With software supported in the cloud, many companies prefer to store their on-premise data on a database management service such as Azure MySQL. Integrating this data with a cloud analytics platform like Databricks can enable organizations to produce efficient results through data modeling.

Migrating data from Azure MySQL to Databricks can help you make decisions that scale your business. This article will discuss two of the most prominent methods to load Azure MySQL to Databricks.

Why Integrate Azure MySQL to Databricks?

There are many reasons to consider integrating Azure MySQL with Databricks. It can enable your organization to perform machine learning modeling on data and generate visualizations and dashboards. Using this, you can decide what key performance indicators (KPIs) you should focus on to expand your business performance.

Databricks support machine learning libraries like Hugging Face Transformers, enabling you to integrate pre-trained models and open-source libraries into your workflow.

An Overview of Azure MySQL

Azure MySQL to Databricks: Azure Database for MySQL

Azure MySQL is Microsoft’s fully managed database management service. It provides a cost-effective solution for managing a database with high availability and security at a 99% service level agreement (SLA).

Azure MySQL allows you to automate updates and backups. It also provides an AI-powered optimization and monitoring system that can improve query speed. With Azure MySQL’s flexible server feature, you can build resilient and responsive applications. 

An Overview of Databricks

Azure MySQL to Databricks: Databricks Lakehouse platform

Databricks is an analytics platform that allows users to build, deploy, and maintain applications at scale. Databricks Data Intelligence Platform can securely integrate with your cloud storage account to deploy and manage applications.

Databricks, when working as a data warehouse, provides 12 times better price/performance for SQL workloads with its AI-optimized query execution. It can enable you to generate useful insights from data to make better data-driven decisions. To compare the Azure ecosystem to Databricks, refer to Azure Data Factory vs. Databricks.

Methods to Load Data from Azure MySQL to Databricks

This section will discuss two of the most widely used methods for how to read Azure MySQL in Databricks.

Method 1: Migrate Azure MySQL to Databricks Using Hevo Data

Hevo Data is an ELT data pipeline platform that provides a cost-effective way to automate your data flow process. It enables real-time data integration with a no-code interface. Hevo allows you to connect your data to 150+ data source connectors according to your specific requirements.

Here are some of the key features of using Hevo:

  • Data Transformation: Hevo provides data transformation techniques to streamline your analytical workflow. You can clean and transform your data with its drag-and-drop and Python-based transformation techniques.
  • Incremental Data Load: Hevo enables you to transfer your modified data in real-time, ensuring efficient source and destination end bandwidth.
  • Automated Schema Mapping: Hevo automatically detects the format of incoming data and replicates it according to the destination schema. It allows you to choose between Full & Incremental Mappings to suit your data replication needs.

Configure Azure MySQL as Source

In this section, you will configure Azure MySQL as your data pipeline source. But before this, you must ensure the prerequisite conditions are satisfied.


  • Your Azure MySQL database instance must be running. You can check for it by following these steps:
    • You must log in to your Microsoft Azure Portal.
    • You must confirm the Status field has the value Available in the Overview tab.

Azure MySQL to Databricks: Checking Status of Azure Database Instance

Setting up Azure MySQL as Source

After satisfying the prerequisites, you can follow the steps in this section to configure Azure MySQL as a source.

  • Select PIPELINES from the Navigation Bar.
  • In the Pipeline List View, click + CREATE.
  • Select Azure MySQL in the Select Source Type page.
  • Specify the mandatory fields on the Configure your Azure MySQL Source page.
Configure Azure  MySQL Source

Azure MySQL to Databricks: Configure your Azure MySQL Source

  • Click TEST CONNECTION and select TEST & CONTINUE. After performing the steps, you must configure Object and Query Mode Settings according to your ingestion mode.

You can follow the Azure MySQL Hevo Documentation to learn more about the steps involved.

Configure Databricks as Destination

This section will help you set up Databricks as a destination for your data pipeline. Hevo provides two different methods for connecting Databricks as a destination: Databricks Partner Connect or Databricks Credentials.

