
Hyperquery
Data notebook built for speed, visibility, and collaboration
In plain words
Hyperquery is a data notebook designed for analytics teams to build and share analyses using SQL or Python. It emphasizes speed, visibility, and team collaboration, allowing users to create shareable analytical work within a single platform. The tool is intended for data professionals and teams who need to collaborate on data-driven insights and analyses.
written from the facts on this page · September 2026
From the sources
Hyperquery is the data notebook for teams that enables you to easily build shareable analyses (in SQL or Python).
Does the same job
all alternatives →- NKNew kind of data notebook for SQL, Python, and viz2023 · hyperquery.ai · ▲12
- HAHashquery, a Python library for defining reusable analysis2024 · hashquery.dev · ▲67
Hi all, we recently open sourced the first version of Hashquery, a Python library which lets you model analytics, DRY up common logic, and execute it against a database or warehouse. We were originally rendering SQL directly for all our queries, but that spiraled out of control with more complex analysis; the SQL needed to be changed sporadically for each new database dialect (BigQuery, Redshift, Aethena, Postgres, etc etc) and the SQL fragments were very challenging to reuse (and so fragments were copy-pasted all over). ~~~ Advantages we think it has over writing SQL by hand: - Queries are…
- PTPgQueuer – Transform PostgreSQL into a Job Queue2024 · github.com · ▲376
PgQueuer is a minimalist, high-performance job queue library for Python, leveraging the robustness of PostgreSQL. Designed for simplicity and efficiency, PgQueuer uses PostgreSQL's LISTEN/NOTIFY to manage job queues effortlessly.

- BPBemiDB – Postgres read replica optimized for analytics2024 · github.com · ▲209
Hi HN! We're Evgeny and Arjun, and we’re building a better way to do analytics with Postgres. We love Postgres for its simplicity, power, and rich ecosystem. But engineers have to still get bogged down with heavyweight and expensive OLAP systems when connecting an analytics data stack. Postgres is amazing at OLTP queries, but not for OLAP queries (large data scans and aggregations). Even in this case, we’ve still heard from countless scaling startups that they still try to use only a read replica to run analytics workloads since they don’t want to deal with the data engineering complexity of…

More growth this month
the category →
AstraPixels▲267A pixel-art solar system at its real current positions.
Growth · 29d ago · astrapixels.com