quarta-feira, 17 de maio de 2017

[From Technet] Try new SQL Server command line tools to generate T-SQL scripts and monitor Dynamic Management Views

This post was authored by Tara Raj and Vinson Yu, Program Managers – SQL Server Team

We are excited to announce the public preview availability of two new command line tools for SQL Server:

  • The mssql-scripter tool enables developers, DBAs, and sysadmins to generate CREATE and INSERT T-SQL scripts for database objects in SQL Server, Azure SQL DB, and Azure SQL DW from the command line.
  • The DBFS tool enables DBAs and sysadmins to monitor SQL Server more easily by exposing live data from SQL Server Dynamic Management Views (DMVs) as virtual files in a virtual directory on Linux operating systems.

Read on for detailed usage examples, try out these new command line tools, and give us your feedback.

mssql-scripter

Mssql-scripter is the multiplatform command line equivalent of the widely used Generate Scripts Wizard experience in SSMS.

You can use mssql-scripter on Linux, macOS, and Windows to generate data definition language (DDL) and data manipulation language (DML) T-SQL scripts for database objects in SQL Server running anywhere, Azure SQL Database, and Azure SQL Data Warehouse. You can save the generated T-SQL script to a .sql file or pipe it to standard *nix utilities (for example, sed, awk, grep) for further transformations. You can edit the generated script or check it into source control and subsequently execute the script in your existing SQL database deployment processes and DevOps pipelines with standard multiplatform SQL command line tools such as sqlcmd.

Mssql-scripter is built using Python and incorporates the usability principles of the new Azure CLI 2.0 tools. The source code can be found on Github at http://ift.tt/2rrod57, and we welcome your contributions and pull requests!

Get started with mssql-scripter

Install
$pip install mssql-scripter
For additional installation tips, visit http://ift.tt/2rrHZxs.

Script Your First Database Objects
For usage and help content, pass in the -h parameter, which will also show all options:
mssql-scripter -h

Here are some example commands
# Generate DDL scripts for all database objects (default) in the Adventureworks database and output to stdout
$ mssql-scripter -S localhost -d AdventureWorks -U sa

# Generate DDL scripts for all database objects and DML scripts (INSERT statements) for all tables in the Adventureworks database and save the script to a file
$ mssql-scripter -S localhost -d AdventureWorks -U sa –schema-and-data  > ./adventureworks.sql

# generate DDL scripts for objects that contain “Employee” in their name to stdout
$ mssql-scripter -S localhost -d AdventureWorks -U sa –include-objects Employee

# Change a schema name in the generated DDL script on Linux and macOS and bash in Windows 10.
# 1) Generate DDL scripts for the Adventureworks database
# 2) Pipe results and change all occurrences of SalesLT to SalesLT_test using sed, and save the script to a file
$ mssql-scripter scripter -S localhost -d Adventureworks -U sa | sed -e “s/SalesLT./SalesLT_test./g” > adventureworks_SalesLT_test.sql

DBFS

A big part of operationalizing SQL Server is to monitor to ensure that SQL Server is performant, highly available, and secure for your applications. With SQL Server 2017, Dynamic Management Views (DMVs) on Windows are also accessible on Linux, allowing your existing scripts and tools that rely on DMVs to continue to work. Traditionally, to get this information, you would use GUI admin tools such as SSMS or command line tools such as SQLCMD to run queries.

Today, we are also introducing a new experimental Linux tool, DBFS, which enables you to access live DMVS mounted to a virtual filesystem using FUSE. All you need to do is view the contents of the virtual files in the mounted virtual directory to see the same data you would see as if you ran a SQL query to view the DMV data. There is no need to log in to the SQL Server using a GUI or command line tool or run SQL queries. DBFS can also be used in scenarios where you want to access DMV data from the context of a script with CLI tools such as grep, awk, and sed.

DBFS uses the FUSE file system module to create two zero byte files for each DMV—one for showing the data in CSV format and one for showing the data in JSON format. When a file is “read,” the relevant information from the corresponding DMV is queried from SQL Server and displayed just like the contents of any CSV or JSON text file.

Features

  • Access data in .json format if you are connected to SQL Server 2016 or later
  • Compatible with Bash tools such as grep, sed, and awk
  • Live DMV data at time of access
  • Works with both SQL Server on Windows and SQL Server on Linux

Notes

  • This tool is currently only available for Ubuntu, Red Hat, and CentOS (SUSE coming soon!).

Next Steps:
See more usage examples and read more about mssql-scripter at http://ift.tt/2rrod57 and get started with the DBFS today at http://ift.tt/2qS3kmJ.

