Streamlining Data Analysis: Creating Views and Performing UAT Checks in BigQuery

BigQuery, Google's powerful cloud data warehouse, offers functionalities beyond simply storing and querying data. This article explores creating views and performing User Acceptance Testing (UAT) checks within BigQuery, empowering you to streamline data analysis and ensure data quality.

Creating Views for Efficient Data Access:

Views serve as virtual tables that reference underlying tables in BigQuery. They offer several benefits:

• Simplified Queries: Views allow you to define complex queries with joins, aggregations, and filters, providing a simplified interface for users who don't need to understand the underlying table structure.
• Data Security: Views can restrict access to specific data columns, enhancing data security by limiting what users can see in the underlying tables.
• Performance Optimization: For frequently used queries, views can pre-aggregate data, potentially improving query performance when compared to querying the underlying tables directly.

Steps to Create a View in BigQuery:

1. Compose Your Query: Begin by writing the SQL query that defines the data you want the view to access. This query can involve joins, filters, and aggregations as needed.

2. Choose "Save View" Option: After crafting your query, locate the "Save" dropdown menu above the query results. Select "Save View" from the dropdown options.

3. Specify View Details: In the "Save view" dialog, provide a descriptive name for your view and choose the dataset where you want to store it. Ensure the dataset exists before saving the view.

4. Save and Use the View: Once you've specified the name and dataset, click "Save" to create the view. You can then use the view name in subsequent queries as if it were an actual table.

Performing UAT Checks for Data Quality:

UAT (User Acceptance Testing) helps ensure your data meets the defined quality standards. Here's how BigQuery empowers you to perform UAT checks:

• Data Completeness: Verify if all expected data is present in your tables. Use queries with functions like COUNT or IFNULL to identify missing values or rows.
• Data Accuracy: Validate if data values are accurate and consistent with expectations. Utilize comparison queries or data profiling tools within BigQuery to identify inconsistencies.
• Data Consistency: Ensure data adheres to defined formats and data types. Implement schema validation checks and utilize functions like CAST to ensure data consistency.
• Data Lineage: Trace the origin of data and understand transformations applied. Leverage BigQuery's information schema to explore table creation timestamps and modification history.

UAT Check Examples in BigQuery:

• Checking for Missing Values:

SQL

SELECT COUNT(*) AS total_rows,
       COUNT(IFNULL(column_name, 0)) AS rows_with_value
FROM your_table;

 

This query compares the total number of rows with the number of rows where a specific column has a value (not null). A significant difference indicates potentially missing values.

• Validating Data Types:

SQL

SELECT * FROM your_table
WHERE CAST(column_name AS FLOAT64) IS NULL;

 

This query checks if any values in a specific column (intended to be a number) cannot be cast to a floating-point data type, potentially indicating incorrect data types.

Remember:

• Utilize Descriptive View Names: Choose clear and concise names for your views to enhance understanding for users querying the data.
• Schedule Regular UAT Checks: Integrate UAT checks into your data pipeline to proactively identify and address data quality issues.
• Document Your UAT Process: Maintain clear documentation outlining the specific checks performed and the criteria used for data quality evaluation.

By effectively utilizing views and UAT checks in BigQuery, you can streamline data access for analysts, promote data quality, and ultimately ensure your data serves your business needs effectively. Remember, data quality is paramount for data-driven decision making, and BigQuery provides the tools to empower you in achieving this crucial objective.