Data & BI
Data engineering best practices and BI from Jutomate’s team and community. You’ll find working examples for Airflow, Amazon Redshift, Athena and BigQuery, fixes for the errors these tools raise, and notes on cutting cost. They start from how we approach data work: reliable data and shared definitions come before any dashboard.
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Amazon Redshift SQL Cheat Sheet: Tables, Views, Users and Practical Commands
Commands for finding Redshift tables and views, getting table definitions, listing users, loading data with COPY and checking query history and plans.
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How to Fix Athena “Query Exhausted Resources at This Scale Factor”
Amazon Athena returns “Query exhausted resources at this scale factor” when a query needs more resources than its execution cluster can provide. Here is how to find the operation behind…
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Presto on AWS Explained: Amazon EMR, Athena and Trino
Presto, Trino and Athena share a history, which makes older guides confusing. Here is how Presto on Amazon EMR compares with Athena, and what to check before choosing an engine.
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80% Cost Reduction in Google Cloud BigQuery
Google Cloud BigQuery costs can be reduced by limiting unnecessary data scans, avoiding repeated calculations and setting query quotas to control consumption. At Investing.com, introducing quotas reduced monthly spending from…
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How to Fix Airflow InvalidToken Errors: Fernet Key Troubleshooting
Airflow raises InvalidToken when it can’t decrypt a stored connection password or Variable. How to find which Fernet key is in play, recover it and rotate keys without losing metadata.