We are open to suggestions, feedback, questions, and of course contributions to the project itself.



from SQL Server Blog http://ift.tt/2qS7sTI

[From Technet] SQL Server 2017 CTP 2.1 now available

Microsoft is excited to announce a new preview for the next version of SQL Server (SQL Server 2017). Community Technology Preview (CTP) 2.1 is available on both Windows and Linux. In this preview, we added manageability features to make it easier to configure SQL Server in Docker containers. We’re also introducing some new command line tools for managing SQL Server in our GitHub repo. And, there’s a preview of SQL Server Integration Services on Linux to try! You can try the SQL Server 2017 preview in your choice of development and test environments now: http://ift.tt/2qS6K98.

Key CTP 2.1 enhancements

The primary enhancement to SQL Server 2017 in this release is the ability to configure SQL Server configuration settings through environment variables passed in as parameters to docker run. This enables many of the SQL Server configuration scenarios in Docker containers such as setting the collation.

For additional detail on CTP 2.1, please visit What’s New in SQL Server 2017, Release Notes and Linux documentation.

SQL Server Integration Services on Linux

SQL Server Integration Services now supports Linux for the first time! Today we are also releasing a preview of SQL Server Integration Services for Ubuntu. The preview enables you to run SSIS packages on the Linux OS, extract data from and load it to most common sources and targets, and perform common transformations. It has a simple command line installation. For more information, see our SSIS blog.

Updated SQL Server Tooling

The latest release of SQL Server Management Studio is out! It features improvements to how it works with SQL Server on Linux so make sure you have the latest. In addition, we are excited to announce the public preview availability of two new command line tools for SQL Server:

  • The mssql-scripter tool enables developers, DBAs, and sysadmins to generate CREATE and INSERT T-SQL scripts for database objects in SQL Server, Azure SQL DB, and Azure SQL DW from the command line.
  • The DBFS tool enables DBAs and sysadmins to monitor SQL Server more easily by exposing live data from SQL Server Dynamic Management Views (DMVs) as virtual files in a virtual directory on Linux operating systems.

New lightweight installer for SQL Server Reporting Services (SSRS)

In CTP 2.1, we moved Reporting Services installation from the SQL Server installer to a separate installer. This is a packaging change, not a product change; access to SQL Server Reporting Services is still included with your SQL Server license. The new installation process keeps our packages lean and enables customers to deploy and update Reporting Services with zero impact on your SQL Server deployments and databases.

To learn more about what’s new in SQL Server 2017 Reporting Services preview, read our Reporting Services Release Notes. To download the latest preview in the new lightweight installer, go to http://ift.tt/2qRVUzQ

To learn more about the recent announcement of a Power BI Report Server preview, which includes the capabilities of SQL Server 2017 Reporting Services and support for Power BI reports, you can read this blog article.

Get SQL Server 2017 CTP 2.1 today!

Try the preview of the SQL Server 2017 today! Get started with the preview of SQL Server with our updated developer tutorials that show you how to install and use SQL Server 2017 on macOS, Docker, Windows, and Linux and quickly build an app in a programming language of your choice.

Have questions? Join the discussion of SQL Server 2017 at MSDN. If you run into an issue or would like to make a suggestion, you can let us know through Connect. We look forward to hearing from you!



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[From Technet] SQL Server 2017 on Linux surpasses 1 million Docker pulls as the next preview version rolls out

This post was authored by Rohan Kumar, General Manager, Database Systems Group

SQL Server 2017 makes it easier and simpler to work with data, with more deployment options than before and monthly preview releases offering regular innovation and improvements. The momentum behind these new options is clear. We are excited to mark a new milestone: Last week, SQL Server on Linux passed 1 million pulls of its container image! The image has been on Docker Hub for the six months since we first launched the SQL Server on Linux public preview in November 2016, with steadily growing customer use. In fact, we now have customers like dv01 going into production with SQL Server 2017 in Docker containers using the production support agreement from our Early Adoption Program (EAP). The container image is also available in the Docker Store, where it’s currently one of the featured images.

Customer interest in containers is high because of the benefits for production, and especially development and test: consistent and reliable behavior across environments, in a lightweight and easy- to-use format. Containers are fast to set up, can easily be stopped and started, and give users the ability to spin up multiple containers together using tools like docker-compose to easily start and interconnect database, application, and other services containers in a micro-services architecture.

SQL Server on Linux containers has been tested extensively in our test lab over the course of SQL Server 2017 public previews. We have been deploying SQL Server on a 150-node Kubernetes cluster in Azure to test each successive monthly Community Technology Preview (CTP). For each test pass, we automatically deploy 750 containers and run over a million tests. In addition to Kubernetes, we are testing on other container platforms with our partners and the community, including Red Hat OpenShift, Docker Swarm, and Mesosphere DC/OS.

