In this episode of Data Exposed with Tolga Tekin, learn about the ability to deploy Azure Arc Data services via Kubernetes to any hardware platform.

Time stamps:

  • [00:55] What is Azure Arc
  • [02:20] Azure data services anywhere at a glance
  • [03:28] On-premises vs Azure
  • [04:02] Azure Arc-enabled data services architecture
  • [06:07] Management capabilities comparison by deployment model
  • [08:22] Customer scenarios with Azure Arc
  • [09:53] Example – managing different infrastructures at different locations
  • [11:51] Getting started
Resources:

Here’s a great tutorial on how to use the Azure Machine Learning Designer interface to create machine learning models without code.

You don’t need to write code to build your model, though there’s the option to bring in custom R or Python where necessary. It’s a replacement for the original ML Studio tool, adding deeper integration into Azure’s machine learning SDKs and with support for more than CPU-based models, offering GPU-powered machine learning and automated model training and tuning.

In this episode with Drew Skwiers-Koballa, you will be introduced to a new experience for database development with the SQL Database Projects extension for Azure Data Studio. 

Whether you are familiar with SQL Server Data Tools (SSDT) or new to SQL projects, you can start editing and building SQL projects in Azure Data Studio on Windows, macOS, and Linux.

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Dipti Pai joins Scott Hanselman to show how to run containerized and VM workloads to get quick, actionable insights at the edge—where data is created—using purpose-built hardware-as-a-service with Azure Stack Edge.

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Advancing Analytics explainshow to parameterize Spark in Synapse Analytics, meaning you can plug notebooks to our orchestration pipelines and dynamically pass parameters to change how it works each time.

But how does it actually work?

Simon’s digging in to give us a quick peek at the new functionality.

For more details on the new parameters, take a peek here: https://docs.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-development-using-notebooks#orchestrate-notebook

This looks interesting.

Udacity announces the Machine Learning Engineer for Microsoft Azure Nanodegree Program, built in collaboration with Microsoft, offers you the chance to build the practitioner-level skills that companies across industries need. In the program, you’ll strengthen your machine learning skills by training, validating, and evaluating models using Azure Machine Learning, and complete a series of three real-world projects to add to your portfolio.

Azure Synapse workspaces can host a Spark cluster.

In addition to providing the execution environment for certain Synapse features such as Notebooks, you can also write custom code that runs as a job inside Synapse hosted Spark cluster.

This video walks through the process of running a C# custom Spark job in Azure Synapse. It shows how to create the Synapse workspace in the Azure portal, how to add a Spark pool, and how to configure a suitable storage account. It also shows how to write the custom job in C#, how to upload the built output to Azure, and then how to configure Azure Synapse to execute the .NET application as a custom job.

Topics/Time index:

  • Create a new Azure Synapse Analytics workspace (0:17)
  • Configuring security on the storage account (1:29)
  • Exploring the workspace (2:42)
  • Creating an Apache Spark pool (3:01)
  • Creating the C# application (4:05)
  • Adding a namespace directive to use Spark (SQL 4:48)
  • Creating the Spark session (5:01)
  • How the job will work (5:22)
  • Defining the work with Spark SQL (6:42)
  • Building the .NET application to upload to Azure Synapse (9:48)
  • Uploading our application to Azyure Synapse (11:45)
  • Using the ZIPed .NET application in a custom Spark job definition (12:39)
  • Testing the custom job (13:36)
  • Monitoring the job (13:56)
  • Inspecting the results (14:25)

If you’re looking to get started with Azure Synapse, Microsoft recently released an Azure Synapse POC template on GitHub that configures a full environment in just a few easy clicks.

Time index:

  • 00:00 – Intro
  • 01:12 – Overview of Azure Synapse
  • 03:13 – Template Overview
  • 06:27 – Deploy the POC Environment
  • 09:45 – Review Deployed Resources
  • 12:03 – Review Logic Apps
  • 15:03 – Synapse Workspace Overview
  • 16:41 – Post-Deployment Steps

To provide customers with an easier network configuration, all newly created virtual clusters will be enabled for access over global virtual network peering connections, now in general availability.

This enables customers to pair managed instances in failover group configuration, in an easy and performant way, by simply connecting virtual networks in different regions. By utilizing global virtual network peering for your managed instances, you will save time through easy network configuration and offload your gateways from database replication traffic.

Review this new feature and more in this episode with Srdan Bozovic.

Time Index:

  • [00:42] Why Global VNet Peering Support is Important
  • [01:39] Auto-failover group connectivity architecture
  • [03:52] Take advantage of Global VNet Peering Support

Resources: