Integrate Amazon Aurora for MySQL with Databox

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Amazon Aurora is a cloud-native relational database engine from AWS that is fully compatible with MySQL while delivering up to five times the throughput of standard MySQL on the same hardware. Aurora clusters automatically replicate data across multiple Availability Zones, handle failover, and manage storage scaling without manual intervention. Connecting Amazon Aurora for MySQL to Databox lets you pull data directly from your Aurora cluster, build datasets using SQL queries, and visualize business-critical figures alongside data from your other connected tools.

Note: This article covers Aurora clusters running the MySQL-compatible engine. If your cluster uses the PostgreSQL-compatible engine, see Integrate Amazon Aurora for PostgreSQL with Databox instead. The steps for configuring public access and security group rules in AWS are the same for both engine types.

If you've already established a connection, you can reuse it to add new data sources to your Databox account.

Databox only reads data from your database — it never writes to it. Create a dedicated MySQL user with SELECT-only privileges scoped to the Databox IP address. You can run these commands using a MySQL client connected to your Aurora cluster endpoint, or via the Query Editor in the AWS Management Console.

MySQL 8.0+ (modern syntax):

CREATE USER 'databox'@'52.4.198.118' IDENTIFIED BY 'your_secure_password';
GRANT SELECT ON your_database.* TO 'databox'@'52.4.198.118';
FLUSH PRIVILEGES;

MySQL 5.7 (legacy single-statement syntax):

GRANT SELECT ON your_database.* TO 'databox'@'52.4.198.118' IDENTIFIED BY 'your_secure_password';
FLUSH PRIVILEGES;

Replace your_database with the name of the database you want to connect, and your_secure_password with a strong password. If you need to grant access to multiple databases, repeat the GRANT statement for each one.

Note: Avoid the following special characters in your password, as they can cause encoding issues when establishing the connection: `, ', ", /, \, and spaces.

By default, Aurora instances are not accessible from outside their VPC. To allow Databox to reach your cluster, each instance in the cluster must be configured as publicly accessible.

1. In the AWS Management Console, go to Databases and select your Aurora DB cluster.
2. Select an individual DB instance within the cluster (not the cluster itself).
3. Click Modify.
4. Under Connectivity, set Public access to Yes.
5. Click Continue, then choose Apply immediately.
6. Click Modify DB instance to save.
7. Repeat for any other instances in the cluster.

Note: For the cluster to be reachable after enabling public access, its subnet group must include subnets with an internet gateway attached. If connectivity still fails, verify that a route to 0.0.0.0/0 via an internet gateway exists in the associated route table.

Aurora controls network access through VPC security groups. Add an inbound rule that permits TCP traffic on port 3306 from the Databox IP address.

1. In the AWS Management Console, go to Databases and select your Aurora DB cluster.
2. Under Connectivity & security, click the link to the VPC security group associated with the cluster.
3. Select the security group, then click Edit inbound rules.
4. Click Add rule and fill in:
   • Type: MySQL/Aurora
   • Protocol: TCP
   • Port range: 3306
   • Source: Custom — enter 52.4.198.118/32
5. Click Save rules.

1. In Databox, go to Data Sources > + New connection.
2. Search for MySQL and click Connect.
3. Fill in the connection form:
   • Data source name — a label for this connection in Databox.
   • Host — your Aurora cluster endpoint, found on the cluster's Connectivity & security tab in the AWS Console (e.g., my-cluster.cluster-abc123.us-east-1.rds.amazonaws.com). You can also use the reader endpoint (my-cluster.cluster-ro-abc123.us-east-1.rds.amazonaws.com) to route queries to read replicas only.
   • Port — the port your Aurora cluster listens on. The default for MySQL-compatible Aurora is 3306.
   • User — the MySQL username created in Step 1.
   • Password — the password for that user.
   • Database name (optional) — the specific database to connect to. Leave blank to connect at the server level.
   • Timezone — the time zone used to interpret date values in query results. Defaults to Etc/UTC.
4. Select an SSL/TLS mode (see SSL/TLS below).
5. Click Connect.

Databox offers three SSL/TLS modes when connecting to a database. Choose the one that matches your server's configuration.

Amazon Aurora provides SSL/TLS certificates signed by AWS-managed certificate authorities. To use SSL with server certificate verification, download the appropriate CA certificate bundle from AWS and paste its contents into the CA certificate bundle (ssl-ca) field in Databox. For step-by-step instructions on configuring each SSL mode, see Enable SSL/TLS for your database connection.

The Amazon Aurora for MySQL integration supports the creation of datasets, which allow you to define and shape the specific data you want to use for reporting in Databox. Datasets make it easier to focus on the most relevant information, enabling you to filter, visualize, and analyze metrics across projects, teams, and clients without writing complex queries each time.

1. Select a table: Pick the appropriate schema within that database.
2. Select columns: Browse and select the specific columns (fields) from your tables or views to include in your dataset. These columns define the structure and content of your dataset.

For more advanced use cases, you can write a custom SQL query instead of selecting columns manually. This allows you to:

• Join multiple tables
• Apply filters and aggregations
• Format or transform data before importing it into Databox

Your query must return a valid tabular result to be used as a dataset.

• Amazon Aurora documentation — Official AWS docs for Amazon Aurora, covering cluster creation, connectivity, security, backups, global databases, and monitoring.
• Amazon Aurora MySQL-compatible edition — MySQL-specific guidance within the Aurora User Guide, including supported versions, parameter groups, and engine-specific features.

For comprehensive details on metrics, data availability, templates, specifications, usage guidelines, and other key information, refer to the resources listed below.

Technical Details

Behavior & Usage

Notes

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