Financial technology startup cuts database management time by 90 percent

Customers are already adopting SQL Server in containers. dv01 is a Wall Street startup, offering a reporting and analytics platform to institutional investors interested in greater insight into consumer lending markets. dv01 had initially based its solution on PostgreSQL and Amazon Redshift, but moved to SQL Server 2016 in Windows Azure Virtual Machines for faster query response times and scalability as its data grew. Because the firm runs all its other workloads on Linux, dv01 signed up for the Early Adoption Program for SQL Server 2017 to get Microsoft advice and assistance on migrating its solution to SQL Server on Linux. This move will help the company avoid managing multiple operating systems within its environment. It opted to deploy the application to production on Docker Engine, using a SQL Server 2017 on Linux image. Its choice to implement SQL Server and Docker containers has cut database management time by 90 percent, freeing its development team to focus on adding new capabilities to the product. To learn more about dv01’s SQL Server 2017 journey, you can read its story here.

“SQL Server 2016 offered the combination of performance and scalability that we needed,” said Dean Chen, VP of Engineering, dv01. “Expensive queries that were taking 30 seconds or more with our previous system now take 1-2 seconds, which means we’re able to do analytics queries in close to real time for our users.”

Making SQL Server on a Linux Docker container easy

With SQL Server 2017 CTP 2.1, available today, we continue to add to the manageability features for SQL Server on Linux Docker containers. We have introduced the ability to configure the SQL Server configuration settings through environment variables passed as parameters to docker run. This enables some of the most common SQL Server configuration scenarios in Docker containers, such as setting the server collation when creating a new SQL Server instance in a container. If you’d like to learn more about the SQL Server 2017 CTP 2.1 release, read our detailed blog for information on the other enhancements and how to get started with the preview.

We want to make it as easy as possible to get started with this technology. If you’d like to learn about how to get started with building a data-centric CI/CD pipeline using SQL Server on Linux containers, join SQL Server engineers Travis Wright and Tobias Ternstrom for this how-to video from the Microsoft Build event for developers.

Reasons to consider running SQL Server in containers

In many ways, container technology is at an inflection point much like hypervisors were 15 years ago. The benefits are immense and increasing every day and include the following:

  • Reduced size on disk for better hardware utilization
  • Reduced CPU/memory consumption, which also results in better hardware utilization
  • Reduced deployment size for faster deployments and scale up/down
  • Reduced patching for less effort, less vulnerability, less down time
  • Better composability using layers of Images, applications defined as multiple containers
  • Easier sharing with Docker Hub and Registry

But in some cases, there are still areas for improvement. For example, configuring high availability in a container platform is not well defined yet. Persistence to local and remote storage is still relatively new and is a complex area of any container platform. Because containers are still new, finding people that are experienced in working with containers can be a challenge. We look forward to working with the community to expand on and refine the capabilities of container platforms in the months to come.

The road ahead for SQL Server in containers

We are targeting support for SQL Server on Linux containers by General Availability of SQL Server 2017 later this year. Customers in our Early Adoption Program can deploy into production on containers right now with full support of our support and engineering teams. We have created a GitHub repository called mssql-docker where you can get Dockerfiles, example entrypoint scripts, and provide us with feedback and feature requests. It’s also a great place to engage with other people running SQL Server in containers.

We are also working on testing SQL Server in Windows containers, including SQL Server 2016 SP1 Developer and Express editions and SQL Server 2017 Evaluation edition. The Windows container images are available now on Docker Hub for testing and experimentation as well.

Thanks again to our community for your interest in and support for SQL Server in containers. We look forward to your continued feedback.

–Rohan Kumar, General Manager, Database Systems Group



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terça-feira, 16 de maio de 2017

[From Technet] Keeping freight moving in Denmark with SQL Server 2016

Danske Logo

Efficient data management keeps goods flowing smoothly in Denmark. Danske Fragtmaend, the country’s largest national transport and distribution firm, has been moving freight for more than a century. Today, Danske Fragtmaend delivers more than 40,000 consignments each day throughout Denmark, and businesses from small mom-and-pop operations to factories rely on its services.

The firm handles logistics in a central location, where 200 dispatchers keep an eye on the movement of thousands of trucks and their cargo. Both drivers and dispatchers need the latest information to operate efficiently, so they rely on a data platform based on SQL Server 2016. The storage system includes 160 terabytes of flash memory for fast I/O and high uptimes. Throughout the day, drivers continually scan transactions with PDAs and send shipping information including GPS coordinates to the data platform. Fast access to information is essential. Ulf Preisler, chief information officer at Danske Fragtmaend, says, “When it comes to short-term logistics, you’ve got to think like an air traffic controller more than a traditional radio dispatcher.”

Because the data changes rapidly, asynchronous replication between geographically disparate datacenters was inadequate. Instead, Danske Fragtmaend runs SQL Server on Windows 2016. Windows Server 2016 introduces a new disaster recovery and preparedness feature, Storage Replica, which enables storage-agnostic, synchronous replication of data across geographically diverse datacenters. Even if disaster strikes one location, all the data exists elsewhere, so there is no possibility of loss.

