How to Prepare for SQL Server 2008 End of Support

For those of us who don't know, on the 9th of July 2019 Microsoft will end its support for SQL Server 2008 and 2008 R2. Afterward, you will no longer receive any regular security updates.

It will result in the SQL Server 2008 and 2008 R2 as increasingly more vulnerable to cyber-attacks in the future, as well as potential business interruptions and loss of data. Likewise, the end of support will also mean that you can fail to meet various compliance standards and industry regulations. Also, your organization will encounter higher maintenance costs regarding legacy servers, firewalls, intrusion systems, and other similar tools that help protect your network and computers.

So, what are your options going forward? There are several options available to you to make this transition a seamless one for your organization.

Migrate to Azure

The first of these options is to migrate to Azure SQL Database Managed Instance. It is a pretty straightforward option with no application code changes and almost no downtime to your systems. A similar option is to move to Azure Virtual Machines. It will provide you with three years of extended security updates at no cost, and you can update to a newer version whenever you are ready.

By making use of Azure Hybrid Benefit, on the other hand, you will be able to run Windows virtual machines on Azure at a lower rate. You can save up to 55% with this option on your existing licenses. You will, however, need to have Software Assurance to use it.

All of these options presented here will require an Azure environment. It can be purchased in different ways and can be used beyond just hosting virtual machines. And SQL Server in Azure can be operated as a database-as-a-service

so that any patches will be assured automatically.

On-Premises Upgrades

For better security, performance, availability, and opportunity for innovation via cloud analytics, you should also upgrade your systems to SQL Server 2017. Several enhancements come with SQL Server 2017, which will help you stay more secure and increase performance. Among these, we can count the Automatic Plan Correction, which will help detect and automatically correct any query plan stability issues.

Similarly, there's the Adaptive Query Processing (AQP) that can batch mode operations used with Columnstore indexes. There are also numerous other diagnostic and troubleshooting improvements. Some Showplan enhancements, for instance, are great at query tuning. Several new DMVs are useful for diagnostic and troubleshooting purposes.

SQL Server 2017 also brings to the table some community-driven enhancements such as the possibility for smart transaction log backup, differential backup, better TempDB monitoring, and diagnostics, as well as improved backup performance for small databases on high-end servers.

Keep in mind that, if you are unable to make the transition before the deadline, there is the possibility to extend security updates for an additional three years. Nevertheless, this option comes at a rather steep cost, but you have the opportunity to cover only the workloads that need it while you make the necessary upgrades.

Takeaway

While the end of support for SQL Server 2008 and 2008 R2 will happen in July 2019, it will also occur for Windows Server 2008 and 2008 R2 on January 14, 2020. You can take this opportunity to modernize your entire database to the latest version of the Windows Server. Managed Solution is here to help you through this whole transition.

How to Avoid the SQL Server End of Life Risks

If you are still running SQL Server 2008 or 2008 R2, then it's essential for you to know that Windows will stop providing extended support as of July 9, 2019. For the companies that miss the deadline, they will be facing severe security and data loss risks, among others.

Once the deadline has passed, Windows will no longer provide any further security updates, leaving your systems vulnerable to cyber-attacks. It's also important to know that these attacks are becoming ever more sophisticated with every passing day and by not having access to these regular updates, you will be at high risk of data loss, ransomware, malware, and other similar issues.

Also, you may have to deal with a sharp drop in customers as a result of your systems being out-of-date. Statistics indicate that over 20% of businesses lose customers as a direct result of security attacks, while a further 30% will experience a loss of revenue because of it.

It will affect your company's reputation. If you are a victim to data loss, your company will be held accountable to your shareholders, investors, customers, and the general public, which will brand your organization as one that's not to be trusted with sensitive information. It is particularly important for companies operating in the financial and healthcare industries.

Lastly, operating on outdated systems will also mean that you could be in breach of various compliance requirements such as the General Data Protection Regulation (GDPR). Running on these systems past July 9, 2019, will draw maintenance costs in terms of legacy servers, firewalls, intrusion systems, and other similar tools.

How To Avoid these SQL Server End of Life Risks

What you need to do in this situation is to move your SQL Server 2008, and 2008 R2 deployments to Azure SQL Database Managed Instance. It will involve no application code changes and an almost nonexistent downtime. It is a fully managed database-as-a-service, which makes use of the best service-level agreements (SLAs) and which doesn't require any future upgrades.

You can also use your existing licenses as well as the Azure Hybrid Benefit to save when migrating to either the Azure Virtual Machines or to Azure SQL Database Managed Instance.

If this migration is not possible for the time being, say if you have a piece of software installed on the server which can only work on the 2008 version, then Microsoft is offering its paid Extended Security Updates option. It will be made available for the following three years after the deadline to all customers with an Enterprise Agreement (EA, EAS or SCE) who purchased SQL Server with active Software Assurance or as part of a subscription.

Nevertheless, this option comes at a quite considerable cost - somewhere around 75% of the price of a fully licensed version of SQL Server. Do keep in mind, however, that this option can be purchased for only those servers that need them. Additionally, the updates will be extended annually, meaning that you can gradually reduce these costs by proceeding with the migration.

Takeaway

The best way to avoid the risks associated with the SQL Server 2008 or 2008 R2 end-of-life is by upgrading your systems to the latest versions. Together with Managed Solution, you will be able to see this happen in no time.

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.

How Azure SQL Threat Detection acts as your built-in security expert

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How Azure SQL Threat Detection acts as your built-in security expert

By Ron Matchoro as written on blogs.msdn.microsoft.com

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

“SQL Threat 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.

We specialize in providing full Microsoft solutions to businesses of every size, industry, and need.

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Infographic 4 Ways SQL Server beats Oracle

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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
Highest performing data warehouse
Voted least vulnerable 6 years in a row
End-to-end mobile BI on any device
In-database Advanced Analytics

VIEW FULL INFOGRPAHIC

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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Azure provides extreme flexibility and enables companies to grow as big as they want to; without being limited by resources. From app development to websites, SQL databases, and content streaming, Azure can be customized for each business.

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Create a Compelling User Experience

Use Azure for a consistent, cloud-based identity environment creating an unparalleled unified user experience across all devices.

In today’s business communication is immediate and 24/7. Innovation happens overnight and anything is possible. This is where the right technology enters the picture. The advances that are transforming the business world are the same tools that you can use to transform your business. With cloud services and mobile devices, you can quickly adapt to change and expand your business efficiently, without expanding your budget.

PREPARE:
When your business is ready to grow, you can't afford for your technology to hold you back. Quickly equip new employees with the tools that they need without a lengthy deployment process.

ADJUST:
Pay only for what you're actually using in a given month without costly delays or large capital investments when you change capacity.

EXPAND:
Your workers might be wasting time struggling with different experiences across their devices. Standardizing on one platform will improve employee productivity while allowing for broad device choice, be it based on personal preference or job function.

Deploy scalable infrastructure quickly with no maintenance costs:

Azure provides extreme flexibility and enables companies to grow as big as they want to; without being limited by resources. From app development to websites, SQL databases, and content streaming, Azure can be customized for each business. Start focusing on growing their businesses and not what they run on, their SMBs will be given the opportunity to grow.

Scale On-Demand Resources To Grow With Demand
Produce A Web Application Within Minutes
Produce Apps Using A Variety Of Languages
Make Global Changes Quickly
Built In SQL Database Functionality
Easy Automatic Storage And Backups
Integrates Deeply With Power BI
Microsoft-Managed Dedicated Cache Tiers

Managed Solution’s Team has the experience and expertise to architect Azure solutions tailored for your environment. Call us at 800- 313-2109 or fill out the contact form and someone with get back to you shortly!

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SQL Server 2016: The database for mission-critical intelligence

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SQL Server 2016: The database for mission-critical intelligence

Joseph Sirosh - Corporate Vice President, Data Group, Microsoft as written on blogs.microsoft.com

The world around us, every business and nearly every industry, is being transformed by technology. This disruption is driven, in part, by the intersection of three trends: a massive explosion of data, intelligence from machine learning and advanced analytics, and the economics and agility of cloud computing.

While databases power nearly every aspect of business today, they were not originally designed with this disruption in mind. Traditional databases were about recording and retrieving transactions such as orders and payments very reliably, very securely and efficiently. They were designed to enable reliable, secure, mission-critical transactional applications at small to medium scale, in on-premises datacenters.

Databases built to get ahead of today’s disruptions do very fast analyses of live data in-memory as transactions are being recorded or queried. They support very low latency advanced analytics and machine learning, such as forecasting and predictive models, on the same data, so that applications can easily embed data-driven intelligence. They allow databases to be offered as a fully managed service in the cloud, in turn making it easy to build and deploy intelligent Software as a Service (SaaS) apps.