Before getting started, ensure the prerequisites are satisfied.

Setting up Databricks as Destination

After satisfying all the prerequisites, you can follow the steps in this section to set Databricks as a destination in the data pipeline.

  • Select DESTINATIONS on the Navigation Bar.
  • From the Destinations List View, click on + CREATE.
  • Select Databricks on the Add Destination page.
  • Specify the necessary details in the Configure your Databricks Destination page.
Azure MySQL to Databricks: Configure your Databricks Destination

Azure MySQL to Databricks: Configure your Databricks Destination

  • Finally, click on TEST CONNECTION and select SAVE & CONTINUE.

To know more about the steps involved in this section, you can refer to Hevo Documentation on Databricks.

Method 2: Sync Azure MySQL to Databricks Using CSV Import/Export

Are you wondering how to insert Azure MySQL data into Databricks table in an alternate way? This section will help you move data from Azure MySQL to Databricks using CSV file transfer.

Step 1: Exporting Data from Azure MySQL

Before starting the steps, you must ensure the prerequisites are satisfied.


Export Data from Azure MySQL

After satisfying the prerequisites, you can follow the steps here:

  • You can create a database on the Azure Database for MySQL flexible server instance.
  • You can determine when to use the import/export techniques.
  • Follow these steps to export the files using the export wizard:
Azure MySQL to Databricks: Table Data Export/Import Wizard

Azure MySQL to Databricks: Table Data Export/Import Wizard

  • You can right-click on the table that you want to export.
  • Select columns, rows offset, and count by clicking on Table Data Export Wizard.
  • You can now select Next on the Select data for export pane. Select the file path, use CSV as the data format, and mention any field you want.
  • You must select Next on the Select output file location pane.
  • Finally, you can click Next on the Export data pane.

To learn more about the steps, follow migrate Azure Database for MySQL.

Step 2: Importing Data into Databricks

You can quickly load data to Databricks using file upload. Follow the steps to do so:

  • Select + New and click on Add data on your Databricks web console.
  • After doing so, you can upload a file to Databricks by selecting the CSV file you exported in the previous step.

To know more about uploading files to Databricks, refer to load data using add data UI.

Limitations of Using CSV Import/Export Method

The CSV Import/Export Method is an efficient way to convert Azure MySQL to Databricks table, but there are some limitations associated with this method.

  • Lack of Automation: The CSV Import/Export method lacks automation. It might become time-consuming because you must manually load data from Azure MySQL to Databricks.
  • Lack of Real-time Data Integration: This method doesn’t automatically account for the new information added to the source. You must look out for updates and repeat the data transfer process.
  • Increase in Errors: You must continuously monitor the data movement from source to destination. Otherwise, it can increase the chances of encountering errors or data inconsistency.

Use Cases of Loading Data from Azure MySQL to Databricks

  • Integrating Azure MySQL to Databricks can enable you to perform advanced analysis on your data to generate valuable insights.
  • Databricks offers users high availability, security, and governance. Its data recovery features enable users to retrieve data without worrying about data loss.
  • Using the Databricks tools to automate, version, schedule, and deploy code and production resources can simplify data monitoring and operations.


This article addresses the Azure MySQL to Databricks data migration process using two widely used methods. Both methods effectively transfer data from Azure MySQL to Databricks, but the second method has some limitations.

To overcome the limitations, you can use Hevo Data, which provides an easy-to-use user interface to make your data integration journey easier. With over 150 source connectors, you can quickly move data without technical knowledge.

Frequently Asked Questions (FAQs)

Q. What is Azure Databricks?

  1. Azure Databricks is one of the most widely used big data analytics platforms that enables users to have a unified workspace, higher availability, security, and more. Its Apache Spark integration allows you to perform data processing and analytics tasks at scale quickly.
Suraj Kumar Joshi
Freelance Technical Content Writer, Hevo Data

Suraj is a technical content writer specializing in AI and ML technologies, who enjoys creating machine learning models and writing about them.

All your customer data in one place.