27.11.2008.Modulvogntog

Best of all, companies that combine flash storage with the latest versions of SQL Server and Windows Server can achieve a multiplying effect on performance. Danske Fragmaend’s lead software developer, Morten Vinther, ran several tests to compare the old storage stack with the new one. “After combining the new all-flash infrastructure and the features from SQL Server 2016 on Windows Server 2016, one of our BI queries ran 9,521 times faster than on the prior infrastructure. That is much more than we expected.”

To find out more about Danske Fragtmaend’s SQL Server 2016 implementation, read the customer story.

Customer Name: Danske Fragtmaend
Industry: Transportation and logistics
Country or Region: Denmark
Customer Website: www.fragt.dk
Employee Size: 900



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[From Technet] SQL Server Command Line Tools for macOS released

This post was authored by Meet Bhagdev, Program Manager, Microsoft

We are delighted to share the production-ready release of the SQL Server Command Line Tools (sqlcmd and bcp) on macOS El Capitan and Sierra.

The sqlcmd utility is a command-line tool that lets you submit T-SQL statements or batches to local and remote instances of SQL Server. The utility is extremely useful for repetitive database tasks such as batch processing or unit testing.

The bulk copy program utility (bcp) bulk copies data between an instance of Microsoft SQL Server and a data file in a user-specified format. The bcp utility can be used to import large numbers of new rows into SQL Server tables or to export data out of tables into data files.

Install the tools for macOS El Capitan and Sierra

/usr/bin/ruby -e “$(curl – fsSL http://ift.tt/YQTuQh)
brew tap microsoft/mssql-release http://ift.tt/2qNZsD8
brew update
brew install mssql-tools
#for silent install ACCEPT_EULA=y brew install mssql-tools

Get started

SQLCMD
sqlcmd -S localhost -U sa -P <your_password> -Q “<your_query>”

BCP
bcp <your table>in ~/test_data.txt -S localhost -U sa -P <your password>-d<your database> -c -t ‘,’
bcp <your table>out ~/test_export.txt -S localhost -U sa -P<your password> -d<your database> -c -t ‘,’

For more information, check out some examples for sqlcmd and bcp.

Please file bugs, questions or issues on our Issues page. We welcome contributions, questions and issues of any kind.

brew-install-mssql-tools



from SQL Server Blog http://ift.tt/2pGN2Nb

segunda-feira, 15 de maio de 2017

[From Technet] Five reasons to run SQL Server 2016 on Windows Server 2016 – No. 5: Consistent data environment across hybrid cloud environments

COnsistent data

Have you ever seen a tree that simultaneously bears completely different species of fruit? It’s a real thing: apples, plums, oranges, lemons, and peaches all growing on the same tree. The growers have the advantage of a consistent environment (the same tree) that allows them to be efficient with resources, pick the type of fruit they need when they need it, and always have the right kind of fruit without having to invest in specialized plants.

Those trees are like the consistent foundation shared by SQL Server 2016, Windows Server 2016, and Microsoft Azure: Common code underlying the Microsoft platform makes it possible to run your data workloads seamlessly on-premises, in a hybrid environment, or strictly in the cloud—and to pick the option you need, while moving easily from one environment to the other.

Common code = Unique value

The common code base creates a write-once-deploy-anywhere SQL Server and Windows Server experience. You have flexibility across physical on-premises machines, private cloud environments, third-party hosted private cloud environments, public cloud, and hybrid deployments. Figure 1 diagrams this unique platform.

Figure 1: Microsoft Data Platform: On-premises, hybrid, and cloud

Figure 1

This means that you can choose a hybrid deployment and take advantage of any of the four basic options for hosting SQL Server:

  1. SQL Server in on-premises non-virtualized physical machines
  2. SQL Server in on-premises virtualized machines
  3. SQL Server on Azure Virtual Machine. This is SQL Server installed and hosted in the cloud on Windows Server virtual machines (VMs) running on Azure. Also known as infrastructure as a service (IaaS), it is optimized to “lift and shift” existing SQL Server applications to the cloud. All versions and editions of SQL Server are available, including free ones for dev/test and lightweight workloads.
  4. Azure SQL Database (Microsoft public cloud). This is a SQL Server database native to the cloud and compatible with most SQL Server features. It is also known as a platform as a service (PaaS) database or a database as a service (DBaaS). It delivers all the agility and world-class security features of Azure and is ideal for software as a service (SaaS) app development.

When you run SQL Server on Windows Server, whether on-premises or in an IaaS virtual machine, you get the benefit of:

  • Improved database performance and availability with support for up to 24 terabytes of memory and 640 cores on a single server.
  • Built-in security at the operating system level. For example, when database admins can use a single Active Directory management pane across Azure and on-premises machines to set policies, enable/disable access, etc., it truly raises the security bar across the organization.
  • Simple and seamless upgrades with Rolling Upgrades.
  • Ability to make SQL highly available on any cloud with Storage Spaces Direct to create virtual shared storage across VMs.
  • Access to new classes of direct-attach storage (such as NVMe) for applications that require redundant storage across machines.
  • Reduce costs of hosting additional VMs by leveraging a Cloud Witness.

