This content originally appeared on DEV Community and was authored by Sri
- Automating Database Documentation 🔹 Problem: Keeping track of schema changes and metadata is time-consuming. 🔹 Solution: Use LLMs to generate table descriptions, column explanations, and data summaries automatically.
SQL Query:
SELECT COMPLETE('Describe the purpose of the orders table, its columns, and their relationships.');
Python (Snowpark):
from snowflake.snowpark.functions import complete
query = "SELECT COMPLETE('Describe the purpose of the customers table.')"
df = session.sql(query).collect()
print(df)
📌 Business Impact: Saves hours of manual documentation effort.
- Log Analysis & Anomaly Detection 🔹 Problem: Large volumes of database logs make it difficult to identify anomalies. 🔹 Solution: Use EXTRACT_ANSWER to summarize error logs and highlight potential issues.
SELECT EXTRACT_ANSWER('Identify the most critical errors from the past 24 hours in the Snowflake query logs.');
Python (Snowpark):
query = "SELECT EXTRACT_ANSWER('Summarize critical database errors from yesterday.')"
df = session.sql(query).collect()
print(df)
📌 Business Impact: Faster incident response and proactive issue resolution.
- Sentiment Analysis on User Queries 🔹 Problem: Database teams receive a high volume of SQL query complaints from users. 🔹 Solution: Use SENTIMENT function to categorize feedback into positive, neutral, or negative.
SELECT SENTIMENT('Users are complaining that the database queries are too slow.');
Expected Output:
Text Sentiment Score
"Users are complaining that the database queries are too slow." -0.85
📌 Business Impact: Prioritizing database performance improvements based on user sentiment.
- Generating SQL Queries from Natural Language 🔹 Problem: Non-technical users struggle to write SQL queries. 🔹 Solution: Use LLMs to convert plain English into SQL automatically.
SQL Query:
SELECT COMPLETE('Write an SQL query to get the top 5 highest revenue customers from the sales table.');
📌 Business Impact: Reduces dependency on DBAs for query writing, enabling self-service analytics.
- Summarizing Large Reports 🔹 Problem: Reading long compliance or audit reports is time-consuming. 🔹 Solution: Use SUMMARIZE function to extract key insights quickly.
SQL Query:
SELECT SUMMARIZE('Summarize this 50-page compliance report.');
📌 Business Impact: Saves time on compliance reviews and regulatory reporting.
- Multi-Language Query Support 🔹 Problem: Global teams require database queries in different languages. 🔹 Solution: Use TRANSLATE function to convert database messages and queries into multiple languages.
SELECT TRANSLATE('Retrieve customer purchase history', 'en', 'es');
📌 Business Impact: Improves collaboration in multinational teams.
🔹 Final Thoughts for DBA Managers
✅ Security Best Practices: Always enable role-based access (CORTEX_USER) and mask sensitive data.
✅ Cost Optimization: Track token consumption using query logs.
✅ Business Efficiency: Automate documentation, reporting, and user query handling.
This content originally appeared on DEV Community and was authored by Sri
Sri | Sciencx (2025-02-09T20:00:48+00:00) Real-World DBA Use Cases for Snowflake LLMs. Retrieved from https://www.scien.cx/2025/02/09/real-world-dba-use-cases-for-snowflake-llms/
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