transformation changes in silver and bronze layer

Question

Hi ,

what transformation takes place between silver and gold layer .i.e

have loaded data in bromze layer whichn is my bronze layer and i transformed data here ... then what will happend to silver to gold layer apart from pk,fk joins and all

Answer 1

What are the primary objectives when moving data from the silver layer to the gold layer?

The main goal of transitioning data from the silver to the gold layer is to prepare data for high-level business intelligence and reporting. This involves refining the data further to ensure it is ready for consumption by end-users, analysts, and decision-makers. The gold layer should provide clean, aggregated, and easy-to-query datasets that align with business requirements.

What specific transformations and aggregations are necessary?

From the silver to the gold layer, you should focus on advanced transformations and aggregations. These may include calculating key performance indicators (KPIs), summarizing data at different levels (daily, monthly), and performing complex calculations that are not required in the silver layer. Ensure that data integrity and consistency are maintained during these transformations.

How should data be organized and structured in the gold layer?

Organize data in the gold layer using a schema that supports efficient querying and analysis. This often involves designing a star schema or snowflake schema, which includes fact tables and dimension tables. Fact tables store quantitative data for analysis, while dimension tables contain descriptive attributes related to the facts, allowing for detailed filtering and slicing of data.

What tools and methods can be used for these transformations?

Leverage tools like Azure Data Factory for orchestrating data workflows and Azure Synapse Analytics for performing data transformations and aggregations. Utilize Data Flows in Azure Data Factory to define complex data transformations visually, or use T-SQL within Azure Synapse Analytics for more control over the transformation logic.

How to ensure data quality and performance in the gold layer?

To ensure high data quality, implement data validation and cleansing steps. Use data profiling techniques to understand the data distribution and identify anomalies. For performance, optimize your queries and ensure that the data model supports efficient data retrieval. Indexing and partitioning strategies in Azure Synapse can also enhance query performance.