They also provide innovative security features built for a world where a majority of data is accessible over the Internet. They support 24×7 high-availability, efficient management and database administration across platforms. They therefore enable mission critical intelligent applications to be built and managed both in the cloud and on-premises. They are exciting harbingers of a new world of ambient intelligence.

We built SQL Server 2016 for this new world, and to help businesses get ahead of today’s disruptions. It supports hybrid transactional/analytical processing, advanced analytics and machine learning, mobile BI, data integration, always encrypted query processing capabilities and in-memory transactions with persistence. It is also perhaps the world’s only relational database to be “born cloud-first,” with the majority of features first deployed and tested in Azure, across 22 global datacenters and billions of requests per day. It is customer tested and battle ready.

Let me share with you what industry analysts and our customers think.

Industry analysts recognize the breadth and depth of our capabilities in data, intelligence and the cloud. Microsoft is the only company recognized as a leader across data platforms and cloud by Gartner in both vision and execution, in database, business intelligence, advanced analytics, data warehouse, cloud infrastructure and cloud application platforms.

The customers we’ve been working with in preview share our excitement and are already benefiting from new innovations, such as built-in analytics.
PROS Holdings, Inc. (NYSE: PRO) is a revenue and profit realization company that helps B2B and B2C customers achieve their business goals through data science. Royce Kallesen, senior director of Science and Research at PROS says: “Microsoft R’s parallelization and enhanced memory management on the server integrated with SQL Server provides much faster results on a common platform with built-in security.” They have realized over 100x faster advanced analytics using SQL Server and built-in Microsoft R Server.

In addition to faster analytics, the real-time in-memory processing capabilities of SQL Server are industry leading. This technology allows up to 100x faster analytics with updatable in-memory columnstores. In addition, as the only commercial database that leads simultaneously in both transaction processing (per the TPC-E benchmark) and data warehousing (per the TPC-H benchmark), SQL Server allows customers to realize incredible performance against massive data sets and gain real-time insights – across all workloads, new and existing applications.
“KPMG observed approximately 60 percent reduction in execution time and 10x table-compression gains for one of the main analytical procedures by leveraging Columnstore Indexes and Parallel Insert functions in SQL Server 2016,” says Michael S. Sellman, executive director, Global IT Services, KPMG LLP.
According to Chris Stolte, co-founder and chief development offficer of Tableau, Inc., “an average 190%+ interactive query performance improvement enables our customers to visually explore large datasets in real-time, even against transactional databases.”

With unique hybrid capabilities, any SQL Server deployment or app can span private clouds, hosted clouds and our public cloud, Microsoft Azure. New Stretch Database technology allows customers to dynamically, transparently and securely stretch their transactional data to Azure, creating a massive database with great price performance. Customers can also use new AlwaysOn Availability Groups to enable disaster recovery at low cost.

Security has never been more important, and we’re humbled to be the industry’s least vulnerable database, six years running, according to the National Institute of Standards and Technology (NIST) public security board. Several capabilities in SQL Server 2016 help protect data at rest and in memory (Always Encrypted), encrypt all user data with low performance overhead (Transparent Data Encryption), mitigate attacks with support for Transport Layer Security version 1.2, and allow developers to build applications that restrict access and protect data from specific users with Dynamic Data Masking (DDM) and Row Level Security (RLS).
DocuSign helps organizations build entire approval workflows without a single sheet of paper or filing cabinet in sight, so security and reliability are critical, as they are with every business today. Docusign partnered with Microsoft to help secure their customers’ data, realize insights with SQL Server analytics and BI capabilities and receive world-class support. Hear directly from their Chief Architect and Vice President of Platforms, Eric Fleischman:

With all of these capabilities built-in, SQL Server 2016 delivers not just a relational database, but an entire data platform for your business with incredible TCO. Today customers can save up to $10 million over three years versus Oracle, running transactional, data warehouse, data integration, business intelligence and advanced analytics workloads.* Judson Althoff, president of Microsoft North America, announced a new program to help more customers adopt SQL Server 2016 and save. Specifically, customers currently running applications or workloads on non-Microsoft paid commercial RDBMS platform will be able to migrate their existing applications with free SQL Server licenses.**

Microsoft is delivering on a vision that no other company can match across data, intelligence and cloud. To learn more, watch the webcast as well as various on-demand videos that showcase the new capabilities of this database built for mission-critical intelligence.

