segunda-feira, 29 de agosto de 2016

[From Technet] Eight scenarios with Apache Spark on Azure that will transform any business

This post was authored by Rimma Nehme, Technical Assistant, Data Group.

Spark-Azure

Since its birth in 2009, and the time it was open sourced in 2010, Apache Spark has grown to become one of the largest open source communities in big data with over 400 organizations from 100 companies contributing to it. Spark stands out for its ability to process large volumes of data 100x faster, because data is persisted in-memory. Azure cloud makes Apache Spark incredibly easy and cost effective to deploy with no hardware to buy, no software to configure, with a full notebook experience to author compelling narratives, and integration with partner business intelligence tools. In this blog post, I am going to review of some of the truly game-changing usage scenarios with Apache Spark on Azure that companies can employ in their context.

Scenario #1: Streaming data, IoT and real-time analytics

Apache Spark’s key use case is its ability to process streaming data. With so much data being processed on a daily basis, it has become essential for companies to be able to stream and analyze it all in real time. Spark Streaming has the capability to handle this type of workload exceptionally well. As shown in the image below, a user can create an Azure Event Hub (or an Azure IoT Hub) to ingest rapidly arriving data into the cloud; both Event and IoT Hubs can intake millions of events and sensor updates per second that can then be processed in real-time by Spark.

Scenario 1_Spark Streaming

Businesses can use this scenario today for:

  • Streaming ETL: In traditional ETL (extract, transform, load) scenarios, the tools are used for batch processing, and data must be first read in its entirety, converted to a database compatible format, and then written to the target database. With Streaming ETL, data is continually cleaned and aggregated before it is pushed into data stores or for further analysis.
  • Data enrichment: Streaming capability can be used to enrich live data by combining it with static or ‘stationary’ data, thus allowing businesses to conduct more complete real-time data analysis. Online advertisers use data enrichment to combine historical customer data with live customer behavior data and deliver more personalized and targeted ads in real-time and in the context of what customers are doing. Since advertising is so time-sensitive, companies have to move fast if they want to capture mindshare. Spark on Azure is one way to help achieve that.
  • Trigger event detection: Spark Streaming can allow companies to detect and respond quickly to rare or unusual behaviors (“trigger events”) that could indicate a potentially serious problem within the system. For instance, financial institutions can use triggers to detect fraudulent transactions and stop fraud in its tracks. Hospitals can also use triggers to detect potentially dangerous health changes while monitoring patient vital signs and sending automatic alerts to the right caregivers who can then take immediate and appropriate action.
  • Complex session analysis: Using Spark Streaming, businesses can use events relating to live sessions, such as user activity after logging into a website or application, can be grouped together and quickly analyzed. Session information can also be used to continuously update machine learning models. Companies can then use this functionality to gain immediate insights as to how users are engaging on their site and provide more real-time personalized experiences.

Scenario #2: Visual data exploration and interactive analysis

Using Spark SQL running against data stored in Azure, companies can use BI tools such as Power BI, PowerApps, Flow, SAP Lumira, QlikView and Tableau to analyze and visualize their big data. Spark’s interactive analytics capability is fast enough to perform exploratory queries without sampling. By combining Spark with visualization tools, complex data sets can be processed and visualized interactively. These easy-to-use interfaces then allow even non-technical users to visually explore data, create models and share results. Because wider audience can analyze big data without preconceived notions, companies can test new ideas and visualize important findings in their data earlier than ever before. Companies can identify new trends and new relationships that were not apparent before and quickly drill down into them, ask new questions and find ways to innovate in new and smarter ways.

Scenario 2_Spark visual data exploration and interactive analysis

This scenario is even more powerful when interactive data discovery is combined with predictive analytics (more on this later in this blog). Based on relationships and trends identified during discovery, companies can use logistic regression or decision tree techniques to predict the probability of certain events in the future (e.g., customer churn probability). Companies can then take specific, targeted actions to control or avert certain events.

Scenario #3: Spark with NoSQL (HBase and Azure DocumentDB)

This scenario provides scalable and reliable Spark access to NoSQL data stored either in HBase or our blazing fast, planet-scale Azure DocumentDB, through “native” data access APIs. Apache HBase is an open-source NoSQL database that is built on Hadoop and modeled after Google BigTable. DocumentDB is a true schema-free managed NoSQL database service running in Azure designed for modern mobile, web, gaming, and IoT scenarios. DocumentDB ensures 99% of your reads are served under 10 milliseconds and 99% of your writes are served under 15 milliseconds. It also provides schema flexibility, and the ability to easily scale a database up and down on demand.

