About Me

I now work for Microsoft Federal in Chevy Chase, MD.

Dedicated to providing customer-driven, results-focused solutions to the complex business problems of today... and tomorrow.

At SQLTrainer.com, LLC  we understand that the technical challenges faced by businesses today are much greater in both scope and complexity than they have ever been. Businesses today are faced with understanding both local IT infrastructures as well as cloud-based technologies.

What is SQLTrainer.com?

Founded in 1998 by Ted Malone, SQLTrainer.com is a technical consulting, training and content development firm dedicated to the following core principles:

  • Technology Alone is NOT the Answer! - Implementing a particular technology because it is interesting or "cool" will not solve customer problems.
  • Technology Solutions do NOT need to be Overly Complex! - Many times developers and technical practitioners will attempt to build the most clever solution possible. While this serves to stroke the egos of those involved, it doesn't provide a maintainable solution.
  • Consultants Should be Mentors First! - When looking to hire an external consultant, businesses should look to the consultant who's willing to train themselves out of a paycheck.

Why the name, SQLTrainer.com?

SQL (pronounced See-Quell) stands for Structured Query Language, which is at the heart of every modern-day relational database system. Since many technology solutions today rely on some form of database storage or interaction, it was only logical to find a way to incorporate SQL into the name of the organization. Given that one of our core principles is to be a mentor/training above everything, the name SQLTrainer made sense. Since we also wanted to represent our embracing of the cloud, it seemed logical to add the ".com", referring to the biggest "cloud" of them all.

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Tuesday, January 20, 2015 2:32:00 PM

Interested in growing your BI and Big Data skills in 2015? Maybe your new year’s resolution is all about learning something new or taking your analytics knowledge to the next level?

See what BI and Big Data training courses were your peers’ favorites in 2014:

And last but not least, check out the brand new Big Data with the Microsoft Analytics Platform Services.

Learn more about Microsoft's big data solutions or find training opportunities on the Microsoft Virtual Academy.

Monday, December 22, 2014 10:00:00 AM

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. 

Wednesday, December 17, 2014 10:00:00 AM

When you put big data to work, results can be beautiful. Especially when those results are as impactful as saving lives. Here are four best practice examples of how big data is being used in healthcare to improve, and often save, lives.

Aerocrine improves asthma care with near-real-time data

Millions of asthma sufferers worldwide depend on Aerocrine monitoring devices to diagnose and treat their disease effectively. But those devices are sensitive to small changes in ambient environment. That’s why Aerocrine is using a cloud analytics solution to boost reliability. Read more.

Virginia Tech advances DNA sequencing with cloud big data solution

DNA sequencing analysis is a form of life sciences research that has the potential to lead to a wide range of medical and pharmaceutical breakthroughs. However, this type of analysis requires supercomputing resources and Big Data storage that many researchers lack. Working through a grant provided by the National Science Foundation in partnership with Microsoft, a team of computer scientists at Virginia Tech addressed this challenge by developing an on-demand, cloud-computing model using the Windows Azure HDInsight Service. By moving to an on-demand cloud computing model, researchers will now have easier, more cost-effective access to DNA sequencing tools and resources, which could lead to even faster, more exciting advancements in medical research. Read more.

The Grameen Foundation expands global humanitarian efforts with cloud BI

Global nonprofit Grameen Foundation is dedicated to helping as many impoverished people as possible, which means continually improving the way Grameen works. To do so, it needed an ongoing sense of its programs’ performance. Grameen and Microsoft brought people and technology together to create a BI solution that helps program managers and financial staff: glean insights in minutes, not hours; expand services to more people; and make the best use of the foundation’s funding. Read more.

Ascribe transforms healthcare with faster access to information

Ascribe, a leading provider of IT solutions for the healthcare industry, wanted to help clinicians identify trends and improve services by supplying faster access to information. However, exploding volumes of structured and unstructured data hindered insight. To solve the problem, Ascribe designed a hybrid-cloud solution with built-in business intelligence (BI) tools based on Microsoft SQL Server 2012 and Windows Azure. Now, clinicians can respond faster with self-service BI tools. Read more.

Learn more about Microsoft’s big data solutions

Tuesday, December 16, 2014 10:00:00 AM

This blog post was authored by: Matt Usher, Senior PM on the Microsoft Analytics Platform System (APS) team

Microsoft is happy to announce the release of the Analytics Platform System (APS) Appliance Update (AU) 3. APS is Microsoft’s big data in a box appliance for serving the needs of relational data warehouses at massive scale. With this release, the APS appliance supports new scenarios for utilizing Power BI modeling, visualization, and collaboration tools over on premise data sets. In addition, this release extends the PolyBase to allow customers to utilize the HDFS infrastructure in Hadoop for ORC files and directory modeling to more easily integrate non-relational data into their data insights.

