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PostgreSQL is one of today's most popular databases, and for good reason. As a robust transactional (OLTP) database, it excels at application-driven tasks like creating, updating, and finding individual rows of data. However, many developers and analysts hit a performance wall when they try using Postgres for analytics. This video explores why running complex analytical queries in Postgres can be painfully slow and why a different approach is often necessary for efficient data analysis.

We'll break down the fundamental reasons behind this performance bottleneck. You'll learn why a system optimized for row-level operations struggles with large-scale aggregations, such as calculating total revenue across an entire dataset. If you've ever wondered "Why is Postgres slow for analytics?" or searched for ways to improve Postgres performance for your data warehouse needs, this is the perfect starting point. We explain why a separate strategy for storing and processing data is crucial for unlocking fast, efficient insights from your analytical workloads.