Industry trends

How to ensure data privacy with open source tools

Use these techniques to secure your data and containerisation to manage your data pipeline

May 25, 2023

Data privacy is a crucial concern for any data pipeline. It involves protecting sensitive data from unauthorised access, use, and disclosure.


To ensure data privacy, several techniques can be employed. These include encryption, dynamic data masking, column-level security/access control, and more.

  • Encryption:

    Encryption is converting data into an unreadable format using an algorithm so that only authorised users with the decryption key can read it. This ensures that data is protected, even if it is stolen or intercepted. Encryption may also be used in conjunction with tokenisation. Tokenisation replaces sensitive data such as credit card numbers, social security numbers, and other personally identifiable information (PII) with a randomly generated token. The token has no meaningful value and can’t be used to derive the original data.

  • Dynamic data masking:

    Dynamic data masking is a technique that allows data engineers to selectively mask sensitive data in real-time. This involves masking sensitive data with fake values or partial data. For example, a credit card number could be masked to show only the last four digits. This ensures that the data is still usable, but sensitive information is protected.

  • Column-level security/access control:

    Column-level security/access control is a technique that enables data engineers to control access to specific columns in a database or data warehouse. It ensures that only authorised users can view or change the data. This technique is useful when different users have different levels of clearance to access specific data columns.

Other techniques to ensure data privacy in data pipelines include masking, data perturbation, obfuscation, and data de-identification.

Ensuring data security when using open-source

Using open-source tools for data ingestion and processing can be a great way to ensure that data is ingested and processed quickly and efficiently. However, it is important to ensure that the system is properly managed.

Kubernetes and Helm are two technologies that have revolutionised the way modern infrastructure is managed.

Kubernetes is an open-source container orchestration platform that automates the deployment, scaling, and management of containerised applications. By using Kubernetes to manage containers, organisations can ensure that containers are properly configured and secured, and that they are running the latest security patches.

Helm is an open-source package manager for Kubernetes that makes it easier to manage, install, and upgrade Kubernetes applications. Helm packages, called charts, provide a standardised way of packaging and deploying Kubernetes applications. Helm charts can be easily shared and reused across different teams and organisations.

For small teams, these are great ways to manage the infrastructure.

However, if you were to scale manifold, it becomes imperative to go for a solution that is managed by a provider. This makes sure you have enterprise support and the ability to respond quickly and that you’re using the appropriate framework to help you deal swiftly with any potential outages.

Open source
data privacy
data pipelines
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