You benefit from the ability to use familiar server products, development tools, and technical expertise across all environments. No other platform delivers across this spectrum of implementations and builds in hybrid capabilities everywhere. Learn how to choose Azure SQL (PaaS) Database or SQL Server on Azure VMs (IaaS).

Free migration tools

Further easing the way to hybrid and cloud solutions are the SQL Azure Migration Wizard and other free migration tools. These are designed to provide easy migration of Windows Server 2016 servers to virtual machines in the cloud.

When determining how much hardware to allocate for certain applications, downsizing datacenters, or migrating existing workloads to virtual machines (VMs), you can tap into cloud capabilities in several ways:

  • Backup to Azure, including, managed backup, backup to Azure Block Blobs, and Azure Storage snapshot backup.
  • The Azure Site Recovery tool to migrate workloads on on-premises VMs and physical servers to run on Azure VMs, with full replication and backup, Azure IaaS VMs between Azure regions, and AWS Windows instances to Azure IaaS VMs. Easy addition of an Azure node to an AlwaysOn Availability Group in a hybrid environment.
  • Two new limited previews, Azure Database Migration Service and Azure SQL Database – Managed Instance, create a great path for customers looking for a way to easily modernize their existing database environment to a fully managed PaaS service without application redesign.

SQL Server License Mobility and Azure Hybrid Use Benefit for Windows Server

Even licensing is designed to ensure that wherever you deploy, you can cost-effectively take advantage of all the options.

  • SQL Server customers with active Software Assurance can use existing licenses on Azure Virtual Machines with no extra charges to SQL Server licensing. Simply assign core licenses equal to the virtual cores in the VM, and pay only for VM compute costs.
  • License Mobility ensures you can easily move SQL Server databases to the cloud using your existing licensing agreement with active Software Assurance. No additional licensing is required for SQL Server passive high availability (HA) nodes; you can configure a passive VM with up to the same compute as your active node to deliver uptime.
  • Windows Server customers with Software Assurance can save up to 40 percent by leveraging on-premises licenses to move workloads to Azure VMs with this Azure Hybrid Use Benefit.

SQL Server 2016 with Windows Server 2016: Built for hybrid cloud

Microsoft continues to build in innovation so that organizations do not have to purchase expensive add-ins to get the benefits of the cloud with security features, simplicity, and consistency across on-premises and the cloud. Together, SQL Server 2016 and Windows Server 2016 will bear fruit for your organization. Get started on hybrid now.

Learn more about SQL Server in Azure VM in this datasheet.

Try SQL Server in Azure.

Improve security, performance, and flexibility with SQL Server 2016 and Windows Server 2016

By running SQL Server 2016 and Windows Server 2016 together you can unlock the full potential of the Microsoft data platform. This series of blogs on five reasons to run these two new releases together barely scratches the surface. What’s the best way to find out just how powerful this combination is? Try it out! Download your free trial of Windows Server 2016 and SQL Server 2016 today.



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[From Technet] ODBC Driver 13.1 for macOS released

This post was authored by Meet Bhagdev, Program Manager, Microsoft

Hi, all. We are delighted to share the production-ready release of the Microsoft ODBC Driver 13.1 for macOS El Capitan and Sierra. The new driver enables access to SQL Server, Azure SQL Database and Azure SQL DW from any C/C++ application on macOS.

Added

Native install experience

The driver can now be installed with brew. Instructions on how to do this are below.

Azure AD support

You can now use Azure AD authentication (username/password) to centrally manage identities of database users and as an alternative to SQL Server authentication.

Always Encrypted support

You can now use Always Encrypted. Always Encrypted lets you transparently encrypt the data in the application, so that SQL Server will only handle the encrypted data and not plaintext values. Even if the SQL instance or the host machine is compromised, an attacker gets ciphertext of the sensitive data.

Table Valued Parameters (TVP) support

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 ODBC Driver 13.1 to encapsulate rows of data in a client application and send the data to the server in a single parameterized command.

AlwaysOn support

This release supports AlwaysOn high availability and disaster recovery. Customers have the option to use AlwaysOn by using features with capabilities like these:

  • Ability to specify whether an application is connecting to an AlwaysOn Availability Group Cluster (multi subnet failover)
  • Ability to specify application intent as read-only versus read/write
  • Ability to make 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.

BCP API support

You can use functions through the ODBC driver as described here.

SQL Server 2017 support

This release is tested and certified against the latest SQL Server release.

TLS 1.2 support

This release supports TLS 1.2 connections to SQL Server.

