For a farming company with a sensor data table updated every 30 seconds, the goal is to minimize costs while facilitating weekly analytical queries. The best data model will effectively manage data storage, update frequency, and query performance.
Partitioned Metrics Table:
Creating a metrics table partitioned by timestamp optimizes query performance and storage costs.
Partitioning by timestamp allows for efficient querying, especially for time-based analyses.
Sensor ID Reference:
Including a sensor_id column in the metrics table that points to the id column in the sensors table ensures data normalization.
This structure avoids redundancy and maintains a clear relationship between sensors and their metrics.
Using INSERT Statements:
Using INSERT statements to append new metrics every 30 seconds is efficient and cost-effective.
INSERT operations are more suitable than UPDATE operations for adding new data entries, especially at high frequencies.
Joining Tables for Analysis:
When running analytical queries, joining the partitioned metrics table with the sensors table as needed provides a comprehensive view of the data.
This approach leverages BigQuery's powerful JOIN capabilities while keeping the data model normalized and efficient.
Google Data Engineer Reference:
BigQuery Partitioned Tables
BigQuery Best Practices
Efficient Data Partitioning
BigQuery Data Modeling
Using this data model, the farming company can manage its sensor data effectively, minimize costs, and perform weekly analytical queries with high efficiency.