ArcticDB

API & Backend-as-a-Service Platforms

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A client-side, serverless database engineered to accelerate quantitative data science by delivering ultrafast access to petabyte-scale time-series data, with billions of rows and hundreds of thousands of columns available in seconds, all without managing dedicated servers. Key features include an instantly queryable data format, built-in versioning and time travel for safe, evolving schemas, effortless integration with the common data-science ecosystem, a familiar data-frame style API, and zero-setup, multi-user operation that supports backtesting, analytics, and modeling for researchers, traders, analysts, risk managers, and data teams working with large-scale data.

ArcticDB can be found in API & Backend-as-a-Service Platforms categories.

The most similar products to ArcticDB are:

DivParser Verified profile Promoted → Neon → MotherDuck → ClickHouