Medallion.Silver

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Overview:

The Silver Layer, also referred to as the ‘Staging’ SQL database, is where data undergoes further refinement and transformation. This layer focuses on structuring the data into well-defined snapshots, adhering to a common data model. The goal is to create standardized and consistent data sets that are ready for analytical purposes and easy integration into the Gold Layer.

Key Characteristics:

  1. Data Transformation and Structuring:
    • SELECT Statements: Data is transformed and structured using SELECT query statements. These queries are designed to clean, join, aggregate, and reshape the raw data into meaningful snapshots.
    • Common Data Model: The Silver Layer follows a common data model, ensuring that table formats and structures are standardised across the data warehouse. This standardisation facilitates easier querying and analysis.
  2. Organisation:
    • Data Marts: The data is organised into data marts, which are subsets of the data warehouse tailored to specific business functions or departments. Each data mart contains only the data relevant to its particular area, optimised for performance and usability.
    • Data Views: Data views are created to provide logical representations of the data, offering a simplified and user-friendly way to access complex data structures. These views abstract the underlying complexity and present the data in a coherent and consistent manner.
  3. Version Control and Development:
    • VSCode and DevOps Integration: The SELECT statements are developed and maintained in Visual Studio Code (VSCode). Upon saving, these scripts are transferred to a DevOps pipeline, ensuring that the latest version of each query is always maintained.
    • Silver Container: The latest versions of the SQL SELECT statements are stored in the Silver Container, providing a centralised and version-controlled repository for all transformation scripts.
  4. Daily Execution and Snapshot Creation:
    • Scheduled Execution: Each morning, the SQL SELECT statements are executed to create snapshot tables. This process ensures that the data is up-to-date and reflects the latest available information.
    • Snapshot Tables in Parquet: The resulting snapshots are stored in Parquet format, maintaining the efficiency and performance benefits of this columnar storage format. These Parquet files are then moved to the Gold Layer for final use.
  5. Historical Data Access:
    • Stored Procedure (get.myhistory): The get.myhistory stored procedure allows users to access a time-based series of weekly snapshots. This procedure provides historical views of the data, enabling trend analysis and historical reporting.

Workflow Summary:

  1. Query Development: SELECT statements are written in VSCode.
  2. Version Control: Queries are saved and transferred to DevOps for version control.
  3. Daily Execution: Queries are executed each morning to create snapshot tables.
  4. Snapshot Storage: Snapshots are stored in Parquet format and moved to the Gold Layer.
  5. Historical Data Access: The get.myhistory procedure provides access to historical snapshots for time-based analysis.

Importance:

  • Data Consistency and Quality: The Silver Layer ensures that data is cleaned, standardized, and structured according to a common data model, enhancing data quality and consistency.
  • Preparation for Analysis: By organizing data into data marts and views, the Silver Layer makes it easier for analysts and business users to access and analyze the data.
  • Automated and Versioned Processes: The integration with DevOps and the automated execution of queries ensure that data transformations are reliable, reproducible, and version-controlled.
  • Historical Analysis: The weekly snapshots and the get.myhistory procedure provide robust support for historical data analysis, enabling the organization to track changes and trends over time.

RAID

Risks:

  • Data Consistency: Ensuring data consistency and accuracy in the staging SQL database is vital for reliable analysis.
  • Query Performance: Poorly optimized queries can lead to slow performance and timeouts.
  • Version Control: Maintaining version control and tracking changes to SQL queries is essential to avoid discrepancies.

Issues:

  • Schema Changes: Managing schema changes and ensuring backward compatibility can be challenging.
  • Snapshot Maintenance: Regularly maintaining and updating snapshots requires careful planning and execution.
  • Data Model Standardization: Ensuring all tables follow the common data model standard can be complex.

Dependencies:

  • Database Management Systems: Dependence on a robust SQL database management system.
  • Data Schema: Consistent and well-defined data schema for organizing data into tables and views.
  • Version Control: Effective version control mechanisms for SQL scripts and schema changes.

Assumptions:

  • Schema Stability: Assumption that the data schema remains stable with minimal changes.
  • SQL Script Accuracy: Assumption that SQL scripts are accurately written and executed.
  • Data Update Frequency: Assumption that weekly data snapshots are sufficient for analysis needs.

Opportunities:

  • Advanced Query Optimization: Using advanced query optimization techniques to improve performance.
  • Data Standardization: Ensuring data standardization across tables to facilitate easier analysis.
  • Historical Data Analysis: Utilizing historical snapshots for trend analysis and forecasting.

Mitigations:

  • Schema Management: Implementing version control and change management processes for schema changes.
  • Snapshot Automation: Automating the process of creating and maintaining snapshots.
  • Data Quality Checks: Regularly performing data quality checks to ensure accuracy and consistency.

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