The public preview of the next major release of SQL Server brings the power of SQL Server to both Linux and Windows. SQL Server enables developers and organizations to build intelligent applications with industry-leading performance and security technologies using their preferred language and environment. With the next release of SQL Server, you can develop applications with SQL Server on Linux, Windows, Docker, or macOS (via Docker) and then deploy to Linux, Windows, or Docker, on-premises or in the cloud.
You’ll find native Linux installations made easy with familiar RPM and APT packages for Red Hat Enterprise Linux and Ubuntu Linux, and a package for SUSE Linux Enterprise Server will be coming soon as well. Finally, the public preview for SQL Server is also available on Azure Virtual Machines on Windows and Linux and as images available on Docker Hub, offering a quick and easy installation within minutes.
Tooling on Linux
We have also released updated versions of our flagship SQL Server tools including SQL Server Management Studio (SSMS), Visual Studio SQL Server Data Tools (SSDT) and SQL Server PowerShell with support for the next release of SQL Server on Windows and Linux. We are also excited to announce the new SQL Server extension for Visual Studio Code that is available now on the Visual Studio Code marketplace. Developers can use the SQL extension for VS Code on macOS/Linux/Windows with SQL Server running anywhere (on-premises, on Linux and Windows, in any cloud, in virtual machines, Docker) and with Azure SQL DB and Azure SQL DW. Native command-line tools are also available for SQL Server on Linux.
Get started today
Try the SQL Server on Linux Public Preview today! Get started with the public preview of the next release of SQL Server on Linux, macOS (via Docker) and Windows with our tutorials that show you how to install and use SQL Server on macOS, Docker, Windows, RHEL and Ubuntu and quickly build an app in a programming language of your choice.
Create a SQL Server on Linux virtual machine in Azure (coming soon)
Install on SUSE Linux Enterprise Server (coming soon)
SQL Server as a Machine Learning Model Management System
By Rimma Nehme as written on blogs.technet.microsoft.com
Machine Learning Model Management
If you are a data scientist, business analyst or a machine learning engineer, you need model management – a system that manages and orchestrates the entire lifecycle of your learning model. Analytical models must be trained, compared and monitored before deploying into production, requiring many steps to take place in order to operationalize a model’s lifecycle. There isn’t a better tool for that than SQL Server!
SQL Server as an ML Model Management System
In this blog, I will describe how SQL Server can enable you to automate, simplify and accelerate machine learning model management at scale – from build, train, test and deploy all the way to monitor, retrain and redeploy or retire. SQL Server treats models just like data – storing them as serialized varbinary objects. As a result, it is pretty agnostic to the analytics engines that were used to build models, thus making it a pretty good model management tool for not only R models (because R is now built-in into SQL Server 2016) but for other runtimes as well.
SELECT * FROM [dbo].[models]
Figure 1: Machine Learning model is just like data inside SQL Server.
SQL Server approach to machine learning model management is an elegant solution. While there are existing tools that provide some capabilities for managing models and deployment, using SQL Server keeps the models “close” to data, thus leveraging all the capabilities of a Management System for Data to be now nearly seamlessly transferrable to machine learning models (see Figure 2). This can help simplify the process of managing models tremendously resulting in faster delivery and more accurate business insights.
Figure 2: Pushing machine learning models inside SQL Server 2016 (on the right), you get throughput, parallelism, security, reliability, compliance certifications and manageability, all in one. It’s a big win for data scientists and developers – you don’t have to build the management layer separately. Furthermore, just like data in databases can be shared across multiple applications, you can now share the predictive models. Models and intelligence become “yet another type of data”, managed by the SQL Server 2016.
Why Machine Learning Model Management?
Today there is no easy way to monitor, retrain and redeploy machine learning models in a systematic way. In general, data scientists collect the data they are interested in, prepare and stage the data, apply different machine learning techniques to find a best-of-class model, and continually tweak the parameters of the algorithm to refine the outcomes. Automating and operationalizing this process is difficult. For example, a data scientist must code the model, select parameters and a runtime environment, train the model on batch data, and monitor the process to troubleshoot errors that might occur. This process is repeated iteratively on different parameters and machine learning algorithms, and after comparing the models on accuracy and performance, the model can then be deployed.
