Usually databases are treated primarily as fairly dumb data storage systems, but they can be capable of much more. Case in point the PostgreSQL database and its – Ada-based – PL/pgSQL programming language, which allows you to perform significantly more complex operations than would be realistically possible with raw SQL. Case in point the implementation of a Kalman Filter by the folk over at Traconiq, which thus removes the necessity for an external filtering pipeline. Using a Kalman Filter is highly desirable when you’re doing something like vehicle tracking using both dead-reckoning and GPS coordinates, as it filters out noise that can be the result of e.g. GPS reception issues.
This video course introduces DuckDB, an open-source database for data analytics in Python. It covers creating databases from files (Parquet, CSV, JSON), querying with SQL and the Python API, concurrent access, and integration with pandas and Polars.
This tutorial provides a comprehensive guide to Apache Doris, a high-performance, real-time analytical database. It covers installation, basic operations, data loading, querying, and performance optimization.
An in-process analytics database, DuckDB can work with surprisingly large data sets without having to maintain a distributed multiserver system. Best of all? You can analyze data directly from your Python app.
pg_timeseries is an open-source PostgreSQL extension focused on creating a cohesive user experience around the creation, maintenance, and use of time-series tables. It allows users to create time-series tables, configure the compression and retention of older data, monitor time-series partitions, and run complex time-series analytics functions with a user-friendly syntax.
Catena uses a Blockchain based on SHA-256 hashes for proof of work, with configurable difficulty. Blocks contain transactions which contain SQL statements. Catena is written from scratch and is therefore completely different from Bitcoin, Ethereum etc.
