---
id: arcticdb
version: "6.22.0"
license: Business Source License 1.1 (See LICENSE.txt)
license_treatment: unclear
maintenance: active
---

# arcticdb — ArcticDB DataFrame Database

License: unclear · Maintenance: active · Downloads: 252.2K/mo

## What it is and what it does

ArcticDB is a Python-native database designed to store and query large Pandas DataFrames efficiently, backed by S3, LMDB, or Azure Blob Storage. It combines a Pandas-like API with a C++ data-processing engine to handle time-series data at scale, supporting versioning, snapshots, and schemaless updates without corrupting existing data. The package depends on pandas, numpy, attrs, protobuf, msgpack, pyyaml, packaging, and pytz.

It is built for scenarios where you need to persist and retrieve DataFrames with version history, efficiently index and filter time-series records, or stream sparse data without schema constraints. Data is pulled directly from storage to the client, eliminating a central server bottleneck. However, production deployments and Database Service use require a paid commercial license; the open-source BSL license permits evaluation and non-production use only.

Use it for:

- Store and version-control multi-year financial time-series data (e.g., 20-year histories of 400,000+ securities) in a single symbol.
- Query and filter large DataFrames by time range or column values using Pandas-like syntax without loading entire datasets into memory.
- Create snapshots of DataFrame state at specific points in time and revert to previous versions for audit or analysis.
- Append and update sparse time-series data without enforcing a fixed schema across all records.
- Stream real-time data to S3 or LMDB with efficient compression and concurrent read access from multiple clients.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

ArcticDB is a serverless DataFrame database that stores and retrieves Pandas DataFrames to S3 or LMDB with a C++ compression engine, supporting time-series data, versioning, and schemaless updates.

Yes, with conditions. Install if you need a versioned, time-series-optimized DataFrame database and can use S3 or LMDB storage. The active maintenance, wide platform support (Python 3.9–3.14), and zero known vulnerabilities are strong signals. However, production deployments require a paid license from ArcticDB Limited; confirm licensing terms with info@arcticdb.io before committing to production use. Medium install friction is acceptable for the capability gained.

## Install

pip install arcticdb
uv add arcticdb
poetry add arcticdb

## Installing arcticdb

Before you install: Medium install friction due to compiled C++ components; prebuilt wheels available for Python 3.9–3.14 on Linux x86_64, Windows x86_64, and macOS arm64. Active maintenance with a release 4 days old and 2474 GitHub stars.

License in practice: Business Source License 1.1 (BSL) — source is available but production use and Database Service deployments require a paid license from ArcticDB Limited. Current version converts to Apache 2.0 on Jul 27, 2028; contact info@arcticdb.io for commercial licensing.

Quickstart:

pip install arcticdb
import arcticdb as adb
import pandas as pd
ac = adb.Arctic('lmdb:///')
lib = ac.create_library('my_lib')
lib.write('symbol', pd.DataFrame({'a': [1, 2, 3]}))
data = lib.read('symbol')

S3 or LMDB storage backend must be available; production use requires a paid license from ArcticDB Limited.

Verify before relying:

- Performance benchmarks for billion-row time-series queries and compression ratios claimed in description.
- Specific latency and throughput characteristics for sparse data storage.
- Horizontal scaling behavior across symbols in production deployments.

## Package facts

- License: Business Source License 1.1 (See LICENSE.txt) (unclear)
- Python support: unspecified
- Install friction: medium
- Maintenance: active
- Downloads: 252.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

pandas dataframe database, time series data storage, s3 dataframe backend, versioned data snapshots, columnar data compression, time-series-db, dataframe-storage, versioning

[View on SkillFed](https://skillfed.io/packages/arcticdb) · [View on PyPI](https://pypi.org/project/arcticdb/)