How to load data from MySQL to AWS Datalake

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

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About the source and destination

MySQL

MySQL is an SQL (Structured Query Language)-based open-source database management system. An application with many uses, it offers a variety of products, from free MySQL downloads of the most recent iteration to support packages with full service support at the enterprise level. The MySQL server, while most often used as a web database, also supports e-commerce and data warehousing applications and more.

AWS Datalake

An AWS Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. It is designed to handle massive amounts of data from various sources, such as databases, applications, IoT devices, and more. With AWS Data Lake, you can easily ingest, store, catalog, process, and analyze data using a wide range of AWS services like Amazon S3, Amazon Athena, AWS Glue, and Amazon EMR. This allows you to build data lakes for machine learning, big data analytics, and data warehousing workloads. AWS Data Lake provides a secure, scalable, and cost-effective solution for managing your organization's data.

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TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

set up MySQL as a source connector (using Auth, or usually an API key)
set up AWS Datalake as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is MySQL

What is AWS Datalake

Integrate MySQL with AWS Datalake in minutes

Try for free now

Prerequisites

A MySQL account to transfer your customer data automatically from.
A AWS Datalake account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including MySQL and AWS Datalake, for seamless data migration.

When using Airbyte to move data from MySQL to AWS Datalake, it extracts data from MySQL using the source connector, converts it into a format AWS Datalake can ingest using the provided schema, and then loads it into AWS Datalake via the destination connector. This allows businesses to leverage their MySQL data for advanced analytics and insights within AWS Datalake, simplifying the ETL process and saving significant time and resources.

Step 1: Set up MySQL as a source connector

1. Open the Airbyte UI and navigate to the "Sources" tab.

2. Click on the "Add Source" button and select "MySQL" from the list of available sources.

3. Enter a name for your MySQL source and click on the "Next" button.

4. Enter the necessary credentials for your MySQL database, including the host, port, username, and password.

5. Select the database you want to connect to from the drop-down menu.

6. Choose the tables you want to replicate data from by selecting them from the list.

7. Click on the "Test" button to ensure that the connection is successful.

8. If the test is successful, click on the "Create" button to save your MySQL source configuration.

9. You can now use your MySQL connector to replicate data from your MySQL database to your destination of choice.

Step 2: Set up AWS Datalake as a destination connector

1. Log in to your AWS account and navigate to the AWS Management Console.
2. Click on the S3 service and create a new bucket where you will store your data.
3. Create an IAM user with the necessary permissions to access the S3 bucket. Make sure to save the access key and secret key.
4. Open Airbyte and navigate to the Destinations tab.
5. Select the AWS Datalake destination connector and click on "Create new connection".
6. Enter a name for your connection and paste the access key and secret key you saved earlier.
7. Enter the name of the S3 bucket you created in step 2 and select the region where it is located.
8. Choose the format in which you want your data to be stored in the S3 bucket (e.g. CSV, JSON, Parquet).
9. Configure any additional settings, such as compression or encryption, if necessary.
10. Test the connection to make sure it is working properly.
11. Save the connection and start syncing your data to the AWS Datalake.

Step 3: Set up a connection to sync your MySQL data to AWS Datalake

Once you've successfully connected MySQL as a data source and AWS Datalake as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select MySQL from the dropdown list of your configured sources.
Select your destination: Choose AWS Datalake from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific MySQL objects you want to import data from towards AWS Datalake. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from MySQL to AWS Datalake according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your AWS Datalake data warehouse is always up-to-date with your MySQL data.

Use Cases to transfer your MySQL data to AWS Datalake

Integrating data from MySQL to AWS Datalake provides several benefits. Here are a few use cases:

Advanced Analytics: AWS Datalake’s powerful data processing capabilities enable you to perform complex queries and data analysis on your MySQL data, extracting insights that wouldn't be possible within MySQL alone.
Data Consolidation: If you're using multiple other sources along with MySQL, syncing to AWS Datalake allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: MySQL has limits on historical data. Syncing data to AWS Datalake allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: AWS Datalake provides robust data security features. Syncing MySQL data to AWS Datalake ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: AWS Datalake can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding MySQL data.
Data Science and Machine Learning: By having MySQL data in AWS Datalake, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While MySQL provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to AWS Datalake, providing more advanced business intelligence options. If you have a MySQL table that needs to be converted to a AWS Datalake table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a MySQL account as an Airbyte data source connector.
Configure AWS Datalake as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from MySQL to AWS Datalake after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

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Warehouses and Lakes

Databases

How to load data from MySQL to AWS Datalake

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

Try Airbyte Cloud free

Deploy Airbyte Open Source

Step 2: Setup AWS Datalake

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between MySQL and AWS Datalake

Get your data syncing in minutes

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TL;DR

set up MySQL as a source connector (using Auth, or usually an API key)
set up AWS Datalake as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is MySQL

What is AWS Datalake

Integrate MySQL with AWS Datalake in minutes

Try for free now

Prerequisites

A MySQL account to transfer your customer data automatically from.
A AWS Datalake account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.