HMI review: building more agile factories with Azure IoT and Windows IoT

by Contributed | Apr 16, 2021 | Technology

This article is contributed. See the original author and article here.

Today wraps up Hannover Messe 2021, one of the world’s leading industrial trade shows. This annual event attracts more than 200,000 visitors and highlights the latest in technology across six topic areas: Automation, Motion & Drive, Digital Ecosystems, Logistics, Energy Solutions, Engineered Parts & Solutions, and Future Hub. With over 6,500 exhibitors, and 1,400 events spread across five days, the show is an incredible opportunity to showcase our technology, learn from those in the industry and hear from our customers.

Due the on-going global pandemic, this year the trade show was hosted virtually and featured as “HM Digital Edition”.  As much as the Microsoft team missed joining with our customers and partners in Germany to participate, the team was excited to still host virtual sessions showcasing several innovative solutions.

The IoT team walked customers through how to build more agile factories through the use of Industrial IoT, Cloud, AI and Mixed Reality solutions. The current challenge for many organizations is to create safer, more secure and agile “factories of the future.” To provide insight into this transformation, we had our customers and partners talk about how they are scaling their solutions with the Microsoft cloud and edge platforms and describe their best practices as they prepare for the “new normal” across hundreds of factories worldwide.  

We also had a joint session with McKinsey where we were able to share the learnings from work on the World Economic Forum’s Global Lighthouse Network and the accelerating adoption of digital technologies in daily manufacturing and supply chain operations.  Digital transformation as explored in these sessions is powered by the intelligent edge and intelligent cloud. These technologies can be quickly and easily integrated into your Windows IoT devices with our upcoming General Availability of Azure IoT Edge for Linux on Windows.

Azure IoT Edge for Linux on Windows, also often referred to as ‘EFLOW’, allows organizations to deploy production Linux-based cloud-native workloads on Windows IoT and retain existing Windows management assets at the edge. This opens a world of capabilities for commercial IoT as well as AI/ML with the availability of pre-built modules from the Azure Marketplace such as Live Video Analytics, SQL Edge, and OPC Publisher as a few examples. Customers can benefit from the power of Windows IoT for applications that require an interactive UX and high-performance hardware interaction without having to choose between Windows or Linux and leverage the best of Windows and Linux together. 

A lot of customers want to deploy these Linux workloads but do not want to introduce a new operating system in their environment that they are unable to manage with existing infrastructure or expertise.  To learn more about EFLOW, check out the following blog, video and documentation.

Security baseline for Microsoft Edge, version 90

by Contributed | Apr 16, 2021 | Technology

This article is contributed. See the original author and article here.

We are pleased to announce the enterprise-ready release of the security baseline for Microsoft Edge, version 90!

We have reviewed the new settings in Microsoft Edge version 90 and determined that there are no additional security settings that require enforcement. The settings from the Microsoft Edge version 88 package continues to be our recommended baseline. That baseline package can be downloaded from the Microsoft Security Compliance Toolkit.

Microsoft Edge version 90 introduced 9 new computer settings, 9 new user settings. We have attached a spreadsheet listing the new settings to make it easier for you to find them.

As a friendly reminder, all available settings for Microsoft Edge are documented here, and all available settings for Microsoft Edge Update are documented here.

Please continue to give us feedback through the Security Baselines Discussion site or this post.

Query serverless SQL pool from an Apache Spark Scala notebook

by Contributed | Apr 16, 2021 | Technology

This article is contributed. See the original author and article here.

Azure Synapse Analytics provides multiple query runtimes that you can use to query in-database or external data. You have the choice to use T-SQL queries using a serverless Synapse SQL pool or notebooks in Apache Spark for Synapse analytics to analyze your data.

You can also connect these runtimes and run the queries from Spark notebooks on a dedicated SQL pool.

In this post, you will see how to create Scala code in a Spark notebook that executes a T-SQL query on a serverless SQL pool.

Configuring connection to the serverless SQL pool endpoint

Azure Synapse Analytics enables you to run your queries on an external SQL query engine (Azure SQL, SQL Server, a dedicated SQL pool in Azure Synapse) using standard JDBC connection. With the Apache Spark runtime in Azure Synapse, you are also getting pre-installed driver that enables you to send a query to any T-SQL endpoint. This means that you can use this driver to run a query on a serverless SQL pool.

First, you need to initialize the connection with the following steps:

• Define connection string to your remote T-SQL endpoint (serverless SQL pool in this case),


• Specify properties (for example username/password)


• Set the driver for connection.

The following Scala code contains the code that initializes connection to the serverless SQL pool endpoint:

// Define connection:
Class.forName("com.microsoft.sqlserver.jdbc.SQLServerDriver")

val hostname = "<WORKSPACE NAME>-ondemand.sql.azuresynapse.net"
val port = 1433
val database = "master" // If needed, change the database 
val jdbcUrl = s"jdbc:sqlserver://${hostname}:${port};database=${database}"

// Define connection properties:
import java.util.Properties

val props = new Properties()
props.put("user", "<sql login name>")
props.put("password", "<sql login password>")

// Assign driver to connection:
val driverClass = "com.microsoft.sqlserver.jdbc.SQLServerDriver"
props.setProperty("Driver", driverClass)

This code should be placed in some cell in the notebook and you will be able to use this connection to query external T-SQL endpoints. In the following sections you will see how to read data from some SQL table or view or run ad-hoc query using this connection.

Reading content of SQL table

The serverless SQL pool in Azure Synapse enables you to create views and external tables over data stored in your Azure Data Lake Storage account or Azure CosmosDB analytical store. With the connection that is initialized in the previous step, you can easily read the content of the view or external table.

In the following simplified example, the Scala code will read data from the system view that exists on the serverless SQL pool endpoint:

val objects = spark.read.jdbc(jdbcUrl, "sys.objects", props).
objects.show(10)

If you create view or external table, you can easily read data from that object instead of system view.

You can easily specify what columns should be returned and some conditions:

val objects = spark.read.jdbc(jdbcUrl, "sys.objects", props).
                            select("object_id", "name", "type").
                            where("type <> 'S'")
objects.show(10)

Executing remote ad-hoc query

You can easily define T-SQL query that should be executed on remote serverless SQL pool endpoint and retrieve results. The Scala sample that can be added to the initial code is shown in the following listing:

val tsqlQuery =
"""
select top 10 *
from openrowset(
    bulk 'https://pandemicdatalake.blob.core.windows.net/public/curated/covid-19/ecdc_cases/latest/ecdc_cases.parquet',
    format = 'parquet') as rows
"""

val cases = spark.read.jdbc(jdbcUrl, s"(${tsqlQuery}) res", props)
cases.show(10)

The text of T-SQL query is defined the variable tsqlQuery. Spark notebook will execute this T-SQL query on the remote serverless Synapse SQL pool using spark.read.jdbc() function.

The results of this query are loaded into local data frame and displayed in the output.

Conclusion

Azure Synapse Analytics enables you to easily integrate analytic runtimes and run a query from the Apache Spark runtime on the Synapse SQL pool. Although Apache Spark has built-in functionalities that enable you to access data on Azure Storage, there some additional Synapse SQL functionalities that you can leverage in Spark jobs:

• Accessing storage using SAS tokens or workspace managed identity. This way you can use serverless SQL pool to access Azure Data Lake storage protected with private endpoints or time limited keys.


• Using custom language processing text rules. Synapse SQL contains text comparison and sorting rules for most of the world language. If you need to use case or accent insensitive searches or filter text using Japanese, France, German, or any other custom language rules, Synapse SQL provides native support for text processing.

The samples described in this article might help you to reuse functionalities that are available in serverless Synapse SQL pool to load data from Azure Data Lake storage or Azure Cosmos DB analytical store directly in your Spark Data Frames. Once you load your data, Apache Spark will enable you to analyze data sets using advanced data transformation and machine learning functionalities that exist in Spark libraries.