How can you secure your star schema?

1 Identify and classify your data

The first step to secure your star schema is to identify and classify your data according to its sensitivity, value, and risk. For example, you may have some data that is public, some that is internal, and some that is confidential or regulated. You should also consider the source, purpose, and usage of your data, as well as the legal and ethical implications of its exposure. By classifying your data, you can define the appropriate level of security and access control for each table or column in your star schema.

2 Apply encryption and masking

The second step to secure your star schema is to apply encryption and masking to your data, especially for the sensitive or confidential ones. Encryption is the process of transforming your data into an unreadable form using a key, so that only authorized users can decrypt it. Masking is the process of replacing or hiding your data with dummy or random values, so that it cannot be identified or misused. You can use encryption and masking at different stages of your data pipeline, such as during extraction, transformation, loading, or querying. You can also use different methods and algorithms depending on your needs and preferences.

3 Implement authentication and authorization

The third step to secure your star schema is to implement authentication and authorization for your data warehouse users and applications. Authentication is the process of verifying the identity of a user or an application, such as by using a username and password, a token, or a certificate. Authorization is the process of granting or denying access to specific data or functions, such as by using roles, permissions, or policies. You should use a centralized and secure mechanism to manage your authentication and authorization, such as a directory service, a single sign-on system, or a role-based access control system.

4 Monitor and audit your data activities

The fourth step to secure your star schema is to monitor and audit your data activities, such as data loading, querying, updating, or deleting. Monitoring is the process of collecting and analyzing data metrics, such as performance, errors, usage, or anomalies. Auditing is the process of recording and reviewing data events, such as who, what, when, where, and how. You should use a robust and reliable tool to monitor and audit your data activities, such as a logging service, a dashboard, or a report. You should also define and enforce your data governance rules and standards, such as data quality, accuracy, consistency, or compliance.

6 Update and patch your data systems

The sixth and final step to secure your star schema is to update and patch your data systems regularly and properly. Update is the process of installing and applying the latest versions and features of your data systems, such as your data warehouse software, your operating system, or your drivers. Patch is the process of fixing and resolving the known issues and vulnerabilities of your data systems, such as bugs, errors, or security flaws. You should use a secure and automated tool to update and patch your data systems, such as a manager, a agent, or a scanner. You should also review and test your update and patch processes, such as by using a checklist, a feedback, or a rollback.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?