Securing Data | Dagster Glossary

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Securing Data

Protect data from unauthorized access, modification, or destruction.

Securing data - a definition:

Securing data in modern data pipelines is an essential aspect to ensure data privacy and prevent data breaches. Some techniques used for securing data in data pipelines include encryption, access control, and data anonymization.

Dagster Cloud provides role-based access control (RBAC) to give you granular control over who can execute or modify which part of your data platform, which narrows down the access to the data the platform manages.

Encryption involves converting plaintext data into ciphertext using encryption algorithms to ensure confidentiality.

Example of securing data in Python:

In Python, the cryptography library can be used for encryption and decryption of data. Please note that you need to have the necessary Python libraries installed in your Python environment to run this code.

from cryptography.fernet import Fernet

# generate a secret key
key = Fernet.generate_key()

# create an instance of the Fernet class using the key
cipher_suite = Fernet(key)

# encrypt the data
message = b"Hello, world!"
encrypted_message = cipher_suite.encrypt(message)


# decrypt the data
decrypted_message = cipher_suite.decrypt(encrypted_message)


Access control involves setting up appropriate permissions and roles to ensure that only authorized personnel can access the data. This can be achieved through techniques like role-based access control (RBAC) or attribute-based access control (ABAC). In Python, access control can be implemented using libraries like Flask-Security or Django-Defender.

Data anonymization involves removing or obfuscating sensitive data from the dataset to ensure privacy while still maintaining the usefulness of the data. Techniques used for data anonymization include masking, generalization, and perturbation. In Python, data anonymization can be achieved using libraries like OpenMondrian or ARX .

Overall, securing data in modern data pipelines requires a combination of technical solutions and best practices to ensure the safety and privacy of sensitive data.

Other data engineering terms related to
Data Security and Privacy: