SQL Server to Databricks Migration For Unified Data Analytics

With the advancing technologies, data analytics and processing have now become a crucial requirement for businesses and organizations. SQL Server to Databricks migration has made it possible for the database administrators to benefit from the advanced data analytics features of Databricks, and further helps with enhancing the overall database performance. 

Through this technical write-up, we will learn more about how to connect SQL Server to Databricks and how this migration can help the database administrators more effectively. But, before jumping right to the methods and other technicalities, we will first understand what Databricks is and why it is used. 

 

Understanding Databricks And Its Functionalities

Databricks can be specified as a unified data analytics platform that offers an environment for database analytics, data processing, machine learning, and data engineering. Founded by Apache Spark, Databricks came to ease large data processing by eliminating the need for multiple tools for various data workloads. Here are some of the features and functionalities of Databricks that will help database administrators and organizations to analyze data in a more advanced way. 

Reasons For SQL Server to Databricks Migration

• Due to data processing limitations in SQL Server.
• To implement modern analytics in SQL Data.
• For improving cost optimization in the SQL Server database.
• To benefit from Databricks cloud native features.
• For real-time data analysis and processing.
• Improved disaster recovery for data in SQL Server.

Features of Databricks To Enhance SQL Server Data Analytics

We will now take a look at the features and functionalities that will allow users to enhance their data analysis in SQL Server and further improve the database performance. 

• Easy Connectivity With SQL Server Database: Databricks offers easy and secure connection with SQL Server using the JDBC or ODBC drivers. These drivers then allow the users and database administrators to read and access the SQL Server data directly from Databricks. 
• Efficient Handling of Large Data in SQL: Databricks allows users to load large SQL Data and further process it in no time. This is made possible with the help of Spark, which further allows the users to handle data more quickly. 
• Enhanced Data Transformation: With the help of SQL Server to Databricks migration, it is now much easier and convenient to filter, clean, and transform the data with the help of Python or SQL. 
• SQL Data Analytics: When users connect SQL Server to Databricks, they can run SQL queries faster and also integrate the results with the BI tools, such as Power BI for reports. 

These are a few features that allow users to analyze SQL Server data in a more secure and advanced way. Now, after learning the features, it is time for us to understand the methods to import data from SQL Server to Databricks and how it can help them. Let’s take a thorough look at these ways and understand the steps and challenges with them ways. 

How to Connect SQL Server to Databricks? Possible Ways Explained

There are different ways that allow the database administrators to carry out the SQL Server to Databricks migration process. The first method we will now discuss is the migration using JDBC Drivers. Let’s take a look at the steps on how this method can be implemented precisely. 

Method 1: Connecting SQL Server Database to Databricks Using JDBC

This method is quite effective for users who have technical expertise and developers. The prerequisites for this method are as follows:

• Credentials of the desired SQL Server.
• Complete access to the SQL Server database.
• JDBC drivers for the import process. 

Now, moving on to the steps, let’s see how we can migrate the SQL Server database to Databricks, using this method. The steps are as follows:

1. Open a Databricks notebook to begin the import process. 
2. Configure the connection setup. Use the commands given below for the same:
   jdbcHostname = “your_sql_server_host”
   jdbcPort = 1433
   jdbcDatabase = “your_database”
   jdbcUsername = “your_user”
   jdbcPassword = “your_password”
3. The next step is to create a JDBC URL for SQL Server. The command that will help to create a URL is given below:
   jdbcUrl = f”jdbc:sqlserver://{jdbcHostname}:{jdbcPort};databaseName={jdbcDatabase}”
4. After creating the URL, configure the connection properties. This can be done using the following command:
   connectionProperties = {
    “user” : jdbcUsername,
    “password” : jdbcPassword,
   “driver” : “com.microsoft.sqlserver.jdbc.SQLServerDriver”
   }
5. Once the connection is secured with the Databricks notebook, it is now time to read the SQL Server data in Databricks. Use the given command for this purpose:
   df = spark.read.jdbc(
   url=jdbcUrl,
   table=”dbo.YourTableName”,
   properties=connectionProperties
   )
   df.show()

These steps will allow you to effectively complete the SQL Server to Databricks migration process and further help you analyze the SQL data using Databricks features. Let’s now move to the next method to understand how it can help the users import data from SQL Server to Databricks. 

