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Now that we have our data arranged into more standardized and integrated datasets, it's time for the final push into the Gold Layer, also known as the Curated Layer, which is in most cases the last leg of our data transformation journey.
In the Gold Layer we expect the data to be ready for consumption for the business users and downstream systems. Thus, the consumers of this data can just plug into the data and expect row level security, data masking, data aggregation, etc. to be incorporated.
Some of the things we try to achieve within the Gold layer are
- Consumer Specific Data Models
One of the ways of getting the data ready for consumption is molding it into a data model that allows for quick and efficient access while being scalable as well. This can be achieved by using Azure Synapse Dedicated Pools, Databricks SQL or Power BI Data marts, depending on your use cases. - Enforce Quality
We have introduced some standardizations on the Silver Layer already, but it's important to remember that the Gold layer will be the version of truth that is exposed to customer dashboards and other downstream reporting systems. So, we want to make sure that the data is consumable and clean, not just in terms of the data quality of specific fields but in terms of the entire data presented. We can do things like adding filters to reduce the amount of data being pushed forward if it does not benefit that consumer's use cases and we can aggregate data where applicable too. - Security
Since the Gold Layer will be the go-to place for most of the organization to get their data, it's a good idea to focus on the security of this data too. Things like Row Level Security and Data Masking are often used here in addition to the normal Role Based Access Control mechanisms.
The above steps can be achieved using Azure Databricks and/or Azure Synapse and/or Power BI. A visual representation of the ask is shown below.
Now that we know what we need to do, it’s important to understand why we are doing this.
From an organizational standpoint, the Gold layer serves one main purpose:
- Consumption
The Gold layer is optimized for analytics, rather than data ingestion or processing. This layer contains curated data often in de-normalized data marts or star schemas. By the time we get to this layer, the data has been transformed into high-value data products that are served to your data consumers. All the data assets in this zone are typically highly governed and well-documented.
To complete this challenge successfully, you should be able to:
- Validate that all the data has been transformed and has been loaded into the Gold layer in the appropriate data models using the points in the discussion above.
- Showcase row level security within the data. It is up to the team to decide on how this will be applied in regards to the table(s) and how it is implemented amongst users.
- Showcase column level security / data masking within the data. It is up to the team to decide on how this will be applied with regards to the table/columns and how it is implemented amongst users.
The following links may be useful to achieving the success criteria listed above.
- What is dedicated SQL pool?
- Introduction to datamarts
- What is a Databricks SQL warehouse?
- Azure Synapse Analytics - Dynamic Data Masking
- Azure Synapse Analytics - Row-Level Security
- Databricks row and column level security
- Power BI - Control access to datamarts
- Brainstorm and discover a quick and easy way to make use of pre-made data models to help support this challenge.
- Brainstorm and find a quicker way to access the Silver Layer data in-place without pushing to a physical model-based Gold Layer.