How should secrets be handled for Batch vs streaming?

When it comes to handling secrets in both batch and streaming data processing, it's essential to consider the underlying architecture and nature of the workload. Secrets, such as API keys, database credentials, and other sensitive configurations, require different handling approaches based on whether the data is processed in batches or in real-time streams.

Handling Secrets in Batch Processing

In batch processing, data is collected, stored, and processed at intervals. Secrets can be managed by using encrypted storage solutions or secret management tools (e.g., AWS Secrets Manager, Azure Key Vault, HashiCorp Vault). Since batch jobs run at scheduled times, you can load secrets at the beginning of the run or pass them as environment variables.

Handling Secrets in Streaming Processing

In streaming processing, data is processed in real time, necessitating a more dynamic approach to secret management. Using dynamic secret management tools or services is preferred, allowing secrets to be frequently rotated and accessed securely on-the-fly. Additionally, utilizing service accounts and role-based access control can secure streams consuming sensitive information.

Example Implementation

<?php // Example: Loading secrets for a batch job $dbUsername = getenv('DB_USERNAME'); $dbPassword = getenv('DB_PASSWORD'); // Database connection $conn = new PDO('mysql:host=localhost;dbname=testdb', $dbUsername, $dbPassword); ?> <?php // Example: Loading secrets for a streaming job $secretsManager = new SecretsManager(); $secret = $secretsManager->getSecret('your-secret-id'); // Use $secret->username and $secret->password in your streaming application ?>

devops secrets management batch processing streaming processing API keys secure access

Related Questions

How do I avoid rehashing overhead with std::set in multithreaded code?

How do I find elements with custom comparators with std::set for embedded targets?

How do I erase elements while iterating with std::set for embedded targets?

How do I provide stable iteration order with std::unordered_map for large datasets?

How do I reserve capacity ahead of time with std::unordered_map for large datasets?

How do I erase elements while iterating with std::unordered_map in multithreaded code?

How do I provide stable iteration order with std::map for embedded targets?

How do I provide stable iteration order with std::map in multithreaded code?

How do I avoid rehashing overhead with std::map in performance-sensitive code?

How do I merge two containers efficiently with std::map for embedded targets?