Method 2: Connect SQL Server to Databricks By Importing Data As CSV File

This is another convenient method for the database administrators to upload the SQL Server data as a CSV file in Databricks. As we can see, this method requires the users to first export database from SQL Server to CSV file format, and then import the CSV file to Databricks. 

To convert SQL data to CSV without compromising database integrity, users can opt for a professional solution, such as SysTools SQL Database Recovery Tool. This utility is capable of exporting the data from a SQL Server database to any desired database or to a CSV format as required by the user.

Here are the steps that will allow the users to export the file as CSV and then import the data into Databricks. 

Phase 1: Export SQL Server Data As CSV File Format

1. Install and run the suggested software. Add the database files using the Open Button. 
2. After the files are added, choose a scan mode to scan the database files for any corruption. 
3. The software will then allow you to preview the scanned database files. Click on the Export Tab to export the file in the desired format. 
4. Choose the CSV file format, and then select the data to be exported as CSV. 
5. Click on the Export button to transform SQL Server data into CSV format. 

After the CSV file has been created, we will now proceed with the SQL Server to Databricks migration. 

Phase 2: Import Data from SQL Server to Databricks Using CSV File

1. Open Databricks and then go to Workspace. 
2. Next, create a notebook in Databricks.
3. From the data panel in Databricks, go to Add data, and then click on Upload Files.
4. After the files are uploaded, read the SQL data in Databricks using the following command:
   df = spark.read.option(“header”, “true”).csv(“/FileStore/your_file.csv”)
   df.show()

With the help of these steps, you can easily export the SQL data as CSV and further import it into Databricks for the required operations. Now, moving on to the next method, let’s take a look at how it will help the users with the desired SQL Server to Databricks migration. 

Method 3: Migrate SQL Server to Databricks Using Azure Data Factory

This method is more of a graphical user interface type of method, and is much convenient for users who do not understand much technicalities. Furthermore, this method allows users to understand the steps they are following, making the entire process easier for the users. 

The steps to this method are as follows:

1. The first step is to go to the Azure Portal and then open the Azure Data Factory(ADF). 
2. Next, create a new pipeline in ADF.
3. Add SQL Server as the source dataset and further add the required credentials for the server.
4. To add the target dataset, select the Azure Databricks Delta Lake or a new notebook. 
5. Next, connect the source with the Databricks workspace. 
6. Once both datasets are connected, map the SQL Server columns with the target dataset. 
7. Lastly, test the dataset after the SQL Server to Databricks migration process. 

These steps will allow you to import data from SQL Server to Databricks using Azure Data Factory. All the methods mentioned above will allow you to effectively connect SQL Server to Databricks. Now, let us take a look at the preventive measures to avoid any risks during this migration process and further carry out the entire process in a more secure way. 

Preventive Measures To Connect SQL Server to Databricks Safely

Here are some factors that are at a major risk during the entire SQL Server data to Databricks migration process. 

• To preserve data integrity during the import process, make sure to take a complete backup of the SQL Server database before initiating the process. 
• To maintain a secure connection and controls, limit the Databricks notebooks’ access using Role-Based Access Control. 
• Filter the data to avoid importing large tables that aren’t required in Databricks. For migrating large databases, don’t run a single operation; instead, convert them into batches and then import them. 
• To preserve schema and data type compatibility, manually map the SQL Server data and its types. 
• As a disaster recovery plan, in case the process fails, create restore points in the SQL Server and further test the process first in a sample production environment.

Conclusion

With the help of this write-up, we have understood the requirement of SQL Server to Databricks migration. Additionally, we also discussed the benefits of this migration process and suggested the best ways that help the users connect SQL Server to Databricks in a secure and precise manner. So, if you are looking for a guide to help you to import data from SQL Server to Databricks.