to-end solution that addresses several predictive analytics scenarios. With predictive analytics, they were able to solve many of their business challenges ranging from predicting energy consumption in different buildings to fault detection. The team was able to effectively operationalize the machine learning models that are built using Azure Machine Learning, which led to better energy utilization in the buildings at CMU.

What advice would you give to developers looking to grow their data science skills?
[Val] I would highly recommend learning multiple subjects: statistics, machine learning, and data visualization. Statistics is a critical skill for data scientists that offers a good grounding in correct data analysis and interpretation. With good statistical skills we learn best practices that help us avoid pitfalls and wrong interpretation of data. This is critical because it is too easy to unwittingly draw the wrong conclusions from data. Statistics provides the tools to avoid this. Machine learning is a critical data science skill that offers great techniques and algorithms for data pre-processing and modeling. And last, data visualization is a very important way to share the results of analysis. A good picture is worth a thousand words – the right chart can help to translate the results of complex modeling into your stakeholder’s language. So it is an important skill for a budding data scientist.

[Wee Hyong] Be obsessed with data, and acquire a good understanding of the problems that can be solved by the different algorithms in the data science toolbox. It is a good exercise to jumpstart by modeling a business problem in your organization where predictive analytics can help to create value. You might not get it right in the first try, but it’s OK. Keep iterating and figuring out how you can improve the quality of the model. Over time, you will see that these early experiences help build up your data science skills.

Besides your own book, what else are you reading to help sharpen your data science skills?

[Val] I am reading the following books:

  • Data Mining and Business Analytics with R by Johannes Ledolter
  • Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) by Ian H. Witten, Eibe Frank, and Mark A. Hall
  • Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die by Eric Siegel

[Wee Hyong] I am reading the following books:

  • Super Crunchers: Why Thinking-By-Numbers Is the New Way to Be Smart by Ian Ayres
  • Competing on Analytics: The New Science of Winning by Thomas H. Davenport and Jeanne G. Harris.

Any closing thoughts?

[Val]  One of the things we share in the book is that, despite the current hype, data science is not new. In fact, the term data science has been around since 1960. That said, I believe we have many lessons and best practices to learn from other quantitative analytics professions, such as actuarial science. These include the value of peer reviews, the role of domain knowledge, etc. More on this later.

[Wee Hyong] One of the reasons that motivated us to write the book is we wanted to contribute back to the data science community, and have a good, concise data science resource that can help fellow data scientists get started with Azure Machine Learning. We hope you find it helpful. 

Rainbow in the Cloud

Cloud tecnologies burst into our lives without asking our permission. 
At the beginning many of us thought the only thing that it brings would be the thunder and the rain. 
Time shows that, instead, it might even bring some color to our skies.
Today, on a first day Keynote of the 16th PASS Summit, Microsoft representatives 
 -Corporate VP Data Platform, Cloud & Enterprize T.K. “Ranga” Rengarajan , 
– General Manager of Power BI James Phillips 
– Corporate VP Information Management &Machine Learning Joseph Sirosh 
took stage one after another talking about the Microsoft Data Platform and what it can offer in the cloud space.
They have talked about the explosion of all kinds of data devices.  Those devices produce enourmous amouts of data. Many also consume enourmous amounts of data.
This data is changing the way we work, the way we do business, and the way we live. We all depend on data to make decisions.
Microsoft Data Platform allows us to do more. To achive more. In theory, i might add. Just because I had no chance to try their new products myself.
Microsot Data Platform has all kind of tecnologies that are used by many mission critical environments around the world.
Tecnologies based on the structured data & tecnologies based on unstructured data.
In memory & on disk data processing. 
Products that can scale up & products that can scale out. 
Technologies that can store data & tecnologies that only procees the data. 
Some can live on premises & some can live only in the cloud.
Microsoft Data Platform in the cloud has a lot to offer. 
Key/value pairs management using Microsoft Azure Tables ( http://msdn.microsoft.com/en-us/library/azure/jj553018.aspx )
Document management, schema free, write optimized plaform using new highly Scalable Azure DocumentDB ( http://azure.microsoft.com/en-us/services/documentdb/ )
Relational database-as-a-service ( http://azure.microsoft.com/en-us/services/sql-database/ )
File system computation using Azure HDinsight, which both process data and store it in the cloud ( http://azure.microsoft.com/en-us/services/hdinsight/ )
Json based full indexing using Azure Search ( http://azure.microsoft.com/en-us/services/search/ )
Broser-based data orchestration by Azure Data Factory ( http://azure.microsoft.com/en-us/services/data-factory/ )
Prediction models tecnology by Azure Stream Analytics and Azure Machine Learning. (http://azure.microsoft.com/en-us/services/machine-learning/)
 
 PowerBI.com dashboards used in several demos today were very impressive with all its interactive and colorful reports. 
Loud music contributed to the great spirits of 3,941 attendees from 56 countries and 2,000 companies. Huge screens thanked all  community volunteers, chapter leaders and sponsors.
The World is excited about data.  Microsoft are excited about their data platform. I am excited to be at the PASS summit. 
Yours,
Maria

Azure previews fully-managed NoSQL database and search services

I am pleased to announce previews of new NoSQL database and search services and the evolution of our Hadoop-based service. Available as previews today are Azure DocumentDB, a fully-managed transactional NoSQL document database-as-a-service, and Azure Search, which enables developers to easily add search capabilities to mobile and cloud applications. Generally available today, Azure HDInsight, our Hadoop-based solution for the cloud, now supports Apache HBase clusters.

With these new and updated services, we’re continuing to make it easier for customers to work with data of any type and size – using the tools, languages and frameworks they want to — in a trusted cloud environment. From Microsoft products like Azure Machine Learning, Azure SQL Database and Azure HDInsight to data services from our partners, we’re committed to supporting the broadest data platform so our customers get data benefits, in the cloud, on their terms.

Preview of Azure DocumentDB

Applications today must support multiple devices, multiple platforms with rapid iterations from the same data source, and also deliver high-scale and reliable performance. NoSQL has emerged as the leading database technology to address these needs. According to Gartner inquiries, flexible data schemas and application development velocity are cited as primary factors influencing adoption. Secondary factors attracting enterprises are global replication capabilities, high performance and developer interest.*

However, while NoSQL technologies address some document database needs, we’ve been hearing feedback that customers want a way to bridge document database functionality with the transactional capabilities of relational databases. Azure DocumentDB is our answer to that feedback – it’s a NoSQL document database-as-a-service that provides the benefits of a NoSQL document database but also adds the query processing and transaction semantics common to relational database systems.

Built for the cloud, Azure DocumentDB natively supports JSON documents enabling easy object mapping and iteration of data models for application development. Azure DocumentDB offers programming libraries for several popular languages and platforms, including.Net, Node.js, JavaScript, and Python. We will be contributing the client libraries to the open source community, so they can incorporate improvements into the versions published on Azure.com.

One DocumentDB customer, Additive Labs, builds online services to help their customers move to the cloud. "DocumentDB is the NoSQL database I am expecting today,” said Additive Labs Founder Thomas Weiss. “The ease and power of SQL-like queries had me started in a matter of minutes. And the ability to augment the engine’s behavior with custom JavaScript makes it way easier to adapt to our customers’ new requirements.”

Preview of Azure Search

Search has become a natural way for users to interact with applications that manage volumes of data.  However, managing search infrastructure at scale can be difficult and time consuming and often requires specialized skills and knowledge. Azure Search is a fully-managed search-as-a-service that customers can use to integrate complete search experiences into applications and connect search results to business objectives through fine-tuned, ranking profiles. Customers do not have to worry about the complexities of full-text search or deploying, maintaining or managing a search infrastructure.

With Azure Search developers can easily provision a search service, quickly create and tune one or more indexes, upload data to be indexed and start issuing searches. The service offers a simple API that’s usable from any platform or development environment and makes it easy to integrate search into new or existing applications. With Azure Search, developers can use the Azure portal or management APIs to increase or decrease capacity in terms of queries per second and document count as load changes, delivering a more cost effective solution for search scenarios. 

Retail platform provider Xomni is already using Azure Search to help the company manage its cloud infrastructure. "We have the most technically advanced SaaS solution for delivering product catalogue data to the retail industry in the market today,” said Xomni CTO Daron Yondem. “Integrating Azure Search into our platform will help solidify our leadership as datasets and faceted search requirements evolve over time."

General availability of Apache HBase for HDInsight

In partnership with Hortonworks, we’ve invested in the Hadoop ecosystem through contributions across projects like Tez, Stinger and Hive. Azure HDInsight, our Hadoop-based service, is another outcome of that partnership.

Azure HDInsight combines the best of Hadoop open source technology with the elasticity and manageability that enterprises require. Today, we’re making generally available HBase as a managed cluster inside HDInsight. HBase clusters are configured to store data directly in Azure Blob storage. For example, customers can use HDInsight to analyze large datasets in Azure Blobs generated from highly-interactive websites or can use it to analyze sensor and telemetry data from millions of end points.

Microsoft data services

Azure data services provide unparalleled choice for businesses, data scientists, developers and IT pros with a variety of managed services from Microsoft and our partners that work together seamlessly and connect to our customers’ data platform investments– from relational data to non-relational data, structured data to unstructured data, constant and evolving data models. I encourage you to try out our new and expanded Azure data services and let us know what you think.

*Gartner, Hype Cycle for Information Infrastructure, 2014, Mark Beyer and Roxane Edjlali, 06 August 2014

 

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Microsoft Next: Välkommen till Microsofts huvudkontor 27-28 november

Den 27-28 november bjuder vi in dig till vår hemmaplan med tvådagarskonferensen Microsoft Next. Ny teknik förändrar hur vi jobbar och utmanar våra tankesätt. Vilken roll får kontoret när vi inte längre behöver vara där? Vad kommer morgondagens medarbetare att kräva av sin arbetsgivare? Det Nya Arbetslivet handlar lika mycket om ledarskap och kultur som de tekniska lösningarna.

Eventets första dag riktar sig till tekniska beslutsfattare. Under dagen presenterar och demonstrerar vi rykande färska produkter, tjänster och tekniker inom affärsområdet Server & Cloud. Produkter och tjänster som nyss lanserats och som är del av dagen är Windows Server 2012 R2, System Center Server 2012 R2, Windows Azure, Windows Intune och SQL Server 2014.

Under eventets andra dag vänder vi oss till dig som ansvarar för marknadsföring, försäljning, HR eller ekonomi. Vi bjuder på spännande talare, inspirerande sessioner och relevanta kundcase. Du kommer också få tillfälle att göra ett studiebesök med rundvisning på vårt kontor i Akalla där vi visar hur vi på Microsoft har skapat vår vision kring Det Nya Arbetslivet. Ett tillfälle till ny värdefull kunskap, inspiration och erfarenhetsutbyte som du inte får missa.

AGENDA ONSDAG 27 NOVEMBER – NYA LÖSNINGAR INOM SERVER & CLOUD

  • Modern Datacenter – För dig som jobbar med infrastruktur och vill lära dig mer om hybrid-moln baserade på Hyper-V, Windows Server 2012R2 och System Center Server R2.
  • People-Centric IT – ”Any Device – Anywhere…”, möjliggör arbete från vilken plats som helst med full säkerhet och managering av IT över Windows, iOS och Android via bland annat Windows Intune. Vi går också igenom nyheter i RDS/VDI i Windows Server 2012 R2.
  • Data Insights – ”Any Data – Any Size…”, oavsett typ av data och hur stora datamängder du har så finns det en lösning lokalt eller i molnet. Hadoop, SQL Server och Power BI är del av det vi går igenom.
  • Modern Apps – Med Windows Azure, Visual Studio 2013 och SQL Server 2014 får du helt nya möjligheter att utveckla applikationer med hög prestanda och kvalitet som spänner över alla typer av klienter. ALM i molnet (Visual Studio Online) eller lokalt (TFS) oavsett om applikationerna är skrivna i .NET, Java eller andra programmeringsspråk.

