How to Migrate Data from MySQL to Databricks?

Unlock the full potential of your MySQL data by integrating it seamlessly with Databricks. With Hevo’s automated pipeline, get data flowing effortlessly—watch our 1-minute demo below to see it in action!

Because of its quick and distinctive engine architecture, MySQL is a particularly efficient relational database management system. It is designed for Online Transaction Processing (OLTP), which means it is optimized for handling a maximum number of transactions, and it is still the preferred method for many users. It would be less than ideal, though, if the user wished to perform elaborate analytical queries and aggregate data across various variables.

That said, MySQL might not be a good fit if you want to do extensive Online Analytical Processing (OLAP) and run complex queries to analyze historical data. You might want to use Databricks as your data warehouse. Databricks supports complex query processing — and it does it fast — with the help of a unified Spark engine and the cloud provider you opt for.

You can replicate your data from MySQL to Databricks using CSV files or use an automated data pipeline like Hevo to ease your replicating process.

Why Migrate from MySQL to Databricks?

• Improved Transaction Management: Databricks offers robust transaction handling with its DBIO package, ensuring reliable commits and minimizing the risk of data corruption, unlike older MySQL versions.
• Enhanced Performance Scaling: While MySQL struggles with handling multiple operations simultaneously, Databricks uses the unified Spark engine for efficient scaling, processing large datasets and complex queries.
• Support for Advanced Analytics: Databricks integrates machine learning, stream processing, and advanced analytics seamlessly, offering capabilities beyond MySQL’s traditional database functions.
• Unified Data Platform: Databricks enables ETL, data warehousing, and advanced analytics in one platform, streamlining workflows and reducing the need for multiple tools.

Methods to Replicate Data from MySQL to Databricks?

You can replicate data from MySQL to Databricks using any of the two methods:

Method 1: Connect MySQL to Databricks Using Hevo

The simple steps to carry out MySQL to Databricks using Hevo:

Step 1: Configure MySQL as a Source

Authenticate and Configure your MySQL Source. Hevo supports all the Cloud MySQL Sources as well.

Step 2: Configure Databricks as a Destination

In the next step, we will configure Databricks as the destination.

Step 3: All Done to Setup Your ETL Pipeline

Once your MySQL to Databricks ETL Pipeline is configured, Hevo will collect new and updated data from your MySQL every five minutes (the default pipeline frequency) and duplicate it into Databricks. You can adjust the pipeline frequency from 5 minutes to an hour, depending on your needs.

Data Replication Frequency

Default Pipeline Frequency	Minimum Pipeline Frequency	Maximum Pipeline Frequency	Custom Frequency Range (Hrs)
1 Hr	15 Mins	24 Hrs	1-24

You can set up your Data Pipeline and start replicating data within a few minutes!

Replicating Data from MySQL to Databricks using Hevo

Hevo is an automated data pipeline tool that can replicate data from MySQL to Databricks. Users can replicate data from 150+ sources in a much simpler way into a data warehouse, database, or destination of their choice for further analysis using Hevo. 

Hevo ensures that you always have analysis-ready data by providing a consistent and reliable solution to manage data in real-time.

Method 2: Replicating Data From MySQL to Databricks Using CSV Files

Connecting MySQL to Databricks using CSV files is a 3-step process. Firstly you need to export data from MySQL as CSV files, then export the CSV files into Databricks and modify the data according to your needs.

• Step 1: Users can export tables, databases, and entire servers using the mysqldump command provided by MySQL. This command can also be used for backup and recovery. Below is the command which can be used to export data as CSV files from MySQL Tables:

mysqldump -u [username] -p -t -T/path/to/directory [database] [tableName] --fields-terminated-by=,

The above command will make a copy of tableName at the location you specify in the command.

• Step 2: In the Databricks UI, navigate the Sidebar menu and click on Data. You simply need to click on Create Table, drag the CSV files in the drop zone, or browse the files from your local computer and upload them. Your path will be like this after uploading: /FileStore/tables/<fileName>-<integer>.<fileType>. You can access your data by clicking the Create Table with UI button.

• Step 3: After uploading the Data to the table in Databricks, you can modify and read the data in Databricks as CSV.
  • You must select a Cluster, click on Preview Table, and read CSV data in Databricks.
  • The data types are read as a string by default. You will need to change the data type of your attributes to the appropriate ones.
  • You can update your data from the left navigation bar. It has the following options:
    • Table Name: It helps you change the name of the table.
    • File Type: You can choose file types such as CSV, JSON, and AVRO.
    • Column Delimiter: It represents an attribute separating character. For example, in the case of CSV ‘,’ is the delimiter.
    • First Row Header: You can select the first row’s column as the header.
    • Multi-line: With the help of this option, you can break the lines in the cells.
  • Once you have configured all the above parameters, you need to click on Create Table.
  • You can read the CSV file from the cluster where you have uploaded the file.

Challenges Faced While Replicating Data

In the following situations, it might not be a wise choice:

• Two-way sync is required to update the data. You will need to perform the entire process frequently to access updated data at your destination.
• If you need to replicate data regularly, the CSV method might not be a good fit for you since it’s time-consuming to replicate data using CSV files. 

Organizations can use automated pipelines like Hevo to avoid such challenges. Besides MySQL, Hevo helps you transfer data from databases such as Postgresql, MongoDB, MariaDB, SQL Server, etc.

Using an automated data pipeline tool, you can transfer data from MySQL to Databricks.

Conclusion

In this blog, you learned about the key factors which could be considered for replicating data from MySQL to Databricks. You deeply understood how data could be replicated using CSV files. You also learned about an automated data pipeline solution known as Hevo.

Don’t forget to share your experience of employing a data pipeline from MySQL to Databricks using Hevo in the comment section. Also, check out this video to know how Hevo seamlessly replicates data from wide data sources.

FAQ on MySQL to Databricks

You can employ Hevo today and enjoy fully automated, hassle-free data replication for 150+ sources. You can sign up for a 14-day free trial, which gives you limitless free sources. The free trial supports 50+ connectors up to 1 million events per month and spectacular 24/7 email support to help you get started.