# ArcticDB

ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.

Repository: https://github.com/man-group/ArcticDB
Canonical: https://ross.abutalabs.com/products/arcticdb
Homepage: http://arcticdb.io
Language: C++
License: NOASSERTION
License Family: other
Topics: big-data, data, data-analysis, data-science, database, pandas, dataframe, quantitative-analysis, quantitative-finance, quantitative-trading
Last push: 2026-08-26T15:58:43+00:00
Link (homepage): http://arcticdb.io
Link (site_page): https://docs.arcticdb.io/
Link (site_page): https://arcticdb.io/about

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 86, longevity 99
- inputs: {"age_days": 1391, "days_push": 7, "days_rel": 16, "gap_med": 7.0, "n_releases_24m": 69}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2493, forks 214 (observed 2026-08-28T04:06:56.591971+00:00)

## What it is
ArcticDB is a high-performance DataFrame database for time series and tick data, developed at Man Group for quantitative data science. It stores and retrieves Pandas DataFrames natively via a Python API, sitting directly on commodity object storage (S3, Azure Blob, GCP, LMDB) with no servers to provision.

## Use cases
- store and query billions of rows of time series data from Python
- persist pandas DataFrames with versioning and point-in-time queries
- build a tick data store for market data in quantitative finance
- backtest trading strategies against large historical datasets
- store evolving-schema dataframe data on S3 without managing a database server
- handle wide cross-sectional data with hundreds of thousands of columns

## When to choose
- you work in Python with pandas/polars and need fast dataframe storage at scale
- you want a serverless datastore on top of S3/Azure/GCP object storage
- you need immutable versioning and time-travel queries over time series
- you are doing quantitative research, backtesting, or market data storage

## When to avoid
- you need SQL queries or joins across relational tables
- you need a general-purpose OLTP database with transactions
- your stack is not Python-centric
- you need real-time streaming ingestion with sub-millisecond latency

## Facets
- artifact type: library
- maturity: active
- function: database, data-science, etl
- domain: databases, data-science, fintech, time-series
- platform: python, windows
- tags: dataframe-database, timeseries, tick-data, object-storage, pandas, quantitative-finance, serverless-database, versioned-data, linux, macos

## Member repositories
- man-group/ArcticDB (main) score 94
- man-group/arctic (mirror) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:56.591971+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:27:38.854312+00:00, confidence not recorded.
  - readme: https://github.com/man-group/ArcticDB (fetched 2026-08-28T04:06:56.591971+00:00, sha 18d29eff2305)
  - homepage: http://arcticdb.io (fetched 2026-08-29T09:42:57.543478+00:00, sha ddaef888f606)
  - site_page: https://docs.arcticdb.io/ (fetched 2026-08-29T09:42:57.546294+00:00, sha 36c6c3c2e4f9)
  - site_page: https://arcticdb.io/about (fetched 2026-08-29T09:42:57.548088+00:00, sha e3651b7c5d0f)
  - registry_pypi: https://pypi.org/pypi/arctic/json (fetched 2026-08-29T09:42:57.549819+00:00, sha 2fdffa8e0d90)
- Data as of 2026-08-30T08:39:29.467469+00:00.