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Microsoft SystemCenter blogsite about virtualization on-premises and Cloud


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NEW via #MSFTConnect 2017 Microsoft #Azure Databrick

Today at Microsoft Connect(); we introduced Azure Databricks, an exciting new service in preview that brings together the best of the Apache Spark analytics platform and Azure cloud. As a close partnership between Databricks and Microsoft, Azure Databricks brings unique benefits not present in other cloud platforms. This blog post introduces the technology and new capabilities available for data scientists, data engineers, and business decision-makers using the power of Databricks on Azure.

Azure Databricks Preview

Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Designed with the founders of Apache Spark, Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts.

Read more on Microsoft Docs what Microsoft Azure Databrick is

Quickstart: Get started with Azure Databricks using the Azure portal :

This quickstart shows how to create an Azure Databricks workspace and an Apache Spark cluster within that workspace. Finally, you learn how to run a Spark job on the Databricks cluster.

Creating Clusters

In Databricks, you can create two different types of resources:
Standard Clusters: Databricks’ standard clusters have lot of configuration options to customize and fine tune your Spark jobs. You can learn more about standard clusters below.
Serverless Pools (BETA): With serverless pools, Databricks’ auto-manages all the resources and you just need to provide the range of instances required for the pool. Serverless pools support only Python and SQL. Serverless pools also auto-configures the resources with right Spark configuration. Visit Serverless Pools to know more about them.

Read more on the Microsoft Azure Blog here:  A Technical Overview of Azure Databricks after Microsoft Connect() 2017.

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Watch all those Awesome Microsoft #MSIgnite 2017 video sessions #Azure #AzureStack #MSOMS

Empower IT and developer productivity with Microsoft Azure with @scottgu

Microsoft Azure virtual machine infrastructure innovation and automation

Microsoft Azure Stack Development Kit and why it matters

Manage hybrid cloud and transform your workplace with PowerShell and Azure Automation

See here all the Microsoft Ignite 2017 video sessions

Thank you Microsoft and MVP’s for those Awesome sessions at Ignite 2017


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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