The AU3 release includes:

  • PolyBase recursive Directory Traversal and ORC file format support
  • Integrated Data Management Gateway enables query from Power BI to on premise APS
  • TSQL compatibility improvements to reduce migration friction from SQL Server SMP
  • Replatformed to Windows Server 2012 R2 and SQL Server 2014

PolyBase Directory Traversal and ORC File Support

PolyBase is an integrated technology that allows customers to utilize the skillset that they have developed in TSQL for querying and managing data in Hadoop platforms. With the AU3 release, the APS team has augmented this technology with the ability to define an external table that targets a directory structure as a whole. This new ability unlocks a whole new set of scenarios for customers to utilize their existing investments in Hadoop as well as APS to provide greater insight into all of the data collected within their data systems. In addition, AU3 introduces full support for the Optimized Row Column (ORC) file format – a common storage mechanism for files within Hadoop.

As an example of this new capability, let’s examine a customer that is using APS to host inventory and Point of Sale (POS) data in an APS appliance while storing the web logs from their ecommerce site in a Hadoop path structure. With AU3, the customer can simply maintain a structure for their logs in Hadoop in a structure that is easy to construct such as year/month/date/server/log for simple storage and recovery within Hadoop that can then be exposed as a single table to analysts and data scientists for insights.

In this example, let’s assume that each of the Serverxx folders contains the log file for that server on that particular day. In order to surface the entire structure, we can construct an external table using the following definition:

CREATE EXTERNAL TABLE [dbo].[WebLogs]
(
	[Date] DATETIME NULL,
	[Uri] NVARCHAR(256) NULL,
	[Server] NVARCHAR(256) NULL,
	[Referrer] NVARCHAR(256) NULL
)
WITH
(
	LOCATION='//Logs/',
	DATA_SOURCE = Azure_DS,
	FILE_FORMAT = LogFileFormat,
	REJECT_TYPE = VALUE,
	REJECT_VALUE = 100
);

By setting the LOCATION targeted at the //Logs/ folder, the external table will pull data from all folders and files within the directory structure. In this case, a simple select of the data will return data from only the last 10 entries regardless of the log file that contains the data:

SELECT TOP 5
	*
FROM
	[dbo].[WebLogs]
ORDER BY
	[Date]

The results are:

Note: PolyBase, like Hadoop, will not return results from hidden folders or any file that begins with an underscore (_) or period(.).

Integrated Data Management Gateway

With the integration of the Microsoft Data Management Gateway into APS, customers now have a scale-out compute gateway for Azure cloud services to more effectively query sophisticated sets of on-premises data.  Power BI users can leverage PolyBase in APS to perform more complicated mash-ups of results from on-premises unstructured data sets in Hadoop distributions. By exposing the data from the APS Appliance as an OData feed, Power BI is able to easily and quickly consume the data for display to end users.

For more details, please look for an upcoming blog post on the Integrated Data Management Gateway.

TSQL Compatibility improvements

The AU3 release incorporates a set of TSQL improvements targeted at richer language support to improve the types of queries and procedures that can be written for APS. For AU3, the primary focus was on implementing full error handling within TSQL to allow customers to port existing applications to APS with minimal code change and to introduce full error handling to existing APS customers. Released in AU3 are the following keywords and constructs for handling errors:

In addition to the error handling components, the AU3 release also includes support for the XACT_STATE scalar function that is used to indicate the current running transaction state of a user request.

Replatformed to Windows Server 2012 R2 and SQL Server 2014

The AU3 release also marks the upgrade of the core fabric of the APS appliance to Windows Server 2012 R2 and SQL Server 2014. With the upgrade to the latest versions of Microsoft’s flagship server operating system and core relational database engine, the APS appliance takes advantage of the improved networking, storage and query execution components of these products. For example, the APS appliance now utilizes a virtualized Active Directory infrastructure which helps to reduce cost and increase domain reliability within the appliance helping to make APS the price/performance leader in the big data appliance space.

APS on the Web

To learn more about the Microsoft Analytics Platform System, please visit us on the web at http://www.microsoft.com/aps

Tuesday, December 16, 2014 9:30:00 AM

As the end of 2014 nears, now is the perfect time to review IT infrastructure plans for the coming year.  If you haven’t made supportability a key initiative for 2015, there are some important dates that you should know about:

After the end of extended support security updates will no longer be available for these products.  Staying ahead of these support dates will help achieve regulatory compliance and mitigate potential future security risks. That means SQL Server 2005 users, especially those running databases on Windows Server 2003, should make upgrading the data platform an IT priority. 

Security isn’t the only reason to think about upgrading. Here are six benefits to upgrading and migrating your SQL Server 2005 databases before the end of extended support:

  1. Maintain compliance – It will become harder to prove compliance with the latest regulations such as the upcoming PCI DSS 3.0. Protect your data and stay on top of regulatory compliance and internal security audits by running an upgraded version of SQL Server.
  2. Achieve breakthrough performance – Per industry benchmarks, SQL Server 2014 delivers 13x performance gains relative to SQL Server 2005 and 5.5x performance gains over SQL Server 2008.  Customers using SQL Server 2014 can further accelerate mission critical applications with up to 30x transaction performance gains with our new in-memory OLTP engine and accelerate queries up to 100x with our in-memory columnstore. 
  3. Virtualize and consolidate with Windows Server – Scale up on-premises or scale-out via private cloud with Windows Server 2012 R2. Reduce costs by consolidating more database workloads on fewer servers, and increase agility using the same virtualization platform on-premises and in the cloud.
  4. Reduce TCO and increase availability with Microsoft AzureAzure Virtual Machines can help you reduce the total cost of ownership of deployment, management, and maintenance of your enterprise database applications. And, it’s easier than ever to upgrade your applications and achieve high availability in the cloud using pre-configured templates in Azure.
  5. Use our easy on-ramp to cloud for web applications – The new preview of Microsoft Azure SQL Database announced last week has enhanced compatibility with SQL Server that makes it easier than ever to migrate from SQL Server 2005 to Microsoft Azure SQL Database. Microsoft’s enterprise-strength cloud brings global scale and near zero maintenance to database-as-a-service, and enables you to scale out your application on demand.
  6. Get more from your data platform investments - Upgrading and migrating your databases doesn’t have to be painful or expensive. A Forrester Total Economic ImpactTM of Microsoft SQL Server study found a payback period of just 9.5 months for moving to SQL Server 2012 or 2014.

Here are some additional resources to help with your upgrade or migration:

Monday, December 15, 2014 10:00:00 AM

As part of SQL Server’s ongoing interoperability program, we are pleased to announce the general availability of two SQL Server drivers: the Microsoft JDBC Driver for SQL Server and the SQL Server Driver for PHP are now available!

Both drivers provide that robust data access to Microsoft SQL Server and Microsoft Azure SQL Database. The JDBC Driver for SQL Server is a Java Database Connectivity (JDBC) type 4 driver supporting Java Development Kit (JDK) version 1.7. The PHP driver will allow developers who use the PHP scripting language version 5.5 to access Microsoft SQL Server and Microsoft Azure SQL Database, and to take advantage of new features implemented in ODBC 

You can download the JDBC driver here, and download the PHP driver hereWe invite you to explore the latest the Microsoft Data Platform has to offer via a trial evaluation of Microsoft SQL Server 2014, or by trying the new preview of Microsoft Azure SQL Database.

Thursday, December 11, 2014 12:00:00 PM

By Tiffany Wissner, Senior Director, Data Platform

Making it easier for more of our customers to access our latest big data technologies, we are announcing updates to some of our flagship data platform products and services. These updates are part of our approach to make it easier for our customers to work with data of any type and size – using the tools, languages and frameworks they want – in a trusted environment, on-premises and in the cloud. 

Azure SQL Database

Announced last month and available today is a new version of Azure SQL Database that represents a major milestone for this database-as-a-service. With this preview, we are adding near-complete SQL Server engine compatibility, including support for larger databases with online indexing and parallel queries, improved T-SQL support with common language runtime and XML index, and monitoring and troubleshooting with extended events. Internal tests using over 600 million rows of data show query performance improvements up to 5x in the Premium tier of the new preview relative to today’s offering. Continuing on our journey to bring in-memory technologies to the cloud, when applying in-memory columnstore in the new preview, performance is also improved up to 100x.

“From a strategy perspective, these SQL Database service updates are our answer to migrating and working with large data types by leveraging features such as online index rebuild, and partitioning,” said Joe Testa, vice president of Systems Development at Weichert, one of the nation’s leading full-service real estate providers. “Simply put, the results so far have been fantastic—we’re seeing >2x better performance and the advanced features that were only previously available in SQL Server, now make it easier to work with our applications as we continue to migrate our mission-critical apps to Azure.”

