Teams meetings aren’t only for remote learning. Educators can use Teams meetings in many ways. With learners and parents, educators can:
Microsoft Teams is a powerful tool for educators to stay connected to learners, families, and colleagues. Teams lets educators create, attend, and run meetings with many features that enhance engagement and collaboration.
Instant polling helps educators to get real-time feedback during a meeting. Educators can quickly choose from three binary option pairs—Yes or No, Thumbs Up Thumbs Down, Heart or Broken Heart—and send a poll question. Attendees can vote and see the animated results on their screen.
Microsoft Whiteboard is a visual collaboration canvas that allows educators and learners to create and share rich content. Educators can design interactive whiteboards in advance and open them in a Teams meeting. They can also embed videos from various sources to provide multiple means of engagement for different learners.
Together mode in Teams meetings uses AI to place all the participants on a shared background, creating a sense of being in the same room. Together mode can help reduce fatigue, improve attention, and foster social connection in online classes.
Using SQL Server Management Studio (SSMS) to connect to a data warehouse in Fabric can facilitate your workflow, especially if you’re already familiar with the tool.
SQL Server Management Studio provides a familiar interface for those who regularly work with SQL Server.
Follow these steps to connect to data warehouse in Fabric from SSMS:
In Microsoft Fabric, two types of authenticated users are supported through the SQL connection string:
The SQL query editor in Microsoft Fabric is a versatile tool that supports Transact-SQL (T-SQL), allowing you to create and run scripts to query your data warehouse. It also provides features such as IntelliSense and debugging to aid in the development process.
T-SQL allows users, analysts and developers to manipulate the data stored in a warehouse.
The SQL query editor isn’t just about querying; it’s about managing your data effectively. Whether you’re creating a new table, inserting rows into a table, granting objects permission or executing complex queries, the SQL query editor is designed to make these tasks seamless.
In most data analytics scenarios involving a data warehouse, the process typically revolves around aggregating numeric measures from fact tables based on attributes in dimension tables. Due to the structure of a star or snowflake schema, such aggregation queries depend on JOIN clauses to link fact tables with dimension tables. Also, they use aggregate functions and GROUP BY clauses to define the aggregation hierarchies.
The following SQL query aggregates sales amounts by year and quarter from the FactSales and DimDate tables in a hypothetical data warehouse:
SQLCopy
SELECT dates.CalendarYear,
dates.CalendarQuarter,
SUM(sales.SalesAmount) AS TotalSales
FROM dbo.FactSales AS sales
JOIN dbo.DimDate AS dates ON sales.OrderDateKey = dates.DateKey
GROUP BY dates.CalendarYear, dates.CalendarQuarter
ORDER BY dates.CalendarYear, dates.CalendarQuarter;
Once the dimension and fact tables in a data warehouse are populated with data, you can use T-SQL to query these tables and perform data analysis. The Transact-SQL (T-SQL) syntax used for querying tables in a warehouse in Fabric closely resembles the SQL syntax used in SQL Server or Azure SQL Database. This familiarity allows for an easy transition for those already used to working with these platforms.
Run the following command on your local machine:
BashCopy
az deployment group create \
--resource-group 240900-linux-postgres \
--template-file deploy/vm-postgres.bicep \
--parameters \
deployVm=false \
deployPostgres=false \
deployStorage=true
BashCopy
STORAGE_ACCOUNT_ID=$(az storage account list \
--resource-group 240900-linux-postgres \
--query '[0].id' \
-o tsv)
USER_ID=$(az ad signed-in-user show \
--query id \
-o tsv)
az role assignment create \
--role "Storage Blob Data Owner" \
--assignee $USER_ID \
--scope $STORAGE_ACCOUNT_IDIn this unit, you will:
tailwind.sql to the Blob Storage account.psql and import a SQL file.Our workload is an application that’s written in Go and works with data inside PostgreSQL. Our data is an open dataset that enables you to explore the power of the Postgres platform and related extensions.
Though this application could easily be run within a container, the stakeholders chose not to do so at this stage. Building a container, deploying to a container platform, or using container orchestration is currently out of scope. But migration to containers might be a logical future step.
The GitHub repository that’s associated with this module provides an application and the related data for you. You learn how to prepare your application and export your data to reach a similar state to this sample application. You can also use the sample application as a template for a greenfield deployment
This module guides you through the migration of an existing workload from an on-premises or cloud environment to Azure. It covers the migration of the compute to an Azure virtual machine (VM) and the data to Azure Database for PostgreSQL. The application is a cloud-agnostic sample that’s a stand-in for any real-world application that’s prepared for migration to the cloud.
In this unit, you explore the value of making the following transitions with the benefit of a full suite of security and identity controls that Azure provides:
You also explore the benefits of managing resources in the cloud from a cost and performance perspective. And you learn how to precisely calculate and manage costs before and after deployment, along with how to optimize performance from both a compute and a data perspective.