mountainss Cloud and Datacenter Management Blog

Microsoft Hybrid Cloud blogsite about Management


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#Microsoft SQL Server 2019 Preview Overview #SQL #SQL2019 #Linux #Containers #MSIgnite

Microsoft SQL Server 2019 Preview

What’s New in Microsoft SQL Server 2019 Preview

• Big Data Clusters
o Deploy a Big Data cluster with SQL and Spark Linux containers on Kubernetes
o Access your big data from HDFS
o Run Advanced analytics and machine learning with Spark
o Use Spark streaming to data to SQL data pools
o Use Azure Data Studio to run Query books that provide a notebook experience

• Database engine
o UTF-8 support
o Resumable online index create allows index create to resume after interruption
o Clustered columnstore online index build and rebuild
o Always Encrypted with secure enclaves
o Intelligent query processing
o Java language programmability extension
o SQL Graph features
o Database scoped configuration setting for online and resumable DDL operations
o Always On Availability Groups – secondary replica connection redirection
o Data discovery and classification – natively built into SQL Server
o Expanded support for persistent memory devices
o Support for columnstore statistics in DBCC CLONEDATABASE
o New options added to sp_estimate_data_compression_savings
o SQL Server Machine Learning Services failover clusters
o Lightweight query profiling infrastructure enabled by default
o New Polybase connectors
o New sys.dm_db_page_info system function returns page information

• SQL Server on Linux
o Replication support
o Support for the Microsoft Distributed Transaction Coordinator (MSDTC)
o Always On Availability Group on Docker containers with Kubernetes
o OpenLDAP support for third-party AD providers
o Machine Learning on Linux
o New container registry
o New RHEL-based container images
o Memory pressure notification

• Master Data Services
o Silverlight controls replaced

• Security
o Certificate management in SQL Server Configuration Manager

• Tools
o SQL Server Management Studio (SSMS) 18.0 (preview)
o Azure Data Studio

Introducing Microsoft SQL Server 2019 Big Data Clusters

SQL Server 2019 big data clusters make it easier for big data sets to be joined to the dimensional data typically stored in the enterprise relational database, enabling people and apps that use SQL Server to query big data more easily. The value of the big data greatly increases when it is not just in the hands of the data scientists and big data engineers but is also included in reports, dashboards, and applications. At the same time, the data scientists can continue to use big data ecosystem tools while also utilizing easy, real-time access to the high-value data in SQL Server because it is all part of one integrated, complete system.

Read the complete Awesome blogpost from Travis Wright about SQL Server 2019 Big Data Cluster here

Starting in SQL Server 2017 with support for Linux and containers, Microsoft has been on a journey of platform and operating system choice. With SQL Server 2019 preview, we are making it easier to adopt SQL Server in containers by enabling new HA scenarios and adding supported Red Hat Enterprise Linux container images. Today we are happy to announce the availability of SQL Server 2019 preview Linux-based container images on Microsoft Container Registry, Red Hat-Certified Container Images, and the SQL Server operator for Kubernetes, which makes it easy to deploy an Availability Group.

SQL Server 2019 preview containers now available

Microsoft Azure Data Studio

Azure Data Studio is a new cross-platform desktop environment for data professionals using the family of on-premises and cloud data platforms on Windows, MacOS, and Linux. Previously released under the preview name SQL Operations Studio, Azure Data Studio offers a modern editor experience with lightning fast IntelliSense, code snippets, source control integration, and an integrated terminal. It is engineered with the data platform user in mind, with built-in charting of query resultsets and customizable dashboards.

Read the Complete Blogpost About Microsoft Azure Data Studio for SQL Server here

SQL Server 2019: Celebrating 25 years of SQL Server Database Engine and the path forward

Awesome work Microsoft SQL Team and Congrats on your 25th Anniversary !

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#Microsoft Azure #DataScience VM (DSVM) on Windows Server 2016 in the #Cloud #Azure #DSVM

The Microsoft Data Science Virtual Machine (DSVM) is a powerful data science development environment that enables you to perform various data exploration and modeling tasks. The environment comes already built and bundled with several popular data analytics tools that make it easy to get started quickly with your analysis for On-premises, Cloud or hybrid deployments. The DSVM works closely with many Azure services and is able to read and process data that is already stored on Azure, in Azure SQL Data Warehouse, Azure Data Lake, Azure Storage, or in Azure Cosmos DB. It can also leverage other analytics tools such as Azure Machine Learning and Azure Data Factory.

You can Choose which OS you Like.

The ‘Data Science Virtual Machine (DSVM)’ is a ‘Windows Server 2016 with Containers’ VM & includes popular tools for data exploration, analysis, modeling & development.

Highlights:

Tools for ML model operationalization as web services in the cloud, using Azure ML or Microsoft R Server.

Creating the Azure DSVM with Windows Server 2016

I use this Azure DSVM for testing and I will enable Auto-Shutdown Scheduler
(to save money)

The following software is by default installed in the Azure DSVM :

My Azure Data Science Virtual Machine in the Cloud 😉

From here you can configure your Data Management Gateway.

Microsoft Data Management Gateway connects on-premises data sources to cloud services for consumption. With Microsoft cloud services, such as Power BI for Office 365 and Azure Data Factory you get benefits including fast deployment, low maintenance cost, and flexible billing model while keeping your enterprise data on-premises. With Data Management Gateway, you can connect on-premises data to cloud services in a secure and managed way, to respond more quickly to changing business needs with a flexible, hybrid cloud platform. You can benefit from Microsoft cloud services while you keep your business running with the on-premises data.

More information about the Data Management Gateway :

Microsoft Data Management Gateway Overview

Running Azure Data Science VM

Portal Overview of the Azure DSVM

From the Microsoft Azure Portal you can add Resource Extensions to your Azure Data Science VM.

From here you can add the following Extensions :

Here you can find more Technical information about Microsoft Azure Data Science Virtual Machine :

Introduction to the cloud-based Data Science Virtual Machine for Linux and Windows

Azure Data Science Virtual Machine on GitHub

Microsoft Azure Data Science Virtual Machine forum

Follow the #DSVM on Twitter

Microsoft Professional Program Certificate in Data Science


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Microsoft Azure Machine Learning Cloud Platform with Free Ebook #Azure #MachineLearning

Azure Machine Learn

Blog Logo Azure MachineThe Microsoft Azure Machine Learning cloud platform provides simplified yet powerful data management, transformation and machine learning tools. R language scripts integrate with built in Azure ML modules to extend the platform. Additionally, models running in Azure ML can be published as web services.

You will be provided information on how to perform data science tasks including, data management, data transformation, and machine learning in the Azure ML cloud environment. You will learn:

  • Data management with Azure ML.
  • Data transformation with Azure ML and R.
  • Data I/O between Azure ML and the R Scripts.
  • R graphics with Azure ML.
  • Building and evaluating machine learning models with Azure ML and R.
  • Publishing Azure ML models as a web service.


Free tier Azure ML accounts are now available with a Microsoft ID at

https://studio.azureml.net/Home/Free.

Azure Machine Learning Ebook

Here you can sign up for a Free Ebook :

 Azure-Machine-Learning-351x185
Microsoft Azure Machine Learning BlogSite

Microsoft Azure Machine Learning on TechNet Wiki