These new preview capabilities are offered as part of service tiers introduced earlier this year, which deliver 99.99% availability, larger database sizes, restore and geo-replication capabilities, and predictable performance. When combined with our recently announced elastic scale technologies that scale out to thousands of databases for processing 10s of terabytes of OLTP data and new auditing capabilities, Azure SQL Database service is a clear choice for any cloud-based mission critical application.

Analytics Platform System

As Microsoft’s “big data in a box” solution built with HP, Dell and Quanta, the Analytics Platform System is a data warehousing appliance that supports the ability to query across traditional relational data and data stored in a Hadoop region – either in the appliance or in a separate Hadoop cluster. This latest release includes a data management gateway that establishes a secure connection between on-premises data stored in the Analytics Platform System and Microsoft’s cloud business intelligence and advanced analytics services such as Power BI and Azure Machine Learning. This capability, coupled with PolyBase, a feature of the Analytics Platform System, allows for seamless integration of data stored in SQL Server with data stored in Hadoop. This now enables users of Power BI and Azure Machine Learning to gain insights from Analytics Platform System, whether on-premises or in the Azure cloud.

New Java, PHP and migration tools

Microsoft is also making available new tools and drivers that support greater interoperability with PHP and Java and make it easier for customers to migrate to and use our big data technologies.

Azure DocumentDB is our fully-managed NoSQL document database service with native support for JSON and JavaScript. DocumentDB already includes SDKs for popular languages, including Node.js, Python, .NET, and JavaScript – today we are adding a new Java SDK that will make DocumentDB easier to use within a Java development environment. The SDK provides easy-to-use methods to manage and query DocumentDB resources including collections, stored procedures and permissions. The Java SDK is also available on Github and welcomes community contributions.

Additionally, we are bolstering our SQL Server tools and drivers with updates to the Microsoft JDBC Driver for SQL Server the SQL Server Driver for PHP. Available early next week, these drivers will make it easier for our customers’ applications to access both SQL Server and Azure SQL Database.

For customers that are migrating their IBM DB2 workloads to SQL Server, we are also making available today the SQL Server Migration Assistant (SSMA) tool which automates all aspects of database migration including migration assessment analysis, schema and SQL statement conversion, data migration as well as migration testing to reduce cost and reduce risk of database migration projects. SSMA 6.0 for IBM DB2 automates migrations from IBM DB2 databases to SQL Server and Azure SQL Database and is free to download and use. Support for IBM DB2 is in addition to earlier updates to SSMA 6.0 including migration support for larger Oracle databases.

Microsoft data platform

These new updates will enable more customers to use Microsoft’s data platform to build, extend and migrate more applications. Microsoft’s data platform includes all the building blocks customers need to capture and manage all of their data, transform and analyze that data for new insights, and provide tools which enable users across their organization to visualize data and make better business decisions. To learn more, go here

Monday, December 8, 2014 10:50:04 AM

Have you been watching Data Exposed over on Channel 9? If you’re a data developer, Data Exposed is a great place to learn more about what you can do with data: relational and non-relational, on-premises and in the cloud, big and small.

On the show, Scott Klein and his guests demonstrate features, discuss the latest news, and share their love for data technology – from SQL Server, to Azure HDInsight, and more!

We rounded up the year’s top 10 most-watched videos from Data Exposed. Check them out below – we hope you learn something new!

