Use case

 Description

Data warehousing and ETL processes

Azure Synapse consolidates data from various sources into a centralized data warehouse. It offers robust ETL capabilities to efficiently transform raw data into structured, usable formats. This centralized data repository is the backbone for enterprise reporting, ensuring decision-makers can access consistent and reliable data.

Real-time data analytics

Azure Synapse supports real-time data processing, enabling organizations to capture and analyze data as it’s generated. This capability is crucial for monitoring live events, detecting anomalies, or making instant decisions based on up-to-the-minute information.

Predictive analytics and machine learning

By integrating seamlessly with Azure Machine Learning, Azure Synapse allows businesses to perform advanced predictive analytics. Organizations can combine historical data with machine learning models to forecast trends, predict outcomes, and make data-driven decisions more accurately.

Business intelligence reporting

Azure Synapse integrates with Power BI to create rich, interactive data visualizations and reports. This integration helps organizations turn raw data into compelling dashboards and reports that provide actionable insights.

Azure Synapse

 Databricks

Platform focus

An all-in-one solution combining data integration, warehousing, and big data analytics. Ideal for holistic solutions.

Focuses on Apache Spark-based big data processing and machine learning. Strong in collaborative data science, engineering, and model deployment.

Data storage integration

Seamless integration with Azure Data Lake and Blob Storage.

Strong integration with cloud storage services like Azure Data Lake and Amazon S3.

SQL support

Native SQL support for data warehousing.

It uses Apache Spark SQL and is optimized for big data scenarios.

Ecosystem integration

Close integration with other Azure services.

Aligns more with the open-source Apache Spark ecosystem.

After a comprehensive overview of Azure Synapse, let’s get hands-on!

Setting Up Azure Synapse

To get started with Azure Synapse, you’ll need to have an active Azure account. Once your account is set up, you can create a new Synapse workspace and configure your data sources and connections. 

1. Start Azure free trial

If you’re new to Azure, the first step is to create a subscription. Click the “Start” button under “Start with an Azure free trial.” 

During the signup process, you’ll need to verify your account using a phone number and provide credit card information for verification purposes.

Start with an Azure free trial.

2. Prerequisite: Create Data Lake Storage Gen2

Before proceeding with Azure Synapse, you must create a Data Lake Storage Gen2 account to store and manage your data. 

Start by navigating to the Azure portal and selecting “Create a resource.” Choose “Storage account” and fill in the required details, such as the resource group, storage account name, and region. 

Ensure that “Azure Blob Storage or Azure Data Lake Storage Gen2” is selected as the primary service, and configure other settings like performance and redundancy as per your use-case.

Create an Azure storage account.

Create an Azure storage account.

After filling in the details, click “Review + create” to deploy the storage account. It can take several minutes before the storage deployment is complete.

Storage account deployment completed.

Once the deployment is complete, your new Data Lake Storage Gen2 account will be listed under the Storage Accounts section and will be ready for use with Azure Synapse.

3. Create Synapse workspace

Azure Synapse workspace is the foundational environment where you can set up, organize, and manage all the resources and services needed for data integration, analytics, and storage within Azure Synapse. It acts as the central hub for configuring and accessing various tools and data assets in your Synapse project.

Create Azure Synapse workspace by clicking the “Create Synapse Workspace” button.

Creating Synapse workspace.

In the next step, you’ll need to fill out the form to create your Azure Synapse workspace. 

Start by selecting your subscription and resource group, then enter a name for your workspace and choose the appropriate region. 

Creating a Synapse workspace – filling in details.

Review the details on the final tab before clicking the “Create” button. 

Validating the Synapse workspace.

It can take several minutes before the Azure Synapse workspace is deployed.

 Azure Synapse Analytics deployment in progress.

Azure Synapse Analytics workspace “demotest3212” created.

Once the workspace is deployed, click on its name to open it.

4. Open Synapse Studio

Azure Synapse Studio is the web-based interface for managing and interacting with your Azure Synapse workspace. It provides a unified workspace where you can perform data integration, big data analytics, and data warehousing tasks all in one place. 

Synapse Studio is essential because it lets you quickly develop, manage, and monitor your data pipelines, SQL scripts, Spark jobs, and more without switching between different tools or environments. 

Synapse Studio.

Importing a Dataset

In Synapse Studio, you can import the data from several different sources. You can import it from a Gen2 storage account linked to the Synapse workspace (see step 2 above), from a SQL server database, or external sources. 

For this tutorial, we will use one of the sample datasets, “Bing COVID-19 Data,” available in the Synapse Gallery. 

To import, click on “Dataset” on the left-hand side navigation menu and then click on “+ sign” → “Gallery.”

Dataset Gallery in Synapse Studio.

You can review the metadata and sample rows from the data before clicking the “Add dataset” button to import this data.

Review dataset in Synapse Studio.

Once the import is successful, you will be able to see the dataset under “Data.”

Data tab in Synapse Studio.

Writing and Running Queries

Azure Synapse Studio provides a user-friendly interface for writing and running queries. You can use SQL to perform a wide range of data analysis tasks, from simple data retrieval to more complex analytics. 

Synapse Studio also allows you to save and manage your queries and view and handle the results of your queries.

You can analyze this dataset using an SQL script or by creating a Notebook. In a Notebook, you can load the dataset as a Spark DataFrame and use Spark for data manipulation and analysis.

To run SQL queries on this dataset, click the three dots next to the dataset name.

Analyzing Data in Synapse Studio with SQL.

Clicking “Select TOP 100 rows” will open an SQL editor where you can write SQL queries and execute them to view the results.

SQL editor in Synapse Studio.

If you want to visualize the output instead of a table view, click “Chart” under “Results”.

Viewing query results as Chart in Synapse Studio.

Those changes are initially saved as drafts when you create or modify a SQL script. Publishing the script by clicking the “Publish” button on top commits those changes, ensuring the latest version is stored in the workspace.

Publishing an SQL script in Synapse Studio means saving your script to the Synapse workspace, making it available for future use, collaboration, and version control.

Example: Analyzing daily growth in COVID-19 confirmed cases worldwide

Let’s run an SQL query on this dataset to analyze the daily increase in COVID-19 confirmed cases worldwide. 

The query retrieves data from the “Bing COVID-19 dataset”, calculates the number of new cases reported each day by comparing the current day’s confirmed cases to the previous day’s count, and orders the results by date. 

SQL query in Synapse Studio SQL editor.

Analyzing Data in Notebooks

In Synapse Studio, you can analyze data using notebooks, which provide an interactive environment for running code, visualizing results, and conducting data analysis. 

Notebooks in Synapse Studio support multiple languages, including PySpark, which is particularly powerful for big data processing. 

To run a Notebook in Synapse Studio, attach it to an Apache Spark pool, which provides the necessary distributed computing resources to process large datasets efficiently.

An Apache Spark pool is a collection of compute nodes that are dynamically allocated to run your Spark jobs. If you don’t already have a Spark pool, you can create one by navigating to the “Manage pools” section in Synapse Studio, where you can specify the number of nodes, their size, and other configurations. 

Once your Spark pool is set up and attached to the notebook, you can execute code cells within the notebook to load, manipulate, and analyze data, as shown in the screenshot below. 

This setup enables you to leverage the full power of Spark for large-scale data analysis directly within Azure Synapse.

Analyze data using Notebooks in Synapse Studio.

Integrating Azure Synapse with Other Azure Services

Azure Synapse integrates seamlessly with other Azure services, enabling you to build comprehensive data analytics solutions. 

Some key integrations include:

Azure Data Factory: Utilize Azure Data Factory to orchestrate complex data workflows and automate ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes. By integrating Azure Synapse with Data Factory, you can easily move and transform data from various sources into your Synapse workspace, ensuring your data is always ready for analysis.
Power BI: Azure Synapse integrates smoothly with Power BI, allowing you to create advanced data visualizations and interactive dashboards. This integration enables businesses to transform raw data into insightful, visually compelling reports that can be shared across teams, fostering data-driven decision-making and enhancing business intelligence capabilities.
Azure Machine Learning: Combine the data processing power of Azure Synapse with Azure Machine Learning to unlock advanced predictive analytics capabilities. This integration allows you to train, deploy, and manage machine learning models directly within your Synapse environment, enabling more accurate predictions and smarter data-driven strategies.
Azure Databricks: For organizations focused on collaborative data science and machine learning, integrating Azure Synapse with Azure Databricks provides a powerful solution. This integration facilitates seamless collaboration among data scientists, engineers, and analysts, allowing them to build and scale data pipelines, develop models, and conduct advanced analytics in a unified, collaborative environment.

Best Practices for Using Azure Synapse

To get the most out of Azure Synapse, it’s important to follow best practices, such as:

Optimizing data storage formats: Selecting the right data storage formats, such as Parquet or ORC, is crucial for ensuring optimal query performance and efficient data processing. These formats are designed for big data analytics and can significantly reduce query execution times and storage costs by supporting columnar storage and compression.
Managing compute resources efficiently: Efficiently managing compute resources is key to balancing performance and cost-effectiveness. By scaling resources up or down based on workload demands and using serverless options where appropriate, you can ensure that you are not overspending on unused compute power while still meeting performance requirements.
Implementing security best practices: Security should be a top priority when using Azure Synapse. To protect sensitive information, implement robust security measures, such as data encryption, role-based access control, and network isolation.
Monitoring and troubleshooting workloads: Continuous monitoring of your Azure Synapse workloads is essential for maintaining optimal performance and identifying potential issues before they impact operations. Utilize built-in monitoring tools to track resource usage, query performance, and data pipeline efficiency, and be proactive in troubleshooting any anomalies to minimize disruptions.

Conclusion

Azure Synapse Analytics stands as a powerful and versatile solution for organizations seeking to harness the full potential of their data. By unifying data integration, big data analytics, and enterprise data warehousing into a single, comprehensive platform, Azure Synapse empowers businesses to streamline their data operations and extract valuable insights with unprecedented efficiency.

The platform’s flexibility, scalability, and seamless integration with other Azure services make it ideal for various data-driven tasks, from real-time analytics to complex machine learning projects. As data grows in volume and importance, Azure Synapse positions itself as a crucial tool for organizations looking to stay competitive in an increasingly data-centric world.

By adopting Azure Synapse, businesses can optimize their current data processes and pave the way for future innovations in data analytics. As we move forward, the ability to quickly and effectively turn data into actionable insights will be a key differentiator for successful organizations. Azure Synapse provides the robust foundation needed to meet this challenge head-on, enabling businesses to unlock new opportunities and drive growth through the power of data.