Microsoft Shops prepare for next generation features in SQL Server 2016

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Microsoft announces SQL Server 2016, an intelligent platform for a mobile first, cloud first world.  The next major release of Microsoft’s flagship database and analytics platform provides breakthrough performance for mission critical applications and deeper insights on your data across on-premises and cloud. Top capabilities for the release include: Always Encrypted – a new capability that protects data at rest and in motion, Stretch Database – new technology that lets you dynamically stretch your warm and cold transactional data to Microsoft Azure, enhancements to our industry-leading in-memory technologies for real-time analytics on top of breakthrough transactional performance and new in-database analytics with R integration.

Always Encrypted

Data security is top of mind, especially for mission critical applications, and SQL Server has been the enterprise database with the fewest security vulnerabilities six years running.*  To help customers with data security and compliance when using SQL Server on-premises or in the cloud, we are introducing Always Encrypted. Always Encrypted, based on technology from Microsoft Research, protects data at rest and in motion. With Always Encrypted, SQL Server can perform operations on encrypted data and best of all, the encryption key resides with the application in the customers trusted environment. Encryption and decryption of data happens transparently inside the application which minimizes the changes that have to be made to existing applications.

Stretch Database

Today, in the Ignite keynote, we showcased how you can gain the benefits of hyper-scale cloud in the box with new hybrid scenarios including Stretch Database. As core transactional tables grow in size, you may need to archive historical data to lower cost and to maintain fast performance. This unique technology allows you to dynamically stretch your warm and cold transactional data to Microsoft Azure, so your operational data is always at hand, no matter the size, and you benefit from the low cost of using Microsoft Azure.  You can use Always Encrypted with Stretch Database to extend your data in a more secure manner for greater peace of mind.

Real-time Operational Analytics & In-Memory OLTP

Building on our industry leading and proven in-memory technologies, customers will benefit from the combination of real-time operational analytics with blazing fast transactional performance – a first among enterprise vendors.  For In-Memory OLTP, which customers today are using for up to 30x faster transactions than disk based systems, you will now be able to apply this technology tuned for transactional performance to a significantly greater number of applications as well as benefit from increased concurrency.  With these enhancements, we also introduce the unique capability to use our in-memory columnstore delivering 100X faster queries with in-memory OLTP for in-memory performance and real-time operational analytics.

Built-in Advanced Analytics, PolyBase and Mobile BI

For deeper insights into data, SQL Server 2016 expands its scope beyond transaction processing, data warehousing and business intelligence to deliver advanced analytics as an additional workload in SQL Server with proven technology from Revolution Analytics.  We want to make advanced analytics more accessible and increase performance for your advanced analytic workloads by bringing R processing closer to the data and building advanced analytic capabilities right into SQL Server.  Additionally, we are building PolyBase into SQL Server, expanding the power to extract value from unstructured and structured data using your existing T-SQL skills. With this wave, you can then gain faster insights through rich visualizations on many devices including mobile applications on Windows, iOS and Android.

Additional capabilities in SQL Server 2016 include:

  • Additional security enhancements for Row-level Security and Dynamic Data Masking to round out our security investments with Always Encrypted.
  • Improvements to AlwaysOn for more robust availability and disaster recovery with multiple synchronous replicas and secondary load balancing.
  • Native JSON support to offer better performance and support for your many types of your data.
  • SQL Server Enterprise Information Management (EIM) tools and Analysis Services get an upgrade in performance, usability and scalability.
  • Faster hybrid backups, high availability and disaster recovery scenarios to backup and restore your on-premises databases to Azure and place your SQL Server AlwaysOn secondaries in Azure.

In addition, there are many more capabilities coming with SQL Server 2016 that deliver mission critical performance, deeper insights on your data and allow you to reap the benefits of hyper-scale cloud.

Last week at Build we announced exciting innovations to support our mission of making it easier to work with your data, no matter how big or complex.  We also shared how we are bringing capabilities to the cloud first in Azure SQL Database as with such as Row-level security and Dynamic Data Masking and then bringing the capabilities, as well as the learnings from running these at hyper-scale, back to SQL Server to improve our on-premises offering.  Thus, all our customers benefit from our investments and learnings in Microsoft Azure.  In addition to our hybrid cloud scenarios and investments in running SQL Server 2016 in Azure Virtual Machine, SQL Server delivers a complete database platform for hybrid cloud, enabling you to more easily build, deploy and manage solutions that span on-premises and cloud.

As the foundation of our end-to-end data platform, with this release of SQL Server we continue to make it easier for customers to maximize your data dividends. With SQL Server 2016 you can capture, transform, and analyze any data, of any size, at any scale, in its native format —using the tools, languages and frameworks you know and want in a trusted environment on-premises and in the cloud.

Be sure to visit the SQL Server 2016 preview page to read about the capabilities of SQL Server 2016 and sign-up to be notified once the public preview is available.

Cheap , Easy and Fast DataWarehousing – Our Implementation of Amazon Redshift

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Conventional methods were proven either slow and/or expensive in providing a complete Analytics Data Warehousing/Reporting solution to one of our clients who provide world-wide IPTV Services.     We then decided to scrap plans for a conventional data warehouse after doing preliminary POC Testing on Redshift.
 
With 1/10th of the cost and high performance and ease of manageability and moving away from traditional in house IT management and support,  the extensively scalable cloud based solution seem to be a perfect fit.      Redshift’s capability to work in sync with Hadoop and integrations with Amazon’s Elastic Map Reduce was another great motivation factor in selection of the technology.
 
There was never any worries about capacity that redshift can handle or other multi-tenancy or scalability factors.   The other primary factor was also being able to choose a feasible, flexible and ease of use reporting system that would work with Redshift and also have the capability of delivering ad-hoc reporting features with multi-tenancy support.
 
The estimated size of the database is expected to be several 10s of 100s of Terrabytes across multiple tenants all residing in a single redshift cluster scalable with multiple compute nodes.   The easy of  management without indexes and just having control over the sort and distribution keys was a significant time saver in data management.  Within few seconds the system is capable of delivering reports of various time granularity shifting through several years of data.

We at Applayatech, provide Redshift consulting service, which includes Redshift data warehouse service too at worthwhile Amazon Redshift pricing.

Contact us for Redshift consulting service, we also provide Redshift data warehouse service. Call us to know more about Amazon Redshift pricing today.
 

How non-relational database technologies free up data to create value

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The proliferation of multiple non-relational databases is transforming the data management landscape. Instead of having to force structures onto their data, organisations can now choose NoSQL database architectures that fit their emerging data needs, as well as combining these new technologies with conventional relational databases to drive new value from their information.

Until recently, data’s potential as a source of rich business insight has been limited by the structures that have been imposed upon it. Without access to the new database technologies now available, standard back-end design practice has been to force data into rigid architectures (regardless of variations in the structure of the actual data).

Inherently inflexible, these legacy architectures have prevented organisations from developing new use cases for the exploitation of structured and unstructured information.

The ongoing proliferation of non-relational database architectures marks a watershed in data management. What is emerging is a new world of horizontally scaling, unstructured databases that are better at solving some problems, along with traditional relational databases that remain relevant for others.

Technology has evolved to the extent that organizations need no longer be constrained by a lack of choice in database architectures. As front-runners have moved to identify the database options that match their specific data needs, we saw three key changes becoming increasingly prevalent during 2012:

  1. A rebalancing of the database landscape, as data architects began to embrace the fact that their architecture and design toolkit has evolved from being relational database-centric to also including a varied and maturing set of non-relational options (NoSQL database systems).
  2. The increasing pervasiveness of hybrid data ecosystems powered by disruptive technologies and techniques (such as the Apache Hadoop software framework for cost-effective processing of data at extreme scale).
  3. The emergence of more responsive data management ecosystems to provide the flexibility needed to undertake prototyping-enabled delivery (test-prove-industrialize) at lower cost and at scale.

From now on, savvy analytical leaders will be seeking to crystallize the use cases to which platforms are best suited. Instead of becoming overly focused on the availability of new technologies, they will identify the “sweet spots” where relational and non-relational databases can be combined to create value for information above and beyond its original purpose.

By taking advantage of the new world of choice in data architectures, more organizations will be equipped to identify and exploit breakthrough opportunities for data monetization.

Just as communications operators have created valuable B2B revenue streams from the wealth of customer data at their disposal, so better usage of their existing data will empower other companies to build potent new business models.

Implementing a rethink of how data is stored, processed and enriched means re-evaluating the traditional world of data management. Until now, data has been viewed as a structured asset and a cost centre that must be maintained.

The availability of new database architectures means that this mindset will change forever. Data management in a services-led world will require IT leaders to think about how the business can most easily take advantage of the data they have and the data they may previously have been unable to harness.

Agile data services architecture

As more architecture options become available, data lifecycles will shrink and become more agile. Rather than seeking to “over control” data, approaches to data management will become much less rigid. One key aim will be to open up new possibilities by encouraging and facilitating data sharing. Amazon stands out as a pioneer in this field. By building a service-oriented platform with an agile data services architecture, the company has been able to offer new services around cloud storage and data management – as well as giving itself the flexibility needed to cope with future demand for as yet unknown services.

Unprecedented accessibility to non-relational databases is reinvigorating the role of conventional architectures and “traditional” data management disciplines. From now on, analytics leaders will increasingly move to adopt hybrid architectures that combine the best of both worlds to leverage fresh new insights from the surging volumes of structured and unstructured information that are now the norm. In summary, there has never been a more exciting time to be a data management professional.