Install the ODBC Driver on macOS

  1. /usr/bin/ruby -e “$(curl -fsSL http://ift.tt/YQTuQh)
  2. brew tap microsoft/msodbcsql https://github.com/Microsoft/homebrew-mssql-release
  3. brew update
  4. brew install msodbcsql
  5. #for silent install ACCEPT_EULA=y brew install msodbcsql

Try our sample

Once you install the driver that runs on a supported macOS distro, you can use this C sample to connect to SQL Server/Azure SQL DB/Azure SQL DW. To download the sample and get started, follow these steps:

  1. wget http://ift.tt/2kmDUXE
  2. gcc sample_c_linux.c -o sample_c_linux -lodbc -w #make sure you change the servername, username and password in the connections string
  3. ./sample_c_linux

Please file bugs, questions and issues on our Issues page. We welcome contributions, questions and issues of any kind. Happy programming!

Meet Bhagdev (meetb@microsoft.com)

brewinstallmsodbcsql



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[From Technet] Extend your app to support SQL Server 2017 on Linux

This post was authored by Vin Yu, Program Manager, SQL Engineering

Microsoft recently announced Community Technology Preview (CTP) 2.0, the first production-quality preview of SQL Server 2017. To help our independent software vendor (ISV) partners leverage the latest enhancements, we are expanding our Data Accelerator program to support SQL Server 2017 on Linux.

Leverage the latest enhancements in SQL Server 2017

Both SQL Server 2016 SP1 and SQL Server 2017 include many performance and security innovations that can be leveraged with minimal changes to the application. These include features such as In-Memory OLTP, which uses memory as the main primary store for fast access backed by a copy of this data on disk for durability, and Always Encrypted, which helps prevent access to highly sensitive data in the database by enabling enhanced client drivers to automatically encrypt and decrypt the data. Because the key is stored outside of SQL Server, anyone with access to the database would only ever see cipher text. These programmability features are built on SQL Server, the database with the least number of vulnerabilities of any major database over the last seven years[1] in the National Institute of Standards and Technology (NIST) vulnerability database. These features and many more are available across all editions, on Windows and Linux, adding value to your application. Learn more about the programmability changes we introduced in SQL Server 2016 SP1 in this video.

With SQL Server 2017, Microsoft continues to add new programmability and performance features to support modern applications with improvements such graph data support, online index rebuild, and Adaptive Query Processing. Applications can create, store, and analyze graph data, which includes the ability to query across SQL tables and graph data. With resumable online index rebuild, you can resume a paused index rebuild operation from where the rebuild operation was paused rather than having to restart the operation at the beginning. And new in SQL Server 2017, we’re also adding the Adaptive Query Processing features that automatically keep database queries running as efficiently as possible without requiring additional tuning from database administrators. In addition to all these features, innovations to SQL Server 2017 engine have led to a world record in the TPC-H 1TB data warehousing workload (non-clustered) benchmark. The benchmark was achieved with SQL Server 2017 on Red Hat Enterprise Linux.[2][3] Learn more about 2017 CTP 2.0 here.

Application compatibility with SQL Server on Linux

With a consistent programmable surface area between SQL Server on Linux and SQL Server on Windows, minimal changes are required to make existing applications support SQL Server on Linux. In some cases, the only application change required to support SQL Server 2017 on Linux is just the connection string. Watch this Channel 9 video to see a demo of application compatibility between SQL Server 2016 on Windows and SQL Server 2017 on Linux.

As you get started with SQL Server on Linux, continue to develop, test, and deploy your application with SQL Server on Linux using all the existing tools you are familiar with. Client drivers such as ODBC and JDBC, along with all existing SQL Server tools such as SSMS, will continue to work with SQL Server on Linux. In addition to existing tools, you can leverage SQL Server running in containers to simplify DevOps scenarios.

Apply to Microsoft Data Accelerator to leverage the latest innovations of SQL Server

Microsoft Data Accelerator provides access to Microsoft’s highly reliable, automated SQL Server upgrade service, now supporting upgrades to SQL Server 2016 as well as SQL Server 2017 CTP adoption path. Backed by the rich experience of upgrading thousands of applications, Data Accelerator enables you to modernize your applications and provide a broader range of platform choices to your customers.

Click here to learn more and qualify for Data Accelerator.


[1] National Institute of Standards and Technology Comprehensive Vulnerability Database, update 2016

[2] TPC-H – Top Ten Performance Results – Non-Clustered, 2017, http://ift.tt/1TpRbbq

[3] TPC-H Result Highlights HPE Proliant DL380 Gen9, 2017, http://ift.tt/2qJJyto



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quarta-feira, 10 de maio de 2017

[From Technet] Serving AI with Data: A Summary of Build 2017 Data Innovations

This post was authored by Joseph Sirosh, Corporate Vice President, Microsoft Data Group

This week at the annual Microsoft Build conference, we are discussing how, more than ever, organizations are relying on developers to create breakthrough experiences. With big data, cloud and AI converging, innovation & disruption is accelerating to a pace never seen before. Data is the key strategic asset at the heart of this convergence. When combined with the limitless computing power of the cloud and new capabilities like Machine Learning and AI, it enables developers to build the next generation of intelligent applications. As a developer, you are looking for faster, easier ways to embrace these converging technologies and transform your app experiences.

