Azure Synapse Analytics

Introduction

Cloud computing applications and platforms are exploding across all industries today, serving as the IT backbone that powers new digital ventures. These platforms and applications have changed the way firms operate and made operations more efficient. More than 77 percent of firms already use the cloud for at least some computing infrastructure.

Azure Synapse is a business analytics service that reduces the time it takes to gain insight from data warehouses and extensive data systems. SQL technologies for business data warehousing, Spark technologies for big data, Data Explorer for log and time-series analytics, Pipelines for data integration and ETL/ELT, and deep connection with other Azure services like Power BI, CosmosDB, and AzureML are all included in Azure Synapse.

Industry-leading SQL

Synapse SQL is a T-SQL distributed query system that enables data warehousing and virtualization scenarios and extends T-SQL to handle streaming and machine learning applications.

Both serverless and dedicated resource types are available in Synapse SQL. Create dedicated SQL pools to reserve processing resources for data stored in SQL tables for predictable performance and cost. Use the always-available, serverless SQL endpoint for unexpected or bursty workloads.
Stream data from cloud sources into SQL tables using the built-in streaming features.
Use machine learning models to store data using the T-SQL PREDICT function to combine AI and SQL.

Creating a Synapse Workspace

In this section, you will learn how to create a synapse workspace.

Prerequisites

You must have access to a resource group for which you have been assigned the Owner role to complete the stages. In this resource group, create the Synapse workspace.

Start the process

Open the Azure Portal and enter Synapse.
In the search results under Services, select Azure Synapse Analytics.
Choose to Add to create a workspace.

Workspace details

Workspace name: Take any globally unique word.
Region: Select the region where you have placed your client applications.

Completing the process

Select Review + Create, and your workspace is ready in a few minutes.

Open Synapse Studio

After creating the Azure Synapse workspace, you have two ways to open Synapse Studio:

In the Azure portal, open your Synapse workspace and click Open in the Open Synapse Studio box in the Overview section of the workspace.
Sign in to your workspace by going to https://web.azuresynapse.net.

Analyze data with a serverless SQL pool

In this section, we shall be analyzing the data with a serverless SQL pool.

The built-in serverless SQL pool

You can use SQL without reserving capacity with serverless SQL pools. The amount of data processed to conduct the query, not the number of nodes used to run the query, determines how much a serverless SQL pool costs.

Built-in is a pre-configured serverless SQL pool that comes with every workspace.

Analyze with Data Explorer

In this section, we will be learning the basic steps to load and analyze data with Data Explorer for Azure Synapse.

Create a Data Explorer pool

Select Manage > Data Explorer pools from the left-hand pane of Synapse Studio.
Select New, and then under the Basics page, fill in the following information, including setting and suggested values.
Built-in is a pre-configured serverless SQL pool that comes with every workspace.
Select Review + Create, and your data explorer will begin the provisioning process.

Create a Data Explorer database

Select Data from the left-hand window of Synapse Studio.
Copy and paste the information into the + (Add new resource) > Data Explorer database: setting and Suggested value.
Select the Create to create the database, which will take less than a minute.

Analyze with Apache Spark

In this section, we shall be discussing the basic steps to load and analyze the data with Apache spark for Azure Synapse.

Create a serverless Apache Spark tool

Select Manage > Apache Spark pools from the left-hand pane of Synapse Studio.
Choose New.
Enter Spark1 as the name of the Apache Spark pool.
Enter Small for Node Size.
Regarding the number of nodes, Set the minimum and maximum to 3 and 3 and 3 and 3 and 3 and 3 and 3 and 3 and 3 and 3 and 3
Select Review + Create > Create from the drop-down menu. In a few seconds, your Apache Spark pool will be ready.

Understanding serverless Apache Spark pools

A serverless Spark pool is a mechanism for users to specify how they wish to use Spark. If necessary, a Spark session is started when you first use a pool. The pool determines how many Spark resources will be consumed by that session and how long it will run before automatically pausing. You pay for the spark resources utilized for that session, not the pool. A Spark pool allows you to work with Spark without having to worry about cluster management. A serverless SQL pool works similarly.

Must Read Apache Server

Analyze data with dedicated SQL pools

In this tutorial, we will create a dedicated SQL pool.

Create a dedicated SQL pool

Select Manage > SQL pools under Analytics pools in Synapse Studio's left-hand pane.
Select SQLPOOL1 for the dedicated SQL pool name.
Choose DW100C for the performance level.
Select Review + Create > Create from the drop-down menu. Your dedicated SQL pool will be up and running in a few minutes.

SQLPOOL1 is the name of the SQL database that your dedicated SQL pool is linked to.