The Spark with NoSQL scenario enables ad-hoc, interactive queries on big data. NoSQL can be used for capturing data that is collected incrementally from various sources across the globe. This includes social analytics, time series, game or application telemetry, retail catalogs, up-to-date trends and counters, and audit log systems. Spark can then be used for running advanced analytics algorithms at scale on top of the data coming from NoSQL.

Scenario 3_Spark NoSQL

Companies can employ this scenario in online shopping recommendations, spam classifiers for real time communication applications, predictive analytics for personalization, and fraud detection models for mobile applications that need to make instant decisions to accept or reject a payment. I would also include in this category a broad group of applications that are really “next-gen” data warehousing, where large amounts of data needs to be processed inexpensively and then served in an interactive form to many users globally. Finally, internet of things scenarios fit in here as well, with the obvious difference that the data represents the actions of machines instead of people.

Scenario #4: Spark with Data Lake

Spark on Azure can be configured to use Azure Data Lake Store (ADLS) as an additional storage. ADLS is an enterprise-class, hyper-scale repository for big data analytic workloads. Azure Data Lake includes all the capabilities required to make it easy for developers, data scientists, and analysts in an enterprise environment to store data of any size, shape and speed, and do all types of processing and analytics across platforms and languages. Because ADLS is a file system compatible with Hadoop Distributed File System (HDFS), it makes it very easy to combine it with Spark for running computations at scale using pre-existing Spark queries.

Scenario 4_Spark with Data Lake

The data lake scenario arose because new types of data needed to be captured and exploited by companies, while still preserving all of the enterprise-level requirements like security, availability, compliance, failover, etc. Spark with data lake scenario enables a truly scalable advanced analytics on healthcare data, financial data, business-sensitive data, geo-location coordinates, clickstream data, server log, social media, machine and sensor data. If companies want an easy way of building data pipelines, have unparalleled performance, insure their data quality, manage access control, perform change data capture (CDC) processing, get enterprise-level security seamlessly and have world-class management and debugging tools, this is the scenario they need to implement.

Scenario #5: Spark with SQL Data Warehouse

While there is still a lot of confusion, Spark and big data analytics is not a replacement for traditional data warehousing. Instead, Spark on Azure can complement and enhance a company’s data warehousing efforts by modernizing the company’s approaches to analytics. A data warehouse can be viewed as an ‘information archive’ that supports business intelligence (BI) users and reporting tools for mission-critical functions of company. My definition of mission-critical is any system that supports revenue generation or cost control. If such a system fails, companies would have to manually perform these tasks to prevent loss of revenue or increased cost. Big data analytics systems like Spark help augment such systems by running more sophisticated computations, smarter analytics and delivering deeper insights using larger and more diverse datasets.

Azure SQL Data Warehouse (SQLDW) is a cloud-based, scale-out database capable of processing massive volumes of data, both relational and non-relational. Built on our massively parallel processing (MPP) architecture, SQLDW combines the power of the SQL Server relational database with Azure cloud scale-out capabilities. You can increase, decrease, pause, or resume a data warehouse in seconds with SQLDW. Furthermore, you save costs by scaling out CPU when you need it and cutting back usage during non-peak times. SQLDW is the manifestation of elastic future of data warehousing in the cloud.

Scenario 5_Spark with SQLDW

Some of the use cases of Spark with SQLDW scenario may include: using data warehouse to get a better understanding of its customers across product groups, then using Spark for predictive analytics on top of that data. Running advanced analytics using Spark on top of the enterprise data warehouse containing sales, marketing, store management, point of sale, customer loyalty, and supply chain data, then run advanced analytics using Spark to drive more informed business decisions at the corporate, regional, and store levels. Using Spark with the data warehousing data, companies can literally do anything from risk modeling, to parallel processing of large graphs, to advanced analytics, text processing – all on top of their elastic data warehouse.

Scenario #6: Machine Learning using R Server, MLlib

Another and probably one of the most prominent Spark use cases in Azure is machine learning. By storing datasets in-memory during a job, Spark has great performance for iterative queries common in machine learning workloads. Common machine learning tasks that can be run with Spark in Azure include (but are not limited to) classification, regression, clustering, topic modeling, singular value decomposition (SVD) and principal component analysis (PCA) and hypothesis testing and calculating sample statistics.

Typically, if you want to train a statistical model on very large amounts of data, you need three things:

  • Storage platform capable of holding all of the training data
  • Computational platform capable of efficiently performing the heavy-duty mathematical computations required
  • Statistical computing language with algorithms that can take advantage of the storage and computation power

Microsoft R Server, running on HDInsight with Apache Spark provides all three things above. Microsoft R Server runs within HDInsight Hadoop nodes running on Microsoft Azure. Better yet, the big-data-capable algorithms of ScaleR takes advantage of the in-memory architecture of Spark, dramatically reducing the time needed to train models on large data. With multi-threaded math libraries and transparent parallelization in R Server, customers can handle up to 1000x more data and up to 50x faster speeds than open source R. And if your data grows or you just need more power, you can dynamically add nodes to the Spark cluster using the Azure portal. Spark in Azure also includes MLlib for a variety of scalable machine learning algorithms, or you can use your own libraries. Some of the common applications of machine learning scenario with Spark on Azure are listed in a table below.

Vertical Sales and Marketing Finance and Risk Customer and Channel Operations and Workforce
Retail Demand forecasting

Loyalty programs

Cross-sell and upsell

Customer acquisition

Fraud detection

Pricing strategy

Personalization

Lifetime customer value

Product segmentation

Store location demographics

Supply chain management

Inventory management

Financial Services Customer churn

Loyalty programs

Cross-sell and upsell

Customer acquisition

Fraud detection

Risk and compliance

Loan defaults

Personalization

Lifetime customer value

Call center optimization

Pay for performance

Healthcare Marketing mix optimization

Patient acquisition

Fraud detection

Bill collection

Population health

Patient demographics

Operational efficiency

Pay for performance

Manufacturing Demand forecasting

Marketing mix optimization

Pricing strategy

Perf risk management

Supply chain optimization

Personalization

Remote monitoring

Predictive maintenance

Asset management

 

Scenario 6_Spark Machine Learning

Examples with just a few lines of code that you can try out right now:

Scenario #7: Putting it all together in a notebook experience

For data scientists, we provide out-of-the-box integration with Jupyter (iPython), the most popular open source notebook in the world. Unlike other managed Spark offerings that might require you to install your own notebooks, we worked with the Jupyter OSS community to enhance the kernel to allow Spark execution through a REST endpoint.

We co-led “Project Livy” with Cloudera and other organizations to create an open source Apache licensed REST web service that makes Spark a more robust back-end for running interactive notebooks.  As a result, Jupyter notebooks are now accessible within HDInsight out-of-the-box. In this scenario, we can use all of the services in Azure mentioned above with Spark with a full notebook experience to author compelling narratives and create data science collaborative spaces. Jupyter is a multi-lingual REPL on steroids. Jupyter notebook provides a collection of tools for scientific computing using powerful interactive shells that combine code execution with the creation of a live computational document. These notebook files can contain arbitrary text, mathematical formulas, input code, results, graphics, videos and any other kind of media that a modern web browser is capable of displaying. So, whether you’re absolutely new to R or Python or SQL or do some serious parallel/technical computing, the Jupyter Notebook in Azure is a great choice.

Scenario 7_Spark with Notebook

You can also use Zeppelin notebooks on Spark clusters in Azure to run Spark jobs. Zeppelin notebook for HDInsight Spark cluster is an offering just to showcase how to use Zeppelin in an Azure HDInsight Spark environment. If you want to use notebooks to work with HDInsight Spark, I recommend that you use Jupyter notebooks. To make development on Spark easier, we support IntelliJ Spark Tooling which introduces native authoring support for Scala and Java, local testing, remote debugging, and the ability to submit Spark applications to the Azure cloud.

Scenario #8: Using Excel with Spark

As a final example, I wanted to describe the ability to connect Excel to Spark cluster running in Azure using the Microsoft Open Database Connectivity (ODBC) Spark Driver. Download it here.

Scenario 8_Spark with Excel

Excel is one of the most popular clients for data analytics on Microsoft platforms. In Excel, our primary BI tools such as PowerPivot, data-modeling tools, Power View, and other data-visualization tools are built right into the software, no additional downloads required. This enables users of all levels to do self-service BI using the familiar interface of Excel. Through a Spark Add-in for Excel users can easily analyze massive amounts of structured or unstructured data with a very familiar tool.

