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.

How to Replicate Data from MySQL to Databricks?

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

Let’s understand replicating the data using the CSV files.

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.
MySQL to Databricks:: Databricks Interface
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  • 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.

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.

Steps to Connect MySQL to Databricks

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.

MySQL to Databricks:: Configure MySQL as a source
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Step 2: Configure Databricks as a Destination

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

MySQL to Databricks: Configure Databricks as Destination
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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 FrequencyMinimum Pipeline FrequencyMaximum Pipeline FrequencyCustom Frequency Range (Hrs)
1 Hr15 Mins24 Hrs1-24

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

Why Replicate Data From MySQL to Databricks?

Let’s understand why there is a need to integrate MySQL to Databricks. Transaction Management and Performance Scaling are the two factors that play an essential role.                    

1) Transaction Management 

COMMIT, stored procedures, and ROLE are not supported by MySQL versions below 5.0. Due to its ineffective transaction management, MySQL is vulnerable to data corruption. 

The following tasks can be carried out for you with the help of the Databricks DBIO package when a user writes a file in a job.

  • Write the distinct transaction id into files.
  • Directly write files to their destination.
  • When the jobs commit, mark the transaction as committed.
  • The following tasks will be handled by DBIO on your behalf when a user attempts to read the files.

Some Examples of Transaction Management in Databricks:

  • Verify the transaction id and status; if the transaction has not finished, either ignore the files or read in your data.
  • The DBIO commit mechanism (docs) can be enabled starting with the Spark 2.1-db4 cluster image with the following SQL configuration:
%sql set
  • The same cluster configuration can also be used to specify this upon cluster construction. All Databricks customers now have access to quick and accurate ETL thanks to the default activation of DBIO transactional commit in Databricks Runtime 3.0 Beta.

2) Performance Scaling

MySQL is one of the most commonly used database alternatives for many applications due to its low cost (free for the community edition), big support community, and versatility. Although MySQL supports handling a high volume of data, it has difficulty dealing with too many operations simultaneously. This means anyone who would be running a significant number of operations at the same time should look for an alternative.

Databricks uses the unified Spark engine to handle complex SQL queries, processing of machine learning queries, and stream data. Databricks is well equipped to handle multiple operations at the same time efficiently.

According to your use case, you can replicate data from MySQL to Databricks, leading to efficient transaction management and performance scaling.

Why Use Hevo?

As the ability of businesses to collect data explodes, data teams have a crucial role to play in fueling data-driven decisions. Yet, they struggle to consolidate the data scattered across sources into their warehouse to build a single source of truth. Broken pipelines, data quality issues, bugs and errors, and lack of control and visibility over the data flow make data integration a nightmare.

1000+ data teams rely on Hevo’s Data Pipeline Platform to integrate data from over 150+ sources in a matter of minutes. Billions of data events from sources as varied as SaaS apps, Databases, File Storage and Streaming sources can be replicated in near real-time with Hevo’s fault-tolerant architecture. What’s more – Hevo puts complete control in the hands of data teams with intuitive dashboards for pipeline monitoring, auto-schema management, custom ingestion/loading schedules. 

All of this combined with transparent pricing and 24×7 support makes us the most loved data pipeline software on review sites.

Sign Up For a 14-day Free Trial Today

Here’s how Hevo challenges the normal to beget the new ‘exceptional.’

  • Reliability at Scale – With Hevo, you get a world-class fault-tolerant architecture that scales with zero data loss and low latency. 
  • Monitoring and Observability – Monitor pipeline health with intuitive dashboards that reveal every stat of pipeline and data flow. Bring real-time visibility into your ELT with Alerts and Activity Logs 
  • Stay in Total Control – When automation isn’t enough, Hevo offers flexibility – data ingestion modes, ingestion, and load frequency, JSON parsing, destination workbench, custom schema management, and much more – for you to have total control.    
  • Auto-Schema Management – Correcting improper schema after the data is loaded into your warehouse is challenging. Hevo automatically maps source schema with destination warehouse so that you don’t face the pain of schema errors.
  • 24×7 Customer Support – With Hevo you get more than just a platform, you get a partner for your pipelines. Discover peace with round the clock “Live Chat” within the platform. What’s more, you get 24×7 support even during the 14-day full-feature free trial.
  • Transparent Pricing – Say goodbye to complex and hidden pricing models. Hevo’s Transparent Pricing brings complete visibility to your ELT spend. Choose a plan based on your business needs. Stay in control with spend alerts and configurable credit limits for unforeseen spikes in data flow.
Visit our Website to Explore Hevo

Let’s Put It All Together

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.

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.

Harsh Varshney
Research Analyst, Hevo Data

Harsh is a data enthusiast with over 2.5 years of experience in research analysis and software development. He is passionate about translating complex technical concepts into clear and engaging content. His expertise in data integration and infrastructure shines through his 100+ published articles, helping data practitioners solve challenges related to data engineering.

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