Best Perplexity prompts for Database Administrators

Given a SQL query with multiple joins and subqueries, analyze the query plan and provide a rewritten version that minimizes disk I/O, reduces memory allocation, and takes advantage of available indexes. Assume the query is running on a Snowflake cluster with 16 nodes, each with 128GB of RAM, and the database contains 100 million rows of data. Provide a step-by-step explanation of the optimization strategy and a comparison of the expected performance improvements.

Develop a Python script to audit data quality issues in a large dataset, including missing values, outliers, and inconsistent formatting. The dataset contains customer information, including names, addresses, and phone numbers, and is stored in a PostgreSQL database. The script should generate a report highlighting the top 10 data quality issues, along with recommendations for remediation and a statistical summary of the affected data. Use the Pandas library for data manipulation and the NumPy library for numerical computations.

Design an ETL pipeline to extract data from a set of APIs, transform the data into a standardized format, and load it into a data warehouse for analysis. The pipeline should handle errors, implement data validation, and support incremental loading. Assume the APIs provide customer interaction data, including clicks, searches, and purchases, and the data warehouse is built on top of a Snowflake cluster. Provide a detailed architecture diagram and a step-by-step explanation of the pipeline's components, including data sources, transformations, and loading mechanisms.

Develop a statistical summary to evaluate the performance of a regression model trained on a dataset of customer behavior, including metrics such as mean absolute error, mean squared error, and R-squared. The model is implemented in Python using the scikit-learn library and is trained on a sample of 10,000 customers. Compare the performance of the model to a baseline model and provide recommendations for improving the model's accuracy, including feature engineering and hyperparameter tuning. Use the Matplotlib library for visualization and the Scipy library for statistical computations.