29

Nov

On-premises data gateway November update is now available
On-premises data gateway November update is now available

https://powerbi.microsoft.com/en-us/blog/on-premises-data-gateway-november-update-is-now-available/Source: https://powerbi.microsoft.com/en-us/blog/on-premises-data-gateway-november-update-is-now-available/           We are excited to announce that we have just released the November update for the On-premises data gateway.

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29

Nov

Upgrade your SQL Server to scale before adding new hardware
Upgrade your SQL Server to scale before adding new hardware

https://blogs.msdn.microsoft.com/sql_server_team/upgrade-your-sql-server-to-scale-before-adding-new-hardware/Source: https://blogs.msdn.microsoft.com/sql_server_team/upgrade-your-sql-server-to-scale-before-adding-new-hardware/   In SQL Server Tiger team, we closely partner with our customers and partners to ensure they can successfully run and scale their Tier 1 mission critical workloads and applications on SQL Server. Based on our experience and READ MORE

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28

Nov

Developing and Operationalizing H2O.ai Models with Azure
Developing and Operationalizing H2O.ai Models with Azure

This post is authored by Daisy Deng, Software Engineer, and Abhinav Mithal, Senior Engineering Manager, at Microsoft.

The focus on machine learning and artificial intelligence has soared over the past few years, even as fast, scalable and reliable ML and AI solutions are increasingly viewed as being vital to business success. H2O.ai has lately been gaining fame in the AI world for its fast in-memory ML algorithms and for easy consumption in production. H2O.ai is designed to provide a fast, scalable, and open source ML platform and it recently added support for deep learning as well. There are many ways to run H2O.ai on Azure. This post provides an overview of how to efficiently develop and operationalize H2O.ai ML models on Azure.

H2O.ai can be deployed in many ways including on a single node, on a multi-node cluster, in a Hadoop cluster and an Apache Spark cluster. H2o.ai is written in Java, so it naturally supports Java APIs. Since the standard Scala backend is a Java VM, H2O.ai also supports the Scala API. It also has rich interfaces for Python and R. The h2o R and h2o Python packages respectively help R and Python users access H2O.ai algorithms and functionality.

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22

Nov

How we made backups faster with SQL Server 2017
How we made backups faster with SQL Server 2017

https://blogs.msdn.microsoft.com/sql_server_team/how-we-made-backups-faster-with-sql-server-2017/Source: https://blogs.msdn.microsoft.com/sql_server_team/how-we-made-backups-faster-with-sql-server-2017/   In my previous blog post on enhancements in SQL Server 2017, we briefly introduced improved backup performance for smaller databases in SQL Server 2017 on high end servers. In the recent PASS Summit 2017, Pedro and I READ MORE

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21

Nov

Perfect statistics histogram in just few steps
Perfect statistics histogram in just few steps

https://blogs.msdn.microsoft.com/sql_server_team/perfect-statistics-histogram-in-just-few-steps/Source: https://blogs.msdn.microsoft.com/sql_server_team/perfect-statistics-histogram-in-just-few-steps/   A similar question about statistics came to the team twice this week: why does updating with fullscan result in fewer histogram steps than when doing a sampled scan? The answer is: the number of histogram steps can READ MORE

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17

Nov

Power BI expands access to intelligence for external guest users

https://powerbi.microsoft.com/en-us/blog/power-bi-expands-access-to-intelligence-for-external-guest-users/Source: https://powerbi.microsoft.com/en-us/blog/power-bi-expands-access-to-intelligence-for-external-guest-users/           Power BI was first introduced with a simple commitment: Empower people and organizations with access to critical intelligence. The recent general availability of Power BI Premium in June broadened…

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16

Nov

SQL Server 2017: A proven leader in database performance
SQL Server 2017: A proven leader in database performance

This post was authored by Bob Ward, Principal Architect, and Jamie Reding, Senior Program Manager and Performance Architect, Microsoft Database Systems Group.

SQL Server continues to be a proven leader in database performance for both analytic and OLTP workloads. SQL Server 2017 is fast, built-in with capabilities and features such as Columnstore indexes to accelerate analytic performance and Automatic Tuning and Adaptive Query Processing to keep your database application at peak speed.

Recently, Hewlett Packard Enterprise (HPE) announced a new world record TPC-H 10TB benchmark result¹ using SQL Server 2017 and Windows Server 2016 on their new DL580 Gen10 Server. This new amazing result at 1,479,748 Composite Query-per-Hour (QphH) was achieved at price/performance of .95 USD per QphH continuing to show SQL Server’s leadership in price and performance.