Conclusion

Above, I’ve described some of the amazing, game-changing scenarios for real-time big data processing with Spark on Azure. Any company across the globe, from a huge enterprise to a small startup can take their business to the next level with these scenarios and solutions. The question is, what are you waiting for?



from SQL Server Blog http://ift.tt/2c3LvH2

quarta-feira, 10 de agosto de 2016

[From Technet] Five must-see speakers at the Microsoft Data Science Summit

The Microsoft Data Science Summit is filled with leading thinkers in big data, machine learning, AI, and open-source technologies. Join us, and get their insights and technical expertise as they discuss real-world challenges and innovative solutions emerging across data science. Here’s a sample of some of the speakers you’ll see—and what they’ll be talking about:

Rafal Lukawiecki, data scientist at Project Botticelli

Rafal will discuss the business opportunity of advanced analytics and the new landscape of data. He’ll speak about data science in practice and the cloud-based Cortana Intelligence Suite, especially Azure Machine Learning and the pros and cons of a variety of data storage approaches.

David Smith, R community lead at Microsoft

Whether it’s called data science, machine learning, or predictive analytics, the combination of new data sources and statistical modeling has produced some truly revolutionary applications. Many of these applications incorporate open-source technologies and research from academic institutions.

In his talk, David will share a few of the ways Microsoft is improving the lives of people around the world—and in particular, people with disabilities—by applying statistics, research, and open-source software in applications and devices. He’ll also share how you can develop such applications yourself, using the open-source R language with Microsoft’s advanced analytics products.

Danielle Dean, senior data scientist lead at Microsoft

Wee Hyong Tok, senior data science manager at Microsoft

How do businesses and data scientists work together to turn raw data into intelligent action? Why do some companies drown in volumes of data, while others thrive on turning the data into golden strategic advantages?

With Wee Hyong Tok and Danielle Dean, unlock the super powers that data scientists use to turn raw data into big results. This talk will draw on practical experiences from working on various exciting data science projects, such as:

  • Understanding the galaxies by working with citizen astronomers to create labeled datasets, and performing classification of the galaxies
  • Understanding the brain and figuring out how to decode signals from the brain using machine learning
  • Empowering aero engine manufacturers to improve aircraft efficiency, drive up aircraft availability, and reduce engine maintenance cost

The session is targeted at data scientists, developers, and database professionals with a keen interest in evolving existing skillsets and creating new value for their organizations.

Frank Seide, principal researcher at Microsoft Research

This talk will introduce CNTK, Microsoft’s cutting-edge, deep-learning toolkit.

CNTK is used to train and evaluate deep neural networks used in Microsoft products, such as the Cortana speech models. It supports feed-forward, convolutional, and recurrent networks for speech, image, and text workloads.

Frank, a key contributor to the development of CNTK, will walk us through it. He’ll discuss what you can and cannot do with CNTK, what a typical use might look like, how it works, and what algorithms it implements.

Join us. Connect in person—and dive deep.

The Microsoft Data Science Summit includes three in-depth tracks you can choose from to get the expertise you want: Advanced Analytics, Big Data, and Solutions. So if you’re a data scientist, big data engineer, or machine learning practitioner who is looking to expand your knowledge with expert insights, join us in Atlanta, September 26–27. But register soon. The summit only happens once a year, and it’s just around the corner!

> Register for Microsoft Data Science Summit



from SQL Server Blog http://ift.tt/2bgeHhX

quarta-feira, 3 de agosto de 2016

[From Technet] ODBC Driver 13.1 for SQL Server released

We are pleased to announce the full release of the Microsoft ODBC Driver 13.1 for SQL Server. The updated driver provides robust data access to Microsoft SQL Server and Microsoft Azure SQL Database for C/C++ based applications.

What’s new

Always Encrypted

You can now use Always Encrypted with the Microsoft ODBC Driver 13.1 for SQL Server. Always Encrypted is a new SQL Server 2016 and Azure SQL Database security feature that prevents sensitive data from being seen in plaintext in a SQL instance. You can now transparently encrypt the data in the application, so that SQL Server or Azure SQL Database will only handle the encrypted data and not plaintext values. If a SQL instance or host machine is compromised, an attacker can only access ciphertext of your sensitive data. Use the ODBC Driver 13.1 to encrypt plaintext data and store the encrypted data in SQL Server 2016 or Azure SQL Database. Likewise, use the driver to decrypt your encrypted data.