Currently, there is no standard method for comparing, sharing or viewing models created by other data scientists, which results in siloed analytics work. Without a way to view models created by others, data scientists leverage their own private library of machine learning algorithms and datasets for their use cases. As models are built and trained by many data scientists, the same algorithms may be used to build similar models, particularly if a certain set of algorithms is common for a business’s use cases. Over time, models begin to sprawl and duplicate unnecessarily, making it more difficult to establish a centralized library.
Figure 3: Why SQL Server 2016 for machine learning model management.
In light of these challenges, there is an opportunity to improve model management.
Why SQL Server 2016 for ML Model Management?
There are many benefits to using SQL Server for model management. Specifically, you can use SQL Server 2016 for the following:
Model Store and Trained Model Store: SQL Server can efficiently store a table of “pre-baked” models of commonly used machine learning algorithms that can be trained on various datasets (already present in the database), as well as trained models for deployment against a live stream for real-time data.
Monitoring service and Model Metadata Store: SQL Server can provide a service that monitors the status of the machine learning model during its execution on the runtime environment for the user, as well as any metadata about its execution that is then stored for the user.
Templated Model Interfaces: SQL Server can store interfaces that abstract the complexity of machine learning algorithms, allowing users to specify the inputs and outputs for the model.
Runtime Verification (for External Runtimes): SQL Server can provide a runtime verification mechanism using a stored procedure to determine which runtime environments can support a model prior to execution, helping to enable faster iterations for model training.
Deployment and Scheduler: Using SQL Server’s trigger mechanism, automatic scheduling and an extended stored procedure you can perform automatic training, deployment and scheduling of models on runtime environments, obviating the need to operate the runtime environments during the modeling process.
Here is the list of specific capabilities that makes the above possible:
ML Model Performance:
Fast training and scoring of models using operational analytics (in-memory OLTP and in-memory columnstore).
Monitor and optimize model performance via Query store and DMVs. Query store is like a “black box” recorder on an airplane. It records how queries have executed and simplifies performance troubleshooting by enabling you to quickly find performance differences caused by changes in query plans. The feature automatically captures a history of queries, plans, and runtime statistics, and retains these for your review. It separates data by time windows, allowing you to see database usage patterns and understand when query plan changes happened on the server.
Hierarchical model metadata (that is easily updateable) using native JSON support: Expanded support for un-structured JSON data inside SQL Server enables you to store properties of your models using JSON format. Then you can process JSON data just like any other data inside SQL. It enables you to organize collections of your model properties, establish relationships between them, combine strongly-typed scalar columns stored in tables with flexible key/value pairs stored in JSON columns, and query both scalar and JSON values in one or multiple tables using full Transact-SQL. You can store JSON in In-memory or Temporal tables, you can apply Row-Level Security predicates on JSON text, and so on.
Temporal support for models: SQL Server 2016’s temporal tables can be used for keeping track of the state of models at any specific point in time. Using temporal tables in SQL Server you can: (a) understand model usage trends over time, (b) track model changes over time, (c) audit all changes to models, (d) recover from accidental model changes and application errors.
ML Model Security and Compliance:
Sensitive model encryption via Always Encrypted: Always Encrypted can protect model at rest and in motion by requiring the use of an Always Encrypted driver when client applications to communicate with the database and transfer data in an encrypted state.
Transparent Data Encryption (TDE) for models. TDE is the primary SQL Server encryption option. TDE enables you to encrypt an entire database that may store machine learning models. Backups for databases that use TDE are also encrypted. TDE protects the data at rest and is completely transparent to the application and requires no coding changes to implement.
Row-Level Security enables you to protect the model in a table row-by-row, so a particular user can only see the models (rows) to which they are granted access.
Dynamic model (data) masking obfuscates a portion of the model data to anyone unauthorized to view it. Return masked data to non-privileged users (e.g. credit card numbers).
Change model capture can be used to capture insert, update, and delete activity applied to models stored in tables in SQL Server, and to make the details of the changes available in an easily consumed relational format. The change tables used by change data capture contain columns that mirror the column structure of a tracked source table, along with the metadata needed to understand the changes that have occurred.
Enhanced model auditing. Auditing is an important mechanism for many organizations to serve as a checks and balances. In SQL Server 2016 are there any new Auditing features to support model auditing. You can implement user-defined audit, audit filtering and audit resilience.
ML Model Availability:
AlwaysOn for model availability and champion-challenger. An availability group in SQL Server supports a failover environment. An availability group supports a set of primary databases and one to eight sets of corresponding secondary databases. Secondary databases are not backups. In addition, you can have automatic failover based on DB health. One interesting thing about availability groups in SQL Server with readable secondaries is that they enable “champion-challenger” model setup. The champion model runs on a primary, whereas challenger models are scoring and being monitored on the secondaries for accuracy (without having any impact on the performance of the transactional database). Whenever a new champion model emerges, it’s easy to enable it on the primary.
ML Model Scalability
Enhanced model caching can facilitate model scalability and high performance. SQL Server enables caching with automatic, multiple TempDB files per instance in multi-core environments.
In summary, SQL Server delivers the top-notch data management with performance, security, availability, and scalability built into the solution. Because SQL Server is designed to meet security standards, it has minimal total surface area and database software that is inherently more secure. Enhanced security, combined with built-in, easy-to-use tools and controlled model access can help organizations meet strict compliance policies. Integrated high availability solutions enable faster failover and more reliable backups – and they are easier to configure, maintain, and monitor, which helps organizations reduce the total cost of model management (TCMM). In addition, SQL Server supports complex data types and non-traditional data sources, and it handles them with the same attention – so data scientist can focus on improving the model quality and outsource all of the model management to SQL Server.
Conclusion
Using SQL Server 2016 you can do model management with ease. SQL Server is unique from other machine learning model management tools, because it is a database engine, and is optimized for data management. The key insight here is that “models are just like data” to an engine like SQL Server, and as such we can leverage most of the mission-critical features of data management built into SQL Server for machine learning models. Using SQL Server for ML model management, an organization can create an ecosystem for harvesting analytical models, enabling data scientists and business analysts to discover the best models and promote them for use. As companies rely more heavily on data analytics and machine learning, the ability to manage, train, deploy and share models that turn analytics into action-oriented outcomes is essential.
Managed Solution is a full-service technology firm that empowers business by delivering, maintaining and forecasting the technologies they’ll need to stay competitive in their market place. Founded in 2002, the company quickly grew into a market leader and is recognized as one of the fastest growing IT Companies in Southern California.
We specialize in providing full managed services to businesses of every size, industry, and need.
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Before & After: Server Room Refresh
Everyday our field engineers and technical support specialists face both challenging and rewarding experiences out in the field. Recently, our team gave one of our clients, a Biotech & Life Sciences Company, a complete server room refresh. Between untangling cables, figuring out the most optimal (and personal) color coding system, and configuring servers to meet the clients' business needs, our team was hard at work and now have the results to prove it! Check out the Sway below to see behind-the-scenes before and after shots of this server room makeover:
Azure SQL Database Threat Detection has been in preview for a few months now. We’ve on-boarded many customers and received some great feedback. We would like to share a couple of customer experiences that demonstrate how SQL Threat Detection helped to address their concerns about potential threats to their database.
What is SQL Threat Detection?
SQL Threat Detection is a new security intelligence feature built into the Azure SQL Database service. Working around the clock to learn, profile and detect anomalous database activities, SQL Threat Detection identifies potential threats to the database. Security officers or other designated administrators can get an immediate notification about suspicious database activities as they occur. Each notification provides details of the suspicious activity and recommends how to further investigate and mitigate the threat.
Currently, SQL Threat Detection on Azure SQL Database detects potential vulnerabilities and SQL injection attacks, as well as anomalous database access patterns. The following customer feedback attests to how SQL Threat Detection warned them about these threats as they occurred and helped them improve their database security.