Today at Build, we made several product announcements, adding to the recent momentum announced last month at Microsoft Data Amp, that will help empower every organization on the planet with data-driven intelligence. Across these innovations, we are pursuing three key themes:

  1. Infusing AI within our data platform
  2. Turnkey global distribution to push intelligence wherever your users are
  3. Choice of database platforms and tools for developers

Infusing AI within our data platform

Joseph_AI1A thread of innovation you will see in our products is the deep integration of AI with data. In the past, a common application pattern was to create machine learning models outside the database in the application layer or in specialty statistical tools, and deploy these models in custom built production systems. This results in a lot of developer heavy lifting, and the development and deployment lifecycle can take months. Our approach dramatically simplifies the deployment of AI by bringing intelligence into existing well-engineered data platforms through a new extensibility model for databases.

SQL Server 2017

We started this journey by introducing R support within the SQL Server 2016 release and we are deepening this commitment with the upcoming release of SQL Server 2017. In this release, we have introduced support for a rich library of machine learning functions and introduced Python support to give you more choices across popular languages. SQL Server can also leverage GPU accelerated computing through the Python/R interface to power even the most intensive deep learning jobs on images, text and other unstructured data. Developers can implement GPU accelerated analytics and very sophisticated AI directly in the database server as stored procedures and gain orders of magnitude higher throughput.

Additionally, as data becomes more complex and the relationships across data are many-to-many, developers are looking for easier ways to ingest and manage this data. With SQL Server 2017, we have introduced Graph support to deliver the best of both relational and graph databases in a single product, including the ability to query across all data using a single platform.

We have made it easy for you to try SQL Server with R, Python, and Graph support today whether you are working with C#, Java, Node, PHP, or Ruby.

Azure SQL Database

We’re continuing to simultaneously ship SQL Server 2017 enhancements to Azure SQL Database, so you get consistent programming surface area across on-premises and cloud. Today, I am excited to announce the support for Graph is also coming to Azure SQL Database so you can also get the best of both relational and graph in a single proven service on Azure.

SQL Database is built for developer productivity with most database management tasks built-in. We have also built AI directly into the service itself, making it an intelligent database service. The service runs millions of customer databases, learns, and then adapts to offer customized experiences for each database. With Database Advisor, you can choose to let the service learn your unique patterns and make performance and tuning recommendations or automatically take action on your behalf. Today, I am also excited to announce general availability of Threat Detection, which uses machine learning around the clock to learn, profile and detect anomalous activity over your unique database and sends alerts in minutes so you can take immediate action versus what historically can take an organization days, months, or years to discover.

Also, we are making it even easier for you to move more of your existing SQL Server apps as-is to Azure SQL Database. Today we announced the private preview for a new deployment option within the service, Managed Instance—you get all the managed benefits of SQL Database and now at the instance level which offers support for SQL Agent, three-part names, DBMail, CDC and other instance-level capabilities.

To streamline this migration effort, we also introduced a preview for Azure Database Migration Service that will dramatically accelerate the migration of on-premises third-party and SQL Server databases into Azure SQL Database.

Eric Fleischman, Vice President & Chief Architect from DocuSign notes, “Our transaction volume doubles every year. We wanted the best of what we do in our datacenter…with the best of what Azure could bring to it. For us, we found that Azure SQL Database was the best way to do it. We deploy our SQL Server schema elements into a Managed Instance, and we point the application via connection string change directly over to the Managed Instance. We basically picked up our existing build infrastructure and we’re able to deploy to Azure within a few seconds. It allows us to scale the business very quickly with minimal effort.”

Learn more about our investments in Azure SQL Database in this deeper blog and sign up for an invitation to these previews today.

Turnkey global distribution to push intelligence wherever your users are

With the intersection of mobile apps, internet of things, cloud and AI, users and data can come from anywhere around the globe. To deliver transformative intelligent apps that support the global nature of modern applications, and the volume, velocity, variety of data, you need more than a relational database, and more than a simple NoSQL database. You need a flexible database that can ingest massive volumes of data and data types, and navigate the challenges of space and time to ensure millisecond performance to any user anywhere on earth. And you want this with simplicity and support for the languages and technologies you know.

Joseph_AI2I’m also excited to share that today, Microsoft announced Azure Cosmos DB, the industry’s first globally-distributed, multi-model database service. Azure Cosmos DB was built from the ground up with global distribution and horizontal scale at its core – it offers turn-key global distribution across any number of Azure regions by transparently scaling and distributing your data wherever your users are, worldwide. Azure Cosmos DB leverages the work of Turing award winner Dr. Leslie Lamport, PAXOS algorithm for distributed systems and TLA+ a high-level modeling language. Check out a new interview with Dr. Lamport on Azure Cosmos DB.