HPE also announced the first TPC-H 3TB result² on a 2-socket system using SQL Server 2017 and Windows Server 2016 with their DL380 Gen Server. They achieved a stellar 1,014,374 QphH on only 2-sockets. These results continue to show how powerful SQL Server can be to handle your analytic query workloads including data warehouses.

SQL Server also is a proven leader for OLTP workloads. Lenovo recently announced a new world-record TPC-E benchmark

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16

Nov

SQL Server 2017: A proven leader in database performance
SQL Server 2017: A proven leader in database performance

This post was authored by Bob Ward, Principal Architect, and Jamie Reding, Senior Program Manager and Performance Architect, Microsoft Database Systems Group.

SQL Server continues to be a proven leader in database performance for both analytic and OLTP workloads. SQL Server 2017 is fast, built-in with capabilities and features such as Columnstore indexes to accelerate analytic performance and Automatic Tuning and Adaptive Query Processing to keep your database application at peak speed.

Recently, Hewlett Packard Enterprise (HPE) announced a new world record TPC-H 10TB benchmark result¹ using SQL Server 2017 and Windows Server 2016 on their new DL580 Gen10 Server. This new amazing result at 1,479,748 Composite Query-per-Hour (QphH) was achieved at price/performance of .95 USD per QphH continuing to show SQL Server’s leadership in price and performance.

HPE also announced the first TPC-H 3TB result² on a 2-socket system using SQL Server 2017 and Windows Server 2016 with their DL380 Gen Server. They achieved a stellar 1,014,374 QphH on only 2-sockets. These results continue to show how powerful SQL Server can be to handle your analytic query workloads including data warehouses.

SQL Server also is a proven leader for OLTP workloads. Lenovo recently announced a new world-record TPC-E benchmark

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16

Nov

SQL Server 2017: A proven leader in database performance
SQL Server 2017: A proven leader in database performance

This post was authored by Bob Ward, Principal Architect, and Jamie Reding, Senior Program Manager and Performance Architect, Microsoft Database Systems Group.

SQL Server continues to be a proven leader in database performance for both analytic and OLTP workloads. SQL Server 2017 is fast, built-in with capabilities and features such as Columnstore indexes to accelerate analytic performance and Automatic Tuning and Adaptive Query Processing to keep your database application at peak speed.

Recently, Hewlett Packard Enterprise (HPE) announced a new world record TPC-H 10TB benchmark result¹ using SQL Server 2017 and Windows Server 2016 on their new DL580 Gen10 Server. This new amazing result at 1,479,748 Composite Query-per-Hour (QphH) was achieved at price/performance of .95 USD per QphH continuing to show SQL Server’s leadership in price and performance.

HPE also announced the first TPC-H 3TB result² on a 2-socket system using SQL Server 2017 and Windows Server 2016 with their DL380 Gen Server. They achieved a stellar 1,014,374 QphH on only 2-sockets. These results continue to show how powerful SQL Server can be to handle your analytic query workloads including data warehouses.

SQL Server also is a proven leader for OLTP workloads. Lenovo recently announced a new world-record TPC-E benchmark

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16

Nov

On-Demand Webinar – AI Development Using Data Science VMs (DSVM), Deep Learning VMs (DLVM) & Azure Batch AI

This post is authored by Barnam Bora, Program Manager in the Cloud AI group at Microsoft.

Microsoft’s Data Science Virtual Machines (DSVM) and Deep Learning Virtual Machines (DLVM) are a family of popular VM images in Windows Server and Linux flavors that are published on the Azure Marketplace. They have a curated but broad set of pre-configured machine learning and data science tools including pre-loaded samples. DSVM and DLVM are configured and tested to work seamlessly with a plethora of services available on the Microsoft Azure cloud, and they enable a wide array of data analytics scenarios that are being used by many organizations across the globe.

We recently hosted a webinar covering the workflow of building ML and AI -powered solutions in Azure using DSVM, DLVM and related services such as Azure Batch AI and Azure Machine Learning Model Management. The webinar video is available from the link below (requires registration with Microsoft) and more information about the webinar are in the sections that follow.


WATCH: AI development using DSVM/DLVM

Scenarios Covered in the Webinar
Single GPU Node AI Model Training

DSVM and DLVM are great tools to develop, test and deploy AI models and solutions. Data scientists

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