  • Introducing Azure Data Factory: Learn about Azure Data Factory, a new service for data developers and IT pros to easily transform raw data into trusted data assets for their organization at scale.
  • Introduction to Azure DocumentDB: Get an introduction to Azure DocumentDB, a NoSQL document database-as-a-service that provides rich querying, transactional processing over schema free data, and query processing and transaction semantics that are common to relational database systems.
  • Introduction to Azure Search: Learn about Azure Search, a new fully-managed, full-text search service in Microsoft Azure which provides powerful and sophisticated search capabilities to your applications.
  • Azure SQL Database Elastic Scale: Learn about Azure SQL Database Elastic Scale, .NET client libraries and Azure cloud service packages that provide the ability to easily develop, scale, and manage the stateful data tiers of your SQL Server applications.
  • Hadoop Meets the Cloud: Scenarios for HDInsight: Explore real-life customer scenarios for big data in the cloud, and gain some ideas of how you can use Hadoop in your environment to solve some of the big data challenges many people face today.
  • Azure Stream Analytics: See the capabilities of Azure Stream Analytics and how it helps make working with mass volumes of data more manageable.
  • The Top Reasons People Call Bob Ward: Scott Klein is joined by Bob Ward, Principle Escalation Engineer for SQL Server, to talk about the top two reasons why people want to talk to Bob Ward and the rest of his SQL Server Services and Support team.
  • SQL Server 2014 In-Memory OLTP Logging: Learn about In-Memory OLTP, a memory-optimized and OLTP-optimized database engine integrated into SQL Server. See how transactions and logging work on memory-optimized-tables, and how a system can recover in-memory data in case of a system failure.
  • Insights into Azure SQL Database: Get a candid and insightful behind-the-scenes look at Azure SQL Database, the new service tiers, and the process around determining the right set of capabilities at each tier.
  • Using SQL Server Integration Services to Control the Power of Azure HDInsight: Join Scott and several members of the #sqlfamily to talk about how to control cloud from on-premises SQL Server.

Interested in taking your learning to the next level? Try SQL Server or Microsoft Azure now.

Thursday, December 4, 2014 10:00:00 AM

Historically, Hadoop has been a platform for big data that you either deploy on-premises with your own hardware or in the cloud and managed by a hosting vendor. Deploying on-premises affords you specific benefits, like control and flexibility over your deployment.  But the cloud provides other benefits like elastic scale, fast time to value, and automatic redundancy, amongst others.

With the recent announcement of the Hortonworks Data Platform 2.2 being made generally available, Microsoft and Hortonworks are partnered to deliver Hadoop on Hybrid infrastructure in both on-premises and cloud.  This will give customers the best of both worlds with control & flexibility of on-premises deployments and the elasticity & redundancy of the cloud.

What are some of the top scenarios or use cases for Hybrid Hadoop? And what are the benefits of taking advantage of a hybrid model?

  • Elasticity: Easily scale out during peak demand times by quickly spinning up more Hadoop nodes (with HDInsight)
  • Reliability: Use the cloud as an automated disaster recovery solution that automatically geo-replicates your data. Or
  • Breadth of Analytics Offerings: If you’re already working with on-prem Hortonworks offerings, you now have access to a suite of turn-key data analytics and management services in Azure, like HDInsight, Machine Learning, Data Factory, and Stream Analytics.

To get started, customers need Hortonworks Data Platform 2.2 with Apache Falcon configured to move data from on-premises into Azure.  Detailed instructions can be found here.

We are excited to be working with Hortonworks to give Hadoop users Hadoop/Big Data on a hybrid cloud. For more resources:

Wednesday, November 19, 2014 10:00:00 AM

To allow developers in Visual Studio to more easily incorporate the benefits of “big data” with their custom applications, Microsoft is adding a deeper tooling experience for HDInsight in Visual Studio in the most recent version of the Azure SDK. This extension to Visual Studio helps developers to visualize their Hadoop clusters, tables and associated storage in familiar and powerful tools. Developers can now create and submit ad hoc Hive queries for HDInsight directly against a cluster from within Visual Studio, or build a Hive application that is managed like any other Visual Studio project.

Download the Azure SDK now for VS 2013 | VS 2012 | VS 2015 Preview.

Integration of HDInsight objects into the “Server Explorer” brings your Big Data assets onto the same page as other cloud services under Azure. This allows for quick and simple exploration of clusters, Hive tables and their schemas, down to querying the first 100 rows of a table.  This helps you to quickly understand the shape of the data you are working with in Visual Studio.

Also, there is tooling to create Hive queries and submit them as jobs. Use the context menu against a Hadoop cluster to immediately begin writing Hive query scripts. In the example below, we create a simple query against a Hive table with geographic info to find the count of all countries and sort them by country. The Job Browser tool helps you visualize the job submissions and status.  Double click on any job to get a summary and details in the Hive Job Summary window. 

You can also navigate to any Azure Blob container and open it to work with the files contained there. The backing store is associated with the Hadoop cluster during cluster creation in the Azure dashboard. Management of the Hadoop cluster is still performed in the same Azure dashboard.

For more complex script development and lifecycle management, you can create Hive projects within Visual Studio. In the new project dialog (see below) you will find a new HDInsight Template category. A helpful starting point is the Hive Sample project type. This project is pre-populated with a more complex Hive query and sample data for the case of processing web server logs.

To get started visit the Azure HDInsight page to learn about Hadoop features on Azure. 

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