A deep dive into running analytics on Postgres, from basic optimizations to advanced techniques and knowing when to quit.

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Running large-scale analytics workloads on Postgres is feasible with the right optimizations and architectural decisions. Analytics queries require different strategies compared to transactional queries, such as using dedicated analytics replicas, proper schema design, materialized views, table partitioning, and vertical partitioning. Postgres extensions for columnar storage can provide significant performance boosts for large datasets. Monitoring and fine-tuning the database settings are essential to handle complex analytical operations efficiently.

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