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Case #1: Attempted database access by former employee
Borja Gómez, architect & development lead at YesEnglish
“SQLThreat Detection is a useful feature that allows us to detect and respond to anomalous database activities, which were not visible to us beforehand. As part of my role designing and building Azure-based solutions for global companies in the Information and Communication Technology field, we always turn on SQL Auditing and Threat Detection, which are built-in and operate independently of our code. A few months later, we received an email alert that “Anomalous database activities from unfamiliar IP (location) was detected”. The threat came from a former employee trying to access one of our customer’s databases, which contained sensitive data, using old credentials. Because SQL Threat Detection allowed us to detect this threat as it occurred, we were able to remediate the threat immediately by locking down the firewall rules and changing credentials, thereby preventing any damage. Such is the simplicity and power of Azure.
Case #2: Preventing SQL Injection attacks
Richard Priest, Architectural Software Engineer at Feilden Clegg Bradley Studios and head of the collective at Missing Widget:
“Thanks to SQL Threat Detection, we were able to detect and fix code vulnerabilities to SQL injection attacks and prevent potential threats to our database. I was extremely impressed how simple it was to enable threat detection policy using the Azure portal, which required no modifications to our SQL client applications. A while after enabling SQL Threat Detection, we received an email notification about ‘An application error that may indicate a vulnerability to SQL injection attacks’. The notification provided details of the suspicious activity and recommended concrete actions to further investigate and remediate the threat. The alert helped me to track down the source my error and pointed me to the Microsoft documentation that thoroughly explained how to fix my code. As the head of IT for an information technology and services company, I now guide my team to turn on SQL Auditing and Threat Detection on all our projects, because it gives us another layer of protection and is like having a free security expert on our team.”
Case #3: Anomalous access from home to production database
Manrique Logan, architect & technical lead at ASEBA:
“SQL Threat Detection is an incredible feature, super simple to use, empowering our small engineering team to protect our company data without the need to be security experts. Our non-profit company provides user-friendly tools for mental health professionals, storing health and sales data in the cloud. As such we need to be HIPAA and PCI compliant, and SQL Auditing and Threat Detection help us achieve this. These features are available out of the box, and simple to enable too, taking only a few minutes to configure. We saw the real value from these not long after enabling SQL Threat Detection, when we received an email notification that ‘Access from an unfamiliar IP address (location) was detected’. The alert was triggered as a result of my unusual access to our production database from home. Knowing that Microsoft is using its vast security expertise to protect my data gives me incredible peace of mind and allows us to focus our security budget on other issues. Furthermore, knowing the fact that every database activity is being monitored has increased security awareness among our engineers. SQL Threat Detection is now an important part of our incident response plan. I love that Azure SQL Database offers such powerful and easy-to-use security features.
How to turn on SQL Threat Detection
SQL Threat Detection is incredibly easy to enable. You simply navigate to the Auditing & Threat Detection configuration blade for your database in the Azure management portal. There you switch on Auditing and Threat Detection, and configure at least one email address for receiving alerts.
Managed Solution is a full-service technology firm that empowers business by delivering, maintaining and forecasting the technologies they’ll need to stay competitive in their market place. Founded in 2002, the company quickly grew into a market leader and is recognized as one of the fastest growing IT Companies in Southern California.
We specialize in providing full Microsoft solutions to businesses of every size, industry, and need.
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United Airlines Uses Azure Site Recovery to Build a Disaster Recovery Solution
To address the need for an enterprise-ready disaster recovery solution, in June 2013 United Airlines joined the Rapid Deployment Program (RDP) for Windows Server 2012 R2. “Now that we are more virtualized, we are looking at a whole new approach to DR, where flexibility and cloud computing combine to provide a resilient solution that we can tailor to meet our needs,” says Wilson. “It made sense to continue on our cloud journey with a Microsoft DR solution.”
Hyper-V Replica offers a data replication solution that replicates virtual machines within a site or to a remote site. The latest version of Hyper-V Replica provides the flexibility that United is looking for, with variable replication frequency—from 30 seconds up to 15 minutes—and support for extended replication to a third site. And the new DR management service, Microsoft Azure Site Recovery, answers the airline’s need for a highly available DR solution because it is delivered as a cloud service running in the Microsoft Azure environment. Azure Site Recovery offers orchestration at scale delivered via recovery plans, so United IT staff can bring up applications in a desired manner at a low recovery time objective. While Azure Site Recovery is a feature of Windows Server 2012 R2, it supports backwards compatibility with all versions of Hyper-V Replica.