Azure Cosmos DB started as “Project Florence” in 2010 to address developer the pain-points faced by large scale applications inside Microsoft. Observing that the challenges of building globally distributed apps are not a problem unique to Microsoft, in 2015 we made the first generation of this technology available to Azure developers in the form of Azure DocumentDB. Since that time, we’ve added new features and introduced significant new capabilities.  Azure Cosmos DB is the result.  It is the next big leap in globally distributed, at scale, cloud databases.

Now, with more innovation and value, Azure Cosmos DB delivers a schema-agnostic database service with turnkey global distribution, support for multiple models across popular NoSQL technologies, elastic scale of throughput and storage, five well-defined consistency models, and financially-backed SLAs across uptime, throughput, consistency, and millisecond latency.

“Domino’s Pizza chose Azure to rebuild their ordering system and a key component in this design is Azure Cosmos DB—delivering the capability to regionally distribute data, to scale easily, and support peak periods which are critical to the business. Their online solution is deployed across multiple regions around the world—even with the global scaling they can also rely on Azure Cosmos DB millisecond load latency and fail over to a completely different country if required.”

Learn more about Azure Cosmos DB in this deeper blog.

Choice of database platforms and tools for developers

We understand that SQL Server isn’t the only database technology developers want to build with. Therefore, I’m excited to share that today we also announced two new relational database services; Azure Database for MySQL and Azure Database for PostgreSQL to join our database services offerings.

Joseph_AI3These new services are built on the proven database services platform, which has been powering Azure SQL Database, and offers high availability, data protection and recovery, and scale with minimal downtime—all built-in at no extra cost or configurations. Starting today, you can now develop on MySQL and PostgreSQL database services on Azure. Microsoft is managing the MySQL and PostgreSQL technology you know, love and expect but backed by an enterprise-grade, highly available and fault tolerant cloud services platform that allows you to focus on developing great apps versus management and maintenance.

“Each month, up to 2 million people turn to the GeekWire website for the latest news on tech innovation. Now, GeekWire is making news itself by migrating its popular WordPress site to the Microsoft Azure platform. Kevin Lisota, Web Developer at GeekWire notes, “The biggest benefit of Azure Database for MySQL will be to have Microsoft manage and back up that resource for us so that we can focus on other aspects of the site. Plus, we will be able to scale up temporarily as traffic surges and then bring it back down when it is not needed. That’s a big deal for us.”

Learn more about these new services and try them today.

Azure Data Lake Tools for Visual Studio Code (VSCode)

Azure Data Lake includes all the capabilities required to make it easy for developers, data scientists, and analysts to store data of any size, shape, and speed, and do all types of processing and analytics across platforms and languages. Additionally, Azure Data Lake includes a set of cognitive capabilities built-in, making it seamless to execute AI over petabytes of data. On our journey to make it easier for every developer to become an AI and data science developer, we are investing in bringing more great tooling for data into the tools you know and love.

Today, I’m excited to announce General Availability of Azure Data Lake Tools for Visual Studio Code (VSCode) which gives developers a light but powerful code editor for big data analytics. The new Azure Data Lake Tools for VSCode supports U-SQL language authoring, scripting, and extensibility with C# to process different types of data and efficiently scale any size of data. The new tooling integrates with Azure Data Lake Analytics for U-SQL job submissions with job output to Azure Data Lake Analytics or Azure Blob Storage. In addition, U-SQL local run service has been added to allow developers to locally validate scripts and test data. Learn more and download these tools today.

Getting started

It has never been easier to get started with the latest advances in the intelligent data platform. We invite you to watch our Microsoft Build 2017 online event for streaming and recorded coverage of these innovations, including SQL Server 2017 on Windows, Linux and Docker; scalable data transformation and intelligence from Azure Cosmos DB, Azure Data Lake Store and Azure Data Lake Analytics; the Azure SQL Database approach to proactive Threat Detection and intelligent database tuning; new Azure Database for MySQL and Azure Database for PostgreSQL. I look forward to a great week at Build and your participation in this exciting journey of infusing AI into every software application.



from SQL Server Blog http://ift.tt/2q3L9ZW

quarta-feira, 3 de maio de 2017

[From Technet] Announcing Power BI Report Server

This post was authored by the Power BI Team

Today Microsoft announced Power BI Premium — a capacity-based licensing model that increases flexibility for how users access, share and distribute content in Power BI. The new offering also introduces the ability to manage Power BI Reports on-premises with the included Power BI Report Server.

Power BI Report Server will be generally available late in the second quarter of 2017.

Read the Power BI Report Server announcement and learn more about Power BI Report Server on the Power BI website.

pbi-sales-report

 

 



from SQL Server Blog http://ift.tt/2pYQOS8