 AGENDA TORSDAG 28 NOVEMBER – DET NYA ARBETSLIVET

  • Per Schlingmann, varumärkesstrateg & kommunikationsrådgivare och en av hjärnorna bakom Nya Moderaterna, ger föreläsningen Att förändras är att vinna (och ha roligt på vägen dit). De verksamheter som har modet och förmågan att gå före och förändra och förnya sig är de som blir vinnarna – men hur går det till?
  • Hur skapar man en social företagskultur? Brit Stakston, kommunikationsstrateg och expert på sociala medier, talar i sin föreläsning Det sociala företaget – det sociala ledarskapet om hur ledare kan stötta den sociala utvecklingen och vända det till en fördel.
  • Missa inte heller chansen att besöka Sveriges Bästa Arbetsplats bakom kulisserna. Under dagen får du möjlighet att hänga med på rundvandringar på vårt kontor där vi visar hur vi på Microsoft har realiserat Det Nya Arbetslivet.
  • Vi avlutar dagen med Leo Razzak, 26 år gammal och sommarpratare 2013  – en fantastisk inspiratör och en av Sveriges mest anlitade föreläsare inom jämställdhets- och integrationsfrågor – en energikick ni inte får missa! 
Konferensen är kostnadsfri, anmäl dig här idag!

Small-Footprint Laptop Lab

Champagne Dev VMs on a Beer Budget Others have posted on this very topic, but I rebuild my home/dev VM lab this past weekend, and it went so well I thought I’d share the steps I went through, in case it helps someone else to get started. I got my start doing this a while back with a very helpful post by Jonathan Kehayias on Virtualbox, but I have evolved my home lab since. As I see it, today you have two choices for this sort of work A cloud service like Azure or Amazon, etc. A small, local VM farm…(read more)

It’s About Delivering Value, Stupid

James Carville coined the phrase, “ It’s the economy, stupid ,” as part of Bill Clinton’s 1992 US Presidential campaign. I’m not fond of the “stupid” part. But I have also never successfully advised a governor to become President of the United States by unseating an incumbent President who enjoyed a 90% approval rating earlier in his presidency. I am fond of delivering value. In fact, that’s a major tenet of Linchpin People ’s culture: Deliver Value. It’s Raining Value There’s value in the cloud….(read more)

[New England] Mark Souza on Big Data and Cloud at NESQL

This Thursday, January 17, at New England SQL Server we’ll be featuring Mark Souza , General Manager of the Data Platform Group at Microsoft. Most of you are probably familiar with that name; he’s the guy who founded the CAT team , and he’s been in a number of key roles in the SQL Server organization for the past several years. Mark’s topic for Thursday is Big Data and Cloud at Microsoft . The talk should be an interesting look into how the company is approaching these key areas. If you’re in New…(read more)

The Perfect Combination: SQL Server 2012, Windows Server 2012 and System Center 2012

Delivering a Complete Data Platform for the Modern Datacenter with Cloud OS

Today’s organizations need the ability to seamlessly build, deploy and manage applications and services across on-premise and cloud computing environments. The Cloud OS platform with Windows Server® 2012, Windows Azure, Microsoft® SQL Server® 2012, Microsoft System Center 2012 and Visual Studio 2012 work together to provide a consistent platform from on-premises to cloud computing environments.  For database applications, we have identified 3 (three) important scenarios where customers will benefit with the Cloud OS platform:

  1. Tackling mission critical OLTP workload SLAs and performance requirements
  2. Revolutionizing enterprise data warehousing
  3. Migrating large mission critical SQL Server workloads into Microsoft private cloud

For non-virtualized environments in an on-premises data center, Windows Server 2012 and SQL Server 2012 provide the best platform for mission-critical workloads in these areas:

