How to load data from MySQL to MongoDB

Learn how to use Airbyte to synchronize your MySQL data into MongoDB within minutes.

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Building in-house pipelines

Bespoke pipelines are:
  • Inconsistent and inaccurate data
  • Laborious and expensive
  • Brittle and inflexible
Furthermore, you will need to build and maintain Y x Z pipelines with Y sources and Z destinations to cover all your needs.

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  • Reliable and accurate
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  • Deployed and governed your way
All your pipelines in minutes, however custom they are, thanks to Airbyte’s connector marketplace and AI Connector Builder.

Start syncing with Airbyte in 3 easy steps within 10 minutes

Set up a MySQL connector in Airbyte

Connect to or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up MongoDB for your extracted MySQL data

Select where you want to import data from your source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the MySQL to MongoDB in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.
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Check out our interactive demo and our how-to videos to learn how you can sync data from any source to any destination.

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Move Large Volumes, Fast

Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.

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Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.

Fully Featured & Integrated

Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.

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Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.

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How to Sync to Manually

Step 1: Prepare Your Google Sheets Data

  1. Open your Google Sheet.
  2. Cleanse the data: Make sure the data is in a consistent format that Snowflake can understand. This includes checking data types, date formats, and null values.
  3. Define headers: Ensure that the first row of your Google Sheet contains the column headers that you will use as field names in Snowflake.

Step 2: Export Data from Google Sheets‍

  1. Export as CSV: Click on File > Download > Comma-separated values (.csv, current sheet). This will download the current sheet to your local machine as a CSV file.

Step 3: Prepare Your Snowflake Environment

  1. Log in to Snowflake: Use your credentials to log in to the Snowflake web interface.
  2. Create a Database and Schema (if not already existing):
    CREATE DATABASE IF NOT EXISTS my_database;
    USE DATABASE my_database;
    CREATE SCHEMA IF NOT EXISTS my_schema;
    USE SCHEMA my_schema;
  3. Create a Table: Define a table in Snowflake that matches the structure of your Google Sheets data.
    CREATE TABLE my_table (
    column1_name column1_datatype,
    column2_name column2_datatype,
    …
    );

Step 4: Upload the CSV File to a Staging Area‍

  1. Create a File Format for CSV files (if not already existing):
    CREATE FILE FORMAT my_csv_format
    TYPE = 'CSV'
    FIELD_DELIMITER = ','
    SKIP_HEADER = 1
    NULL_IF = ('NULL', 'null');
  2. Create a Stage to hold your CSV file:CREATE STAGE my_stageFILE_FORMAT = my_csv_format;
  3. Upload the CSV to the Stage:You can use Snowflake's web interface to manually upload the CSV file to the stage you created. Alternatively, you can use Snowflake's PUT command to upload the file from your local machine if you have the Snowflake CLI installed.
    PUT file:///path/to/yourfile.csv @my_stage;

Step 5: Copy Data into Snowflake Table

Copy the data from the stage to your Snowflake table:

COPY INTO my_table

FROM @my_stage/yourfile.csv

FILE_FORMAT = (FORMAT_NAME = my_csv_format)

ON_ERROR = 'CONTINUE';

‍

Adjust the ON_ERROR parameter based on your preference for handling errors during the copy process.

Step 6: Verify the Data Load

  1. Check the loaded data:
  2. SELECT * FROM my_table;
  3. Review any errors that occurred during the data load process and adjust your data or table schema as necessary.

Step 7: Clean Up

  1. Remove the CSV from the stage after the data load is successful:

REMOVE @my_stage/yourfile.csv;

  1. Drop the stage and file format if they will not be used again:

DROP STAGE my_stage;DROP FILE FORMAT my_csv_format;