Unity Catalog: A Game Changer in Data Governance
Data governance is probably not the first word to come to mind when you typically hear of Databricks. However, that is about to change once you read how the introduction of Unity Catalog in Databricks has wholly altered the Data Governance playing field. Let’s delve into the details.
To underscore the extensive importance of Unity Catalog’s introduction in the Databricks platform, let’s first revisit some of the challenges we have had in data governance over in recent years.
So, here are some common data governance challenges:
1. Tasks Assignment Framework: In an organisation, each team member is usually assigned a role, and with these roles will come needed permissions to access the data and accomplish the tasks assigned to them; now, setting the correct permissions and ensuring the clearly defined roles and responsibilities are meted out to the right individuals can be a time-consuming and arduous task, especially for larger companies.
2. Data Privacy and Access: As discussed in the previous bullet point, assigning tasks to different positions can be a challenging process. Ensuring team members have access to the correct data to perform their roles while also adhering to data privacy concerns and confirming the proper restrictions are put in place can sometimes be a very tight rope to walk.
3. Workplace Collaboration Culture: A company with an excellent collaborative culture is one of the aims of data governance. However, bringing members of different teams and departments on the same page is often tricky, as they sometimes have other goals and objectives. Still, it is necessary for there to be cross-collaboration to ensure proper communication between teams and avoid situations like having siloed data or segmented data marts instead of an integrated Enterprise Data Warehouse in an organisation.
4. Bad Data Management: Ideally, Data should be appropriately managed throughout its lifecycle. Otherwise, it could lead to varying degrees of problems, especially when it needs to be used for analysis. Effective Data Governance strives to ensure the right policies and steps are taken throughout the data lifecycle, from its generation to the archival or deletion phase. However, that is only sometimes the case. In worst-case scenarios, Data is sometimes analysed without the proper data lineage or metadata context present, resulting in problems in its downstream use cases.
Now that we understand some of the past challenges that made data governance harder to implement. Let’s see precisely how the introduction of Unity Catalog alleviates these problems.
Ways Unity Catalog has eased Data Governance
Collaborative Workspace Sharing: Databricks provides a collaborative platform where Data engineers, Data scientists, and analysts can work together. This facilitates knowledge sharing and collaborative data exploration, enhancing the overall data discovery process. Unity Catalog helps with access permission for delta sharing, which is an integral part of sharing with users outside the organisation and in a different Databricks workspace as long as the Databricks workspace you are communicating with is connected to a Unity Catalog-enabled Metastore. Without Unity Catalog, sharing is more complicated as workspaces are closed off, and you would have to go through the convoluted permissioning process imposed by IAM policies and other data control processes.
Data access control: Unity Catalog eases the control of authorising data access to user accounts by applying the access control policies set up by the account administrators. It uses a standard SQL-based security model or the data explorer in Databricks to grant permissions tables and views; it dramatically reduces the operational overhead of data governance within the work environment because the permission granted cuts across all workspaces. This saves a ton of time and is highly effective.
Data access audit: Unity Catalog helps the data audit process by monitoring who accesses, modifies, or deletes data within the workspace. This is exceptionally crucial for security and compliance purposes, and since Unity Catalog captures user-level audit logs on actions performed against the metastore, this makes the audit and compliance check for data access seamless.
Data discovery: Unity Catalog Tags and document data assets for easy access. It facilitates data discovery by providing a centralised metadata layer, which means all your databases, tables, and views can be shared across all Databricks workspaces. This means a centralised repository of metadata where users can explore available datasets, understand their structure and identify relevant data for analysis.
Data Lineage: Databricks can help users trace the lineage of data, providing insights into how data is generated, transformed, and consumed across different stages of the data pipeline. This is crucial for understanding the flow and impact of data within an organisation. This refers to tracking the flow of data from its origin to its destination, including all the transformations and processes it undergoes. This feature has an unbelievable impact on data audit and data governance purposes.
Conclusion and Summary
To conclude, Databrick has introduced Unity Catalog, a unified governance solution for data and AI assets on the Databricks Lakehouse Architecture. which is the first of its kind and has transformed Data Governance by solving many of the essential problems facing it today, as we have seen in the examples above. The distinct feature of Unity Catalog is its architecture, which was intentionally made to make data governance easier and allow organisations to focus on their core business functions without the headache of implementing effective data governance.