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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Announcing SQL Server on Linux

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Announcing SQL Server on Linux

By Scott Guthrie - Executive Vice President, Cloud and Enterprise Group, Microsoft

It’s been an incredible year for the data business at Microsoft and an incredible year for data across the industry. At the March Data Driven event in New York, we kicked off a wave of launch activities for SQL Server 2016 with general availability. This is the most significant release of SQL Server that we have ever done, and brings with it some fantastic new capabilities.

SQL Server 2016 delivers:

Groundbreaking security encryption capabilities that enable data to always be encrypted at rest, in motion and in-memory to deliver maximum security protection
In-memory database support for every workload with performance increases up to 30-100x
Incredible Data Warehousing performance with the #1, #2 and #3 TPC-H 10 Terabyte benchmarks for non-clustered performance, and as of March 7, the #1 SAP SD Two-Tier performance benchmark on Windows1
Business Intelligence for every employee on every device – including new mobile BI support for iOS, Android and Windows Phone devices
Advanced analytics using our new R support that enables customers to do real-time predictive analytics on both operational and analytic data
Unique cloud capabilities that enable customers to deploy hybrid architectures that partition data workloads across on-premises and cloud based systems to save costs and increase agility

These improvements, and many more, are all built into SQL Server and bring you not just a new database but a complete platform for data management, business analytics and intelligent apps – one that can be used in a consistent way across both on-premises and the cloud. In fact, over the last year we’ve been using the SQL Server 2016 code-base to run in production more than 1.4 million SQL Databases in the cloud using our Azure SQL Database as a Service offering, and this real-world experience has made SQL Server 2016 an incredibly robust and battle-hardened data platform.

Gartner recently named Microsoft as leading the industry in their Magic Quadrant for Operational Database Management Systems in both execution and vision. We’re also a leader in Gartner’s Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics, and Magic Quadrant for Business Intelligence and Analytics Platforms, as well as leading in vision in the Magic Quadrant for Advanced Analytics Platforms.

Extending SQL Server to Also Now Run on Linux

Today I’m excited to announce our plans to bring SQL Server to Linux as well. This will enable SQL Server to deliver a consistent data platform across Windows Server and Linux, as well as on-premises and cloud. We are bringing the core relational database capabilities to preview today, and are targeting availability in mid-2017.

SQL Server on Linux will provide customers with even more flexibility in their data solution. One with mission-critical performance, industry-leading TCO, best-in-class security, and hybrid cloud innovations – like Stretch Database which lets customers access their data on-premises and in the cloud whenever they want at low cost – all built in.

“This is an enormously important decision for Microsoft, allowing it to offer its well-known and trusted database to an expanded set of customers”, said Al Gillen, group vice president, enterprise infrastructure, at IDC. “By taking this key product to Linux Microsoft is proving its commitment to being a cross platform solution provider. This gives customers choice and reduces the concerns for lock-in. We would expect this will also accelerate the overall adoption of SQL Server.”

“SQL Server’s proven enterprise experience and capabilities offer a valuable asset to enterprise Linux customers around the world,” said Paul Cormier, President, Products and Technologies, Red Hat. “We believe our customers will welcome this news and are happy to see Microsoft further increasing its investment in Linux. As we build upon our deep hybrid cloud partnership, spanning not only Linux, but also middleware, and PaaS, we’re excited to now extend that collaboration to SQL Server on Red Hat Enterprise Linux, bringing enterprise customers increased database choice.”

“We are delighted to be working with Microsoft as it brings SQL Server to Linux,” said Mark Shuttleworth, founder of Canonical. “Customers are already taking advantage of Azure Data Lake services on Ubuntu, and now developers will be able to build modern applications that utilize SQL Server’s enterprise capabilities.”

Bringing SQL Server to Linux is another way we are making our products and new innovations more accessible to a broader set of users and meeting them where they are. Just last week, we announced our agreement to acquire Xamarin. Recently, we also announced Microsoft R Server , our technologies based on our acquisition of Revolution Analytics, with support for Hadoop and Teradata.

The private preview of SQL Server on Linux is available starting today and we look forward to working with the community, our customers and our partners to bring it to market.