Select Data > Workspace from the drop-down menu.
A database named SQLPOOL1 should appear. Click Refresh if you don't see it.

Analyze data in a storage account

In this section, we will discuss the data in a storage account.

Overview

So far, we've looked at cases in which data is stored in workspace databases. We'll now demonstrate how to operate with files stored in storage accounts. In this case, we'll use the workspace's primary storage account and container, which we defined when the workspace was created.

contosolake is the name of the storage account.
Users are the name of a container in the storage account.

Integrate with pipelines

This example will be learning how to integrate with pipelines and activities using Synapse Studio.

Create a pipeline and add a notebook activity

Go to the Integrate hub in Synapse Studio.
To make a new pipeline, go to Add > Pipeline. To launch the Pipeline designer, click on the new pipeline object.
Expand the Synapse folder under Activities and drag a Notebook object into the designer.
The Settings tab of the Notebook activity properties should be selected. Select a notebook from your current Synapse workspace using the drop-down list.

Schedule the pipeline to run every hour

Select Add trigger > New/edit in the pipeline.
Select New from the Choose trigger menu, then set the Recurrence to "every 1 hour."
Choose OK.
Choose Publish All.

Visualize data with Power BI

In this section, we will learn how to visualize data with Power BI.

Create a Power BI workspace

Sign into powerbi.microsoft.com.
Click on the workspace, then select Create a workspace. Create a new Power BI workspace with a unique name.

Link your Azure Synapse workspace to your new Power BI workspace

Go to Manage > Linked Services in Synapse Studio.
Connect to Power BI from the New menu.
Set the name of the workspace to NYCTaxiWorkspace.
Name the workspace after the Power BI workspace you just built.
Click the Create button.

Monitor your Synapse Workspace

In this section, you will learn how to monitor your Synapse workspace.

Introduction to Monitor Hub

Navigate to the Monitor hub in Synapse Studio. You may see a history of all the activities that have occurred in the workspace and are currently ongoing.

Pipelines, triggers, and integration runtimes can all be monitored under Integration.

You can keep track of Spark and SQL actions under Activities.

Data explorer activities

Go to Activities > KQL Requests to get started.
KQL requests can be seen in this view.
From the Pool filter, choose a Pool to monitor. You may now see all KQL requests running in your workspace in that pool.
To see the full text of a specific KQL request, locate it and click the More link.

Apache Spark activities

Go to Apache Spark apps > Activities. You can now see all Spark applications currently running or recently completed in your workspace.
Locate and click on the name of an application that is no longer functioning. You may now see the spark application's details.
If you're acquainted with Apache Spark, clicking on the Spark history server will take you to the usual Apache Spark history server UI.

SQL activities

Select Activities > SQL Requests from the drop-down menu.
SQL requests can be seen in this view.
From the Pool filter, choose a Pool to monitor. You may now see all ongoing SQL requests or have run in that pool in your workspace.
To see the full text of a specific SQL request, locate it and click the More link.

Add an administrator to your Synapse workspace.

In this section, we shall be learning how to add an administration to the Synapse workspace.

Owner role of the workspace

Open your Synapse workspace in the Azure portal.
From the left-hand menu, select Access control (IAM).
To access the Add role assignment page, go to Add > Add role assignment.
Assign the following responsibilities. See Assign Azure roles using the Azure portal for further information.

Role assignment on the workspace's primary storage account

In the Azure portal, go to the workspace's primary storage account.
From the left-hand menu, select Access control (IAM).
To access the Add role assignment page, go to Add > Add role assignment.
Assign the following responsibilities. See Assign Azure roles using the Azure portal for further information.

Frequently Asked Questions

What is Azure Synapse?

Azure Synapse is a business analytics service that reduces the time it takes to gain insight from data warehouses and extensive data systems.

What is Microsoft Azure Synapse analytics?

Big data analytics, such as enterprise data warehousing, uses Azure Synapse Analytics to analyze and manage heterogeneous data workloads, which is helpful in BI and machine learning.

What is PolyBase in Azure Synapse?

The PolyBase functionality in Azure Synapse allows you to query and import external data using T-SQL. This is beneficial when importing data from Azure Blob storage or Data Lake. At this time, the Azure SQL database does not support PolyBase.

Conclusion

We learned about Azure Synapse Analytics in this article. We discussed Industry-leading SQL. We also discussed creating a Synapse Workspace, including prerequisites, starting the process, workspace details, completing the process, and opening the Synapse studio. We also discussed how to analyze data with a serverless SQL pool, with data explorer, with apache-spark, with a dedicated SQL pool, analyze data in a storage account, integrate with pipelines, visualize data with BI, and monitor synapse workspace.

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