Azure Active Directory (AAD)

AAD authentication is a mechanism of connecting to Azure SQL Database v12 using identities in AAD. Use AAD authentication to centrally manage identities of database users and as an alternative to SQL Server authentication. The ODBC Driver 13.1 allows you to specify your AAD credentials in the ODBC connection string to connect to Azure SQL DB.

Internationalized Domain Names (IDNs)

IDNs allow your web server to use Unicode characters for server name, enabling support for more languages. Using the new Microsoft ODBC Driver 13.1 for SQL Server, you can convert a Unicode serverName to ASCII compatible encoding (Punycode) when required during a connection.

AlwaysOn Availability Groups (AG)

The driver now supports transparent connections to AlwaysOn Availability Groups. The driver quickly discovers the current AlwaysOn topology of your server infrastructure and connects to the current active server transparently.

Note: You can also download ODBC Driver 13 for SQL Server from the download center. ODBC Driver 13 for SQL Server was released with SQL Server 2016 and does not include new features such as Always Encrypted and Azure Active Directory Authentication.

Next steps

Download the ODBC Driver 13.1 for SQL Server.

Roadmap

We are committed to bringing more feature support for connecting to SQL Server, Azure SQL Database and Azure SQL DW. We invite you to explore the latest the Microsoft Data Platform has to offer via a trial of Azure SQL Database or by trying the new SQL Server 2016.

Please stay tuned for upcoming releases that will have additional feature support. This applies to our wide range of client drivers including PHP 7.0, JDBC and ADO.NET which are already available.



from SQL Server Blog http://ift.tt/2axRQMg

sexta-feira, 29 de julho de 2016

[From Technet] Don’t miss SQL Server Geeks Annual Summit 2016!

This post was co-authored by Rimma Nehme, Technical Assistant, Data Group.

I am really excited to both attend and speak at the SQL Server Geeks Annual Summit (#SSGAS2016), Asia’s Premier Data & Analytics Conference taking place on 11-13 August in Bangalore, India. SQLServerGeeks Annual Summit 2016 is a full 3-day conference with more than 100 breakout sessions and deep dive pre-con sessions on SQL Server, BI & Analytics, Cloud, Big Data, and related technologies.

This is a truly unique conference (see this video), comprised with multiple tracks on Database Management, Database Development, Business Intelligence, Advanced Analytics, Cloud, and Big Data. The summit attracts SQL experts from around the globe. SSGAS 2016 is the only Data/Analytics event in Asia where product teams from Microsoft’s Data Group fly down from Redmond to deliver advanced sessions on the latest technologies. Apart from engineering, the conference gets full participation from the SQL CAT & TIGER teams of Microsoft.

Last year’s summit was a great success, and you can see some of the feedback below.

Why should you attend?

•    To get real-world training from industry experts.
•    To hear a very thought-provoking keynote by Joseph Sirosh, CVP of Data Group at Microsoft.
•    To learn directly from our engineering and customer experts on how you can build data-driven intelligent solutions, on-premises and in the cloud.
•    To learn how SQL Server 2016 with R, Hadoop, and other advanced technologies can drive new and exciting services for your customers.
•    To network and connect with the MVPs and MCMs.
•    To talk directly to our product team members.
•    To ask questions during Open-Talks and Chalk-Talks.
•    To learn about Advanced Analytics, Cloud, and Big Data.
•    To see expert-level demo-oriented sessions.
•    To learn about the latest trends in the Data & Analytics world.
•    To hear me talk about our Planet-Scale NoSQL DocumentDB.
•    Do you really need to hear more reasons to attend? Smile

I invite you to watch the #SSGAS2016 hashtag on Twitter for new and exciting updates. Hopefully I’ll see some of you there!



from SQL Server Blog http://ift.tt/2audL7Y

quinta-feira, 28 de julho de 2016

[From Technet] Join us at the Microsoft Data Science Summit

Here’s your chance to dive deeper into the new analytics capabilities in SQL Server 2016: The Microsoft Data Science Summit.

On September 26 – 27, the Microsoft Data Science Summit will host an interactive event on the disruptive technologies and innovative solutions in big data, machine learning and the Internet of Things.
It’s a two-day intensive event featuring:

  • SQL Server sessions including U-SQL as the Choice for Processing Massive Batch Workloads and Scalable Data Science in Azure Data Lake with U-SQL: An End-to-End Walkthrough
  • Hands-on labs with Cortana Intelligence Suite, Microsoft R Server, SQL Server 2016, and open-source technologies
    Real code, real products, and real-world examples
  • Keynotes and talks from industry visionaries
  • Incredible demos
  • Relaxed opportunities to connect with peers and Microsoft product experts

Join us. See what’s possible in big data, analytics and AI, now.

So if you’re a data scientist, big data engineer, machine learning specialist, or team leader who wants insights into the latest big data, machine learning, and advanced analytics technologies, join us. We’ll have three in-depth tracks you can choose from to get the knowledge and hands-on experience you want for your business: Advanced Analytics, Big Data, and Solutions. Take a look at all three tracks to find the best match for your needs.

Register soon. September 26 – 27 is approaching fast. We look forward to talking data with you in Atlanta!

> Register for Microsoft Data Science Summit



from SQL Server Blog http://ift.tt/2atmsOc

quarta-feira, 27 de julho de 2016

[From Technet] ODBC Driver 13.1 for SQL Server released

We are pleased to announce the full release of the Microsoft ODBC Driver 13.1 for SQL Server. The updated driver provides robust data access to Microsoft SQL Server and Microsoft Azure SQL Database for C/C++ based applications.

What’s new

Always Encrypted

You can now use Always Encrypted with the Microsoft ODBC Driver 13.1 for SQL Server. Always Encrypted is a new SQL Server 2016 and Azure SQL Database security feature that prevents sensitive data from being seen in plaintext in a SQL instance. You can now transparently encrypt the data in the application, so that SQL Server or Azure SQL Database will only handle the encrypted data and not plaintext values. If a SQL instance or host machine is compromised, an attacker can only access ciphertext of your sensitive data. Use the ODBC Driver 13.1 to encrypt plaintext data and store the encrypted data in SQL Server 2016 or Azure SQL Database. Likewise, use the driver to decrypt your encrypted data.

Azure Active Directory (AAD)

AAD authentication is a mechanism of connecting to Azure SQL Database v12 using identities in AAD. Use AAD authentication to centrally manage identities of database users and as an alternative to SQL Server authentication. The ODBC Driver 13.1 allows you to specify your AAD credentials in the ODBC connection string to connect to Azure SQL DB.

Internationalized Domain Names (IDNs)

IDNs allow your web server to use Unicode characters for server name, enabling support for more languages. Using the new Microsoft ODBC Driver 13.1 for SQL Server, you can convert a Unicode serverName to ASCII compatible encoding (Punycode) when required during a connection.

AlwaysOn Availability Groups (AG)

The driver now supports transparent connections to AlwaysOn Availability Groups. The driver quickly discovers the current AlwaysOn topology of your server infrastructure and connects to the current active server transparently.

Note: ODBC Driver 13 for SQL Server was released with SQL Server 2016 RTW. For new features such as Always Encrypted and Azure Active Directory authentication use ODBC Driver 13.1 for SQL Server.

Next steps

Download ODBC Driver 13.1 for SQL Server.

Roadmap

We are committed to bringing more feature support for connecting to SQL Server, Azure SQL Database and Azure SQL DW. We invite you to explore the latest the Microsoft Data Platform has to offer via a trial of Microsoft Azure SQL Database or by trying the new SQL Server 2016.

Please stay tuned for upcoming releases that will have additional feature support. This applies to our wide range of client drivers including PHP 7.0, JDBC and ADO.NET which are already available.



from SQL Server Blog http://ift.tt/2awcvCp

quarta-feira, 20 de julho de 2016

[From Technet] Microsoft drivers 4.0 for PHP for SQL Server with PHP 7.0 support released

Dear PHP Community,

We wanted to extend a massive ‘thank you’ for providing feedback for our preview releases over the last few weeks. We’ve been working hard to incorporate the feedback you have provided us. You will find that we’ve fixed many issues you reported, and we are proud to be able to release the final build of our 4.0 drivers. We will continue to fix bugs and ship regular updates to the GitHub repository. The new driver enables access to SQL Server 2008+, Azure SQL Database and Azure SQL DW from any PHP 7 application.

The major highlights of this release include: support for SQL Server 2016, PHP7, bug fixes, and better test coverage.

