Clean GS & Pudu Data: SQL Templates & Transformations

In the realm of robotics and data analytics, standardizing data from diverse sources is crucial for generating meaningful insights. This article outlines the process of creating SQL templates to clean and transform data from Gaussian Splatting (GS) and Pudu robots, focusing on extracting useful columns and applying initial transformations to create clean views. This approach ensures consistency and facilitates effective data analysis for dashboarding and reporting.

Identifying Core Analytics Fields

The foundation of any data cleaning process lies in identifying the key fields that drive your analytics. For robotics data, several core fields are essential for understanding robot performance, operational efficiency, and task completion. These fields provide a comprehensive view of robot activities and enable in-depth analysis.

• Vendor: Distinguishing the source of the data is paramount. Whether it's 'gs' for Gaussian Splatting or 'pudu', this field allows you to segment and analyze data based on the robot manufacturer. Knowing the vendor helps in understanding the specific characteristics and capabilities of the robots being used.
• Robot Identification: The robot_id and robot_name are crucial for tracking individual robot performance. These fields allow you to monitor the activities of specific robots, identify potential issues, and optimize their operation. Each robot's unique identifier enables detailed performance analysis and targeted improvements.
• Site Information: The site_id and site_name provide context for where the robots are operating. This information is essential for understanding how environmental factors and site-specific conditions affect robot performance. Analyzing data by site can reveal opportunities for optimizing robot deployment and task execution.
• Map Details: The map_name indicates the specific map or environment in which the robot is operating. This field is important for understanding how the robot navigates and performs tasks in different environments. Analyzing data by map can help identify areas where the robot may encounter challenges and where improvements can be made.
• Task Specifications: The task_id and task_name define the specific tasks that the robot is performing. This information is crucial for understanding the robot's workload and how effectively it is completing its assigned duties. Tracking task-specific data allows for performance evaluation and optimization of task execution.
• Timestamps and Duration: The start_ts, end_ts, and duration_minutes provide a timeline of robot activities. These fields are essential for understanding the duration of tasks, identifying bottlenecks, and optimizing task scheduling. Analyzing temporal data allows for efficient resource allocation and improved task management.
• Performance Metrics: Fields such as cleaned_area_sqft, completion_rate, status, and error_code provide insights into the robot's performance and the success of its tasks. These metrics are crucial for evaluating the robot's efficiency, identifying potential issues, and implementing corrective actions. Monitoring these metrics ensures optimal robot performance and task completion.

By focusing on these core analytics fields, you can create a robust data foundation for analyzing robot performance, optimizing operations, and improving overall efficiency. Ensuring that these fields are accurately captured and transformed is essential for generating meaningful insights and driving data-informed decisions.

Crafting SQL Templates for GS Data

To streamline the data cleaning process for Gaussian Splatting (GS) robots, we can create an SQL template (sql/gs_clean_task_template.sql) that selects the most relevant columns from the GS staging tables (gs_active_tr and gs_static_tr) and applies initial transformations. This template ensures a consistent and standardized output, making it easier to analyze and compare data across different GS robots. The SQL template serves as a blueprint for extracting, transforming, and loading (ETL) data from raw sources into a clean, usable format.

Selecting from GS Staging Tables

The first step in creating the SQL template is to identify and select the most useful columns from the GS staging tables. The gs_active_tr table typically contains real-time data about the robot's current activities, while the gs_static_tr table holds static information about the robot and its configuration. By querying both tables, we can gather a comprehensive set of data for analysis.

• Example: SELECT robot_id, task_id, start_time, end_time, cleaned_area_sqm, status FROM gs_active_tr;
• Example: SELECT robot_name, site_id, map_name FROM gs_static_tr WHERE robot_id = [robot_id from gs_active_tr];

Casting Types and Normalizing Units

Once the relevant columns have been selected, the next step is to cast the data types to ensure consistency and accuracy. For example, timestamps should be cast to a standard datetime format, and numerical values should be cast to the appropriate data type (e.g., integer or float). Additionally, it's important to normalize units to a standard format. For instance, if the cleaned area is stored in square meters (sqm), it should be converted to square feet (sqft) for consistency.

• Example: CAST(start_time AS DATETIME) AS start_ts
• Example: cleaned_area_sqm * 10.764 AS cleaned_area_sqft

Standardizing Status Values

Different data sources may use different values to represent the status of a task. To ensure consistency, it's important to standardize these values to a common set of terms. For example, you might map different status codes to values like 'completed', 'failed', 'in progress', or 'pending'. This standardization makes it easier to analyze and compare data across different robots and tasks.

• Example: CASE status WHEN '1' THEN 'completed' WHEN '2' THEN 'failed' ELSE 'in progress' END AS status

Outputting a Consistent Column Set

Finally, the SQL template should output a consistent column set for GS data. This ensures that the data is structured in a predictable and standardized format, making it easier to analyze and integrate with other data sources. The column set should include all the core analytics fields identified earlier, such as vendor, robot_id, robot_name, site_id, site_name, map_name, task_id, task_name, start_ts, end_ts, duration_minutes, cleaned_area_sqft, completion_rate, status, and error_code.

By following these steps, you can create a robust SQL template for cleaning and transforming GS data, ensuring that it is accurate, consistent, and ready for analysis.

Developing SQL Templates for Pudu Data

Similar to the GS data, we can create an SQL template (sql/pudu_clean_task_template.sql) for Pudu robots. This template should mirror the structure and semantics of the GS template, ensuring that the output columns are consistent across both data sources. This consistency is crucial for creating unified dashboards and reports that provide a comprehensive view of robot operations.

The Pudu SQL template should perform the same types of transformations as the GS template, including casting types, normalizing units, and standardizing status values. However, the specific columns and table names may differ, depending on the structure of the Pudu data source. Therefore, it's important to carefully examine the Pudu data and map the relevant columns to the corresponding fields in the SQL template.

Matching Column Names and Semantics

One of the key goals of the Pudu SQL template is to match the column names and semantics of the GS template. This ensures that the data from both sources can be easily combined and analyzed. For example, if the GS template uses the column name 'cleaned_area_sqft', the Pudu template should use the same column name, even if the original column in the Pudu data source has a different name.

• Example: SELECT cleaned_area_m2 * 10.764 AS cleaned_area_sqft FROM pudu_tasks;

Handling Data Differences

While the goal is to match the column names and semantics, it's important to handle any differences in the data. For example, the Pudu data source may use different units or status codes than the GS data source. In these cases, the SQL template should perform the necessary transformations to ensure consistency.

• Example: CASE task_status WHEN '1' THEN 'completed' WHEN '2' THEN 'failed' ELSE 'in progress' END AS status

Validating the Pudu SQL Template

After creating the Pudu SQL template, it's important to validate that it is working correctly. This can be done by running the template against a sample of Pudu data and comparing the output to the expected results. Any discrepancies should be identified and corrected to ensure the accuracy and consistency of the data.

By following these steps, you can create a robust SQL template for cleaning and transforming Pudu data, ensuring that it is compatible with the GS data and ready for analysis.

Creating Vendor-Agnostic View Definitions

To further simplify the data analysis process, consider creating vendor-agnostic view definitions. These views provide a unified interface for querying data from both GS and Pudu robots, hiding the underlying differences in the data sources. This approach makes it easier to create dashboards and reports that provide a comprehensive view of robot operations, regardless of the vendor.

• Example: CREATE VIEW gs_task_clean AS (SELECT ... FROM gs_clean_task_template);
• Example: CREATE VIEW pudu_task_clean AS (SELECT ... FROM pudu_clean_task_template);

Benefits of Vendor-Agnostic Views

• Simplified Queries: Vendor-agnostic views allow you to query data from both GS and Pudu robots using a single query, without having to worry about the underlying data sources.
• Consistent Data: These views ensure that the data is consistent across both vendors, making it easier to compare and analyze the data.
• Reduced Complexity: By hiding the underlying data sources, vendor-agnostic views reduce the complexity of the data analysis process.

Documenting Transformation Rules

Finally, it's important to document all the transformation rules used in the SQL templates. This documentation serves as a reference for understanding how the data is being cleaned and transformed, and it can be used to ensure that the transformations are consistent over time. The documentation should include a description of each transformation, the rationale for the transformation, and any assumptions or limitations.

Create a docs/transformations.md file to document the following:

• Casting types
• Normalizing units
• Standardizing status values

By documenting the transformation rules, you can ensure that the data cleaning process is transparent, consistent, and maintainable.

In conclusion, cleaning and transforming data from GS and Pudu robots requires a systematic approach that involves identifying core analytics fields, creating SQL templates, and documenting transformation rules. By following the steps outlined in this article, you can create a robust data foundation for analyzing robot performance, optimizing operations, and improving overall efficiency. For further reading on data transformation best practices, check out this comprehensive guide.