    • Performance & Scalability:  SQL Server 2012 can consume the operating system maximum for both processors and memory.  Windows Server 2012 supports logical 640 processors (cores) over a max of 64 sockets and up to 4 TB of RAM, allowing SQL Server applications to scale to meet the demand of most mission critical applications. The new NIC Teaming feature in Windows Server 2012 allows 2 or more network adapters to behave as a single, virtual device.  This improves the reliability of the networking subsystem – if one NIC dies, the other continues to function – and allows the bandwidth available to each to be pooled for greater total network throughput for SQL Server data. With SMB improvements in Windows Server 2012, SQL Server can store database files on remote (SMB) file shares, providing customers with many more deployment options for their database server storage. The new data de-duplication feature in Windows Server 2012 provides compression on steroids and delivers 30-90% storage savings for FILESTREAM BLOBs and other external files in SQL Server applications.
    • Availability:  SQL Server 2012 support for Windows Server Core is expected to eliminate the need for 50-60% of the OS-level patches.  With Windows Server 2012, the server admin can configure the SQL Server to run with full support for graphical interfaces and then switch to run in Server Core mode. Cluster Aware Updating automates SQL Server cluster node maintenance, making the process easier, faster, more consistent and more reliable with significantly less downtime. With dynamic quorum management, the cluster can dynamically reconfigure itself to keep running down to the last surviving node to allow a SQL Server AlwaysOn cluster to adjust the number of quorum votes dynamically that are required to keep running, while simplifying set up by as much as 80%.

Organizations are also seeking a cloud-optimized IT infrastructure that can span from a private cloud behind your firewall to a public cloud behind a service provider’s firewall.  One key element to achieving this is having a common virtualization platform across private and public clouds.  This increases efficiency and performance across infrastructures, which is essential for database applications. Windows Server 2012 offers the best virtualization platform for SQL Server 2012. By working together, SQL Server 2012, Windows Server 2012, and System Center 2012 offer a seamlessly integrated, on-premise and cloud-ready information platform to meet the demands of today’s enterprise.  We have just published a white paper on the detailed benefits on this integration. Key benefits include:

    • Better Scalability: Higher capacity vCPUs (up to 64), memory (up to 1 TB), and VM density (up to 8,000 per cluster)
    • Better Performance: Hyper-V support on NUMA and fiber channel
    • Better Availability: Faster & simultaneous live migration and dynamic quorum support in SQL Server AlwaysOn cluster
    • Better Manageability: Same management tool (System Center) for SQL Server virtual machines in both private and public cloud

We have also published the latest performance report for SQL Server 2012 running on Windows Server 2012 Hyper-V. Key points from the performance report include:

    • With Windows Server 2012 Hyper-V’s new support for up to 64 vCPUs, ESG Lab took an existing SQL Server 2012 OLTP workload that was previously vCPU limited and increased the performance by six times, while the average transaction response times improved by five times.
    • Manageably-low Hyper-V overhead of 6.3% was recorded when comparing SQL Server 2012 OLTP workload performance of a physical server to a virtual machine configured with the same number of virtual CPU cores and the same amount of RAM.

When compared to VMware vSphere 5.1, Windows Server 2012 Hyper-V offers a number of advantages for SQL Server workloads:

    • Performance & Scalability: Windows Server 2012 Hyper-V is better equipped to deploy mission critical SQL Server workloads in virtualized environment, allowing up to 64 virtual processors per VM with no SKU-specific restrictions. By contrast, the free vSphere Hypervisor, along with vSphere 5.1 Essentials, Essentials Plus and Standard editions support only 8 vCPUs per VM, with vSphere 5.1 Enterprise supporting 32vCPUs and only the most expensive edition, vSphere 5.1 Enterprise Plus, allows support up to 64 vCPUs. No such SKU-specific restrictions are in place with Hyper-V. Hyper-V offers superior performance for SQL Server virtualization, supporting 320 logical processors per host, whilst vSphere 5.1 supports just half that number, restricting scalability and density. Hyper-V also supports up to 4TB of host physical memory, with an individual VM able to utilize up to 1TB of memory. Compared with VMware, where the vSphere Hypervisor host physical memory is capped at 32GB and 2TB for vSphere 5.1 Enterprise Plus.
    • Storage & High Availability: For the mission critical SQL Server AlwaysOn scenario that makes use of Windows Server Failover Clustering (WSFC), customers retain full Hyper-V functionality, whereas when virtualizing Windows Server based clusters, VMware recommends turning off key features such as vMotion for VM mobility, DRS for dynamic resource allocation, Memory Overcommit, meaning sacrificed density, and finally, vSphere Fault Tolerance (FT). Also, when using Fiber Channel for Guest Clusters, VMware restrict scale to just 5 nodes. No such restriction applies with Hyper-V, with unmatched scale for failover clustering, with support for up to 64 nodes and 8,000 VMs per cluster. Hyper-V Live Migration also offers unlimited simultaneous Live Migrations and Shared-Nothing Live Migration for seamlessly moving VMs between hosts and clusters. Additionally, Hyper-V fully supports Guest Clustering with Live Migration and Dynamic memory, unlike VMware. On storage, Hyper-V is optimized to take advantage of increased capacity of single virtual disks to store huge databases, file repositories or document archives of up to 64TB in size, while vSphere is restricted to only 2TB per virtual disk. Hyper-V also supports the latest hardware innovations such as 4K Advanced Format Disks, which comes with higher capacities, better alignment and resiliency, and ultimately, higher performance. vSphere unfortunately, doesn’t support this new innovation in hardware.
    • Deployment & Management: Hyper-V, combined with System Center, supports VM migration and management from private (behind your firewall) to public cloud (behind service provider’s firewall) through a single pane of glass. This provides organizations with unparalleled levels of flexibility. Additionally, System Center not only supports Hyper-V, but also VMware vSphere and Citrix XenServer based infrastructures. Hyper-V, combined with System Center also provides complete infrastructure monitoring (hardware, hypervisor, operating system, and applications) which is especially useful for deploying, optimizing and monitoring the ongoing performance of workloads such as SQL Server. With VMware, customers are required to purchase expensive additional products to deliver any form of monitoring beyond the standard virtual machine metrics.
    • Lower costs: Hyper-V provides a significantly lower total cost of ownership (TCO) than VMware vSphere for initial licensing and ongoing operations. More details on the cost comparison can be obtained through this web site where the analysis shows that a VMware private cloud solution can cost 5.5 times more than a Microsoft based private cloud solution.

Hyper-V proves to be the best solution for virtualizing SQL Server databases, with superior capabilities in many areas, whilst offering significantly better TCO than VMware. Many customers understand the benefits outlined in the summary and they have chosen to run their SQL Servers using Hyper-V or have switched their existing SQL Server to Hyper-V from VMware. See these case studies for more details.

Microsoft’s Cloud OS platform consisting of SQL Server 2012, Windows Server 2012, System Center 2012, Windows Azure, and Visual Studio 2012 offer a unique and consistent platform, from on-premises, to cloud computing environments, to help organizations modernize their datacenters by leveraging the CAPEX and OPEX efficiencies that cloud computing environments provide. Customers should consider using this platform by trying SQL Server 2012, Windows Server 2012, System Center 2012, Windows Azure, and Visual Studio 2012.

Crutchfield Turns to Microsoft and EMC to Help Transform SQL Server to the Private Cloud

When it comes to consumer electronics gear, audio and video enthusiasts rely on Crutchfield Corporation for excellent customer service and stellar product know-how. Crutchfield powers its information-based service with a wide range of tools for its website visitors, customers and internal customer advisors. To keep improving their stellar customer service, Crutchfield develops most of its line of business applications in-house – many leveraging SQL Server. In recent years, an expanding set of applications led to rampant data and server growth in its data center. 

To address this challenge, Crutchfield looked to EMC and Microsoft technologies. Already a user of Microsoft technologies, Crutchfield was able to virtualize 75% of its EMC storage infrastructure using Microsoft Windows Server Hyper-V.  

Using EMC and Microsoft technologies, Crutchfield was able to:

  • Save a total of $500,000 through virtualization using Windows Server Hyper-V
  • Drive applications to market 20% faster
  • Decrease SQL Server disk read times and latency from 5 – 10 milliseconds to less than one millisecond
  • Improve storage utilization from 40 to 80 percent.

Watch Crutchfield Information Systems Manager of Enterprise Storage Craig VanHuss discuss how the company worked with EMC and Microsoft to transform its Microsoft applications, speeding performance and increasing efficiency, in this video.