Please join me Satya Nadella, Joseph Sirosh and Judson Althoff at our Data Driven event on Thursday to hear more about this news and how Microsoft is helping customers transform their business using data.

Source: http://blogs.microsoft.com/blog/2016/03/07/announcing-sql-server-on-linux/

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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Migrate to Azure

On-Premises Upgrades

Takeaway

How To Avoid these SQL Server End of Life Risks

Takeaway

SQL Server as a Machine Learning Model Management System

By Rimma Nehme as written on blogs.technet.microsoft.com

Machine Learning Model Management

SQL Server as an ML Model Management System

SELECT * FROM [dbo].[models]

Why 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:

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.

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).

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.

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).

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:

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.

Conclusion

How Azure SQL Threat Detection acts as your built-in security expert

By Ron Matchoro as written on blogs.msdn.microsoft.com

What is SQL Threat Detection?

Case #1: Attempted database access by former employee

Borja Gómez, architect & development lead at YesEnglish

Case #2: Preventing SQL Injection attacks

Richard Priest, Architectural Software Engineer at Feilden Clegg Bradley Studios and head of the collective at Missing Widget:

Case #3: Anomalous access from home to production database

Manrique Logan, architect & technical lead at ASEBA:

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.

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

Highest performing data warehouse

Voted least vulnerable 6 years in a row

End-to-end mobile BI on any device

In-database Advanced Analytics

VIEW FULL INFOGRPAHIC

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

Create a Compelling User Experience

Use Azure for a consistent, cloud-based identity environment creating an unparalleled unified user experience across all devices.

PREPARE: When your business is ready to grow, you can't afford for your technology to hold you back. Quickly equip new employees with the tools that they need without a lengthy deployment process.

ADJUST: Pay only for what you're actually using in a given month without costly delays or large capital investments when you change capacity.

EXPAND: Your workers might be wasting time struggling with different experiences across their devices. Standardizing on one platform will improve employee productivity while allowing for broad device choice, be it based on personal preference or job function.

Deploy scalable infrastructure quickly with no maintenance costs:

Scale On-Demand Resources To Grow With Demand

Produce A Web Application Within Minutes

Produce Apps Using A Variety Of Languages

Make Global Changes Quickly

Built In SQL Database Functionality

Easy Automatic Storage And Backups

Integrates Deeply With Power BI

Microsoft-Managed Dedicated Cache Tiers

Managed Solution’s Team has the experience and expertise to architect Azure solutions tailored for your environment. Call us at 800- 313-2109 or fill out the contact form and someone with get back to you shortly!

SQL Server 2016: The database for mission-critical intelligence

Joseph Sirosh - Corporate Vice President, Data Group, Microsoft as written on blogs.microsoft.com

Let me share with you what industry analysts and our customers think.

Microsoft is delivering on a vision that no other company can match across data, intelligence and cloud. To learn more, watch the webcast as well as various on-demand videos that showcase the new capabilities of this database built for mission-critical intelligence.

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

Announcing SQL Server on Linux

By Scott Guthrie - Executive Vice President, Cloud and Enterprise Group, Microsoft

SQL Server 2016 delivers:

Groundbreaking security encryption capabilities that enable data to always be encrypted at rest, in motion and in-memory to deliver maximum security protection

In-memory database support for every workload with performance increases up to 30-100x

Incredible Data Warehousing performance with the #1, #2 and #3 TPC-H 10 Terabyte benchmarks for non-clustered performance, and as of March 7, the #1 SAP SD Two-Tier performance benchmark on Windows1

Business Intelligence for every employee on every device – including new mobile BI support for iOS, Android and Windows Phone devices

Advanced analytics using our new R support that enables customers to do real-time predictive analytics on both operational and analytic data

Unique cloud capabilities that enable customers to deploy hybrid architectures that partition data workloads across on-premises and cloud based systems to save costs and increase agility

Extending SQL Server to Also Now Run on Linux

PREPARE:
When your business is ready to grow, you can't afford for your technology to hold you back. Quickly equip new employees with the tools that they need without a lengthy deployment process.

ADJUST:
Pay only for what you're actually using in a given month without costly delays or large capital investments when you change capacity.

EXPAND:
Your workers might be wasting time struggling with different experiences across their devices. Standardizing on one platform will improve employee productivity while allowing for broad device choice, be it based on personal preference or job function.