With Azure Site Recovery:
Safeguard complex workloads against outages
Support heterogenous environments (including Hyper-V)
Leverage computer resources
Reduce infrastructure costs by migrating workloads to Azure
Using Azure as a destination for disaster recovery eliminates the cost and complexity maintaining a secondary site, and replicated data is stored in Azure Storage, with all the resilience that provides. Site Recovery provides test failovers to support disaster recovery drills without affecting production environments. You can also run planned failovers with a zero-data loss for expected outages, or unplanned failovers with minimal data loss (depending on replication frequency) for unexpected disasters. After failover you can failback to your primary sites. Site Recovery provides recovery plans that can include scripts and Azure automation workbooks so that you can customize failover and recovery of multi-tier applications.
Managed Solution is a full-service technology firm that empowers business by delivering, maintaining and forecasting the technologies they’ll need to stay competitive in their market place. Founded in 2002, the company quickly grew into a market leader and is recognized as one of the fastest growing IT Companies in Southern California.
We specialize in providing full Microsoft solutions to businesses of every size, industry, and need.
Learn more about professional services provided by Managed Solution
To Learn More about Professional Services, contact us at 800-208-3617
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SQL Server 10% Cash Back partner incentive ends June 30, 2016
Just a few weeks left to take advantage of the cashback incentive for sales of SQL Server Standard and SQL Server Premium Open licenses to SMB Commercial customers that runs from March 1, 2016 through June 30, 2016. Incentives will only be paid on sales that occur after you successfully register and is limited to the first 400 partners to apply. So ensure you register before the sale and don't leave money on the table. WW Open Premier partners are not eligible for this incentive.
Key dates to be aware of:
•
March 1, 2016: Incentive start date and portal live date for registration
•
June 30, 2016: Incentive end date
•
August 2016: Approximately two months post end of promotion payouts will begin
MAX PAYOUTS ON FUNDING DOLLARS
SPRINT TO THE FINISH TO GET THE MOST MICROSOFT FUNDING BEFORE THE DEADLINE
Review eligibility for offers - we are ready to help you through the technical stages necessary to execute & ensure the service is set-up successfully.
Managed Solution is a full-service technology firm that empowers business by delivering, maintaining and forecasting the technologies they’ll need to stay competitive in their market place. Founded in 2002, the company quickly grew into a market leader and is recognized as one of the fastest growing IT Companies in Southern California.
We specialize in providing full Microsoft Solutions to businesses of every size, industry, and need.
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Donuts and Data: Celebrating National Donut Day and SQL Server 2016
Happy National Donut Day! People all over the country are celebrating this delicious day by eating a variety of donuts. Managed Solution joined in on the fun by providing donuts for the office, including cream filled, old fashioned, and even some donuts with colorful sprinkles. What's a sweet treat that won't make you run on the treadmill all night? Data made easy with the new SQL Server 2016.
SQL Server 2016 is much more than a database – it is the data management and business analytics platform for intelligent applications that can tackle any data project for any application. SQL Server 2016 is the only database that is born in the cloud. It is the summation of capabilities we released first in Microsoft Azure, where they are already being used in millions of production databases.
Watch this video to learn more about how SQL Server 2016 is transforming businesses with data:
Now that you've learned a little bit more about SQL Server 2016 and data, it's time to treat yourself to a donut - after all it is a holiday!
Managed Solution’s Team has the experience and expertise to architect SQL database and reporting systems tailored for your environment. Contact us for more information 800-307-0296
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4 Ways SQL Server Beats Oracle:
Intelligent Cloud Database: Everything Built In:
In-memory across all workloads
Scale Performance on the Fly, Without App Downtime
Managed Solution’s Team has the experience and expertise to architect SQL database and reporting systems tailored for your environment. Contact us for more information 800-307-0296
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