Improvements from our previous release:

  • Fixed a heap corruption when binding parameters in a prepare statement with error
  • Fixed leaks in SQLSRV streams and output parameters handling
  • Fixed leaks in SQLSRV fetch object
  • Fixed leaks in SQLSRV binding object parameters
  • Fixed leaks in SQLSRV buffered result set
  • Fixed leaks in SQLSRV getting datetime and stream fields
  • Fixed leaks in PDO_SQLSRV field cache
  • Fixed leaks in PDO_SQLSRV construct when connecting with error
  • Fixed leaks in PDO_SQLSRV exception handling

We will continue to make bug fixes and adding new features on your feedback on GitHub.

Future plans

Going forward we plan on improving the current Linux port, expand SQL 16 Feature Support (example: Always Encrypted), build verification/fundamental tests, and bug fixes reported on GitHub.

Getting the product ready for release

You can find the latest bits on our Github repository, at our existing address. We provide support for any bugs reported on our Github Issues page. As always, we welcome contributions of any kind, be they Pull Requests, or Feature Enhancements. Additionally, you can also get the pre-packaged exe. from the Download Center.

I’d like to thank everyone on behalf of the team for supporting us in our endeavors to provide you with a high-quality driver. Happy downloading!

Meet Bhagdev (meetb@microsoft.com)

MSFTlovesPHP



from SQL Server Blog http://ift.tt/2agGe1C

segunda-feira, 18 de julho de 2016

[From Technet] SQL Server 2016 posts world record TPC-H 10 TB benchmark

SQL Server 2016 delivers unparalleled performance and security built-in for your most mission critical transactional systems and data warehouses, along with an integrated business intelligence and advanced analytics solution for building intelligent applications.  Blazing-fast performance is key to ensuring you can deliver a flawless transactional experience while at the same time support demanding real-time operational analytics over the data as fast as the data is coming in.

Recently, Lenovo announced the number one TPC-H 10TB benchmark world record1 using SQL Server 2016 and Windows Server 2016 on Lenovo System x3850 X6 using the latest the latest Intel Xeon E7 processor technology. In May 2016, Lenovo also published a new number one TPC-H 30TB world record2 using SQL Server 2016 and Windows Server 2016 on Lenovo System x3950 X6. These results, in addition to recent benchmarks by software and hardware partners, as well as key applications, show that SQL Server 2016 is the fastest in-memory database on the planet for your applications.3

SQL Server 2016 owns the top TPC-E performance benchmarks4 for transaction processing, the top TPC-H performance benchmarks for data warehousing, and the top performance benchmarks with leading business applications. PROS Holdings uses SQL Server 2016’s superior performance and built-in R Service to deliver advanced analytics more than 100x faster than before, resulting in higher profits for their customers. KPMG, a leader in audit, tax, and advisory solution, posted 2.5x faster execution time with ten times the table compression with their solution using SQL Server 2016.

Customers can also gain tremendous performance improvement by simply upgrading to SQL Server 2016 without application changes (e.g. queries will run up to 34x faster)5. In addition to leading performance benchmarks, SQL Server 2016 also delivers top price/performance for both workloads providing customers with significantly reduced total cost of ownership.

Easily experience SQL Server 2016 by creating a test environment using an Azure SQL VM. You can also experience the full features through the free developer edition (you will be prompted to sign in to Visual Studio Dev Essentials before you can download SQL Server 2016 Developer Edition). Visit SQL Server 2016 to learn more about new features and download the SQL Server 2016 e-book.

SQL Server 2016 performance

 

1Non-clustered TPC-H 10TB. http://ift.tt/29P8jHJ. http://www.tpc.org/3325.

2Non-clustered TPC-H 30 TB. http://ift.tt/29Vo2Zw. http://www.tpc.org/3321.

3Learn more about how your organization can scale to handle the increasing amount of data being stored in modern data warehouses by reading the Intel whitepaper entitled “Accelerating Large-Scale Business Analytics,” which illustrates the integration of Microsoft SQL Server 2016 and Intel® Xeon® E7 platform driving advanced analytics on a large 100TB dataset.

4http://ift.tt/29P7T46

5Based on internal tests from Microsoft, customers who upgrade to SQL Server 2016 will also experience tremendous performance gain including faster real-time analytics with up to 34x performance on in-memory columnstore queries, faster synchronization and greater availability with up to seven times faster AlwaysOn throughput, 3.6x faster reporting on AlwaysOn replicas and seven to ten times faster on database maintenance (DBCC).



from SQL Server Blog http://ift.tt/29VotDn

quinta-feira, 14 de julho de 2016

[From Technet] Microsoft JDBC Driver 6.0 for SQL Server is now released!

This post was authored by Andrea Lam, Program Manager, SQL Server.

