Best of Data Analysis — October 2023

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   Article

   TigerData (Creators of TimescaleDB)·3y

   Real-time Analytics in Postgres: Why It's Hard (and How to Solve It)

   Enabling real-time data analytics is a key capability of modern applications, but PostgreSQL materialized views have limitations, such as inefficient refreshes, lack of automatic refreshes, and not showing up-to-date results. Timescale's continuous aggregates solve these limitations by providing automatic and efficient refreshes, as well as combining materialized data with raw data. Continuous aggregates are suitable for real-time analytics, live dashboards, reporting, and more.

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   Article

   Community Picks·3y

   Analyzing Data 180,000x Faster with Rust

   This article describes how a Rust program was optimized to analyze data 180,000 times faster compared to a Python program. It explains the k-CorrSet problem and the various optimizations made to the Rust program. The final speedup achieved was approximately 180,000 times faster than the Python program.

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3. 3
   Article

   KDnuggets·3y

   Why SQL is THE Language to Learn for Data Science

   SQL is the essential language for data science due to its universal database accessibility, efficient data cleaning capabilities, seamless integration with other languages, and requirement for most data science jobs.

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