Ensuring Database Consistency with Automated Validation: A Guide with New Relic

In data engineering, maintaining database consistency is critical for accurate and reliable insights. Even small inconsistencies between databases can lead to significant issues for data teams. Automating database consistency checks not only minimizes errors but also reduces the need for manual intervention.

This guide covers how to set up automated consistency validation between MySQL and Snowflake using Python and New Relic. By securely connecting to databases, retrieving row counts, and sending results to New Relic for monitoring, you can ensure that your data remains consistent and accurate in real-time.

In Part 1, we’ll cover:

• Why database consistency is essential.

• Setting up Python for database validation.

• Securely connecting to MySQL and Snowflake.

  
  

In Part 2, we’ll cover:

• Automating row count validation to check for data consistency.

• Logging results in New Relic.

• Setting up alerts and dashboards in New Relic for real-time insights.

Why Database Consistency Matters

Database consistency ensures that data remains accurate and reliable across different systems, making it critical for reporting and analytics. This process is especially important in complex data environments, where automated validation can detect discrepancies that might otherwise go unnoticed.

Setting Up Python for Database Validation

Before writing the script, ensure you have the following libraries installed:

pip install newrelic mysql-connector-python snowflake-connector-python 

sshtunnel cryptography

Connecting to Snowflake and MySQL

Securely connecting to databases is an essential first step. We’ll use SSH tunneling to connect to MySQL and private key authentication for Snowflake.

Snowflake Connection

To connect securely to Snowflake, we use its Python connector along with a private key for authentication. This method requires configuring Snowflake credentials, including account, user, and the private_key_path, in your script.

import snowflake.connector
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import serialization


with open(PRIVATE_KEY_PATH, 'rb') as key_file:
    private_key = serialization.load_pem_private_key(
        key_file.read(),
        password=PRIVATE_KEY_PASSPHRASE.encode(),
        backend=default_backend()
    )

MySQL Connection

For MySQL, we use SSHTunnel to establish a secure tunnel, which keeps our data transfer secure. Configure the SSH and MySQL credentials to enable the connection.

from sshtunnel import SSHTunnelForwarder
import mysql.connector


with SSHTunnelForwarder(
        (ssh_host, ssh_port),
        ssh_username=ssh_user,
        ssh_password=ssh_password,
        remote_bind_address=remote_bind_address
) as tunnel:
    conn = mysql.connector.connect(
        host='127.0.0.1',
        port=tunnel.local_bind_port,
        database=db_name,
        user=db_user,
        password=db_password
    )

With the connections established, we’re now ready to move to the core of the validation process. In Part 2, we’ll implement the row count validation, log the results in New Relic, and set up alerts and dashboards for proactive monitoring of database consistency.