We are pleased to announce the full release of the Microsoft JDBC Driver 6.0 for SQL Server! The updated driver provides robust data access to Microsoft SQL Server and Microsoft Azure SQL Database for Java-based applications.

What’s new

Always Encrypted

You can now use Always Encrypted with the Microsoft JDBC Driver 6.0 for SQL Server. Always Encrypted is a new SQL Server 2016 and Azure SQL Database security feature that prevents sensitive data from being seen in plaintext in a SQL instance. You can now transparently encrypt the data in the application, so that SQL Server or Azure SQL Database will only handle the encrypted data and not plaintext values. If a SQL instance or host machine is compromised, an attacker can only access ciphertext of your sensitive data. Use the JDBC Driver 6.0 to encrypt plaintext data and store the encrypted data in SQL Server 2016 or Azure SQL Database. Likewise, use the driver to decrypt your encrypted data.

Azure Active Directory (AAD)

AAD authentication is a mechanism of connecting to Azure SQL Database v12 using identities in AAD. Use AAD authentication to centrally manage identities of database users and as an alternative to SQL Server authentication. The JDBC Driver 6.0 allows you to specify your AAD credentials in the JDBC connection string to connect to Azure SQL DB.

Table-Valued Parameters (TVPs)

TVP support allows a client application to send parameterized data to the server more efficiently by sending multiple rows to the server with a single call. You can use the JDBC Driver 6.0 to encapsulate rows of data in a client application and send the data to the server in a single parameterized command.

Parameterized queries

Extended support for retrieving parameter metadata with prepared statements for complex queries such as sub-queries and/or joins.

Internationalized Domain Names (IDNs)

IDNs allow your web server to use Unicode characters for server name, enabling support for more languages. Using the new Microsoft JDBC Driver 6.0 for SQL Server, you can convert a Unicode serverName to ASCII compatible encoding (Punycode) when required during a connection.

AlwaysOn Availability Groups (AG)

The driver now supports transparent connections to AlwaysOn Availability Groups. The driver quickly discovers the current AlwaysOn topology of your server infrastructure and connects to the current active server transparently.

Next steps

You can download the JDBC Driver 6.0 for SQL Server here.

Learn how to pick the right JDBC jar file based on your system requirements here and read up on more documentation here.

Roadmap

We are committed to bringing more feature support for connecting to SQL Server, Azure SQL Database and Azure SQL DW. We invite you to explore the latest the Microsoft Data Platform has to offer via a trial of Microsoft Azure SQL Database or by trying the new SQL Server 2016.

Please stay tuned for upcoming releases that will have additional feature support. This applies to our wide range of client drivers including PHP 7.0, Node.js, ODBC and ADO.NET which are already available.



from SQL Server Blog http://ift.tt/29RyeCE

terça-feira, 12 de julho de 2016

[From Technet] SQL Server 2014 SP2 is now available

The SQL Server team is excited to bring you SQL Server 2014 Service Pack 2 (SP2). This service pack is available on the Microsoft Download Center and will also be available on MSDN, MBS/Partner Source and VLSC in the coming weeks. As part of our continued commitment to software excellence for our customers, this upgrade is available to all customers with existing SQL Server 2014 deployments.

SQL Server 2014 SP2 includes a rollup of released hotfixes as well as more than twenty improvements centered around performance, scalability and diagnostics based on feedback from customers and the SQL community. These improvements enable SQL Server 2014 to perform faster and scale out of the box on modern hardware design, and showcase the SQL Server Team’s commitment to provide continued value into in-market releases.

SQL Server 2014 SP2 will include:

  • All fixes and CUs for SQL 2014 released to date.
  • Performance, scale and supportability improvements.
  • New improvements based on connect feedback items filed by the SQL community.
  • Improvements originally introduced in SQL 2012 SP3, after SQL 2014 SP1 was released.

Find more information on SQL Server 2014 SP2 by reading the SQL Server Release Services Blog post and What’s New in SQL Server 2014 SP2. To obtain SQL Server 2014 SP2, the options below are available:

SQL Server 2014 SP2

SQL Server 2014 SP2 Express

SQL Server 2014 SP2 Report Builder

SQL Server 2014 SP2 Master KB

SQL Server 2014 SP2 Feature Packs

SP2 Reporting Services Add-in for Microsoft SharePoint 

SP2 Semantic Language Statistics 



from SQL Server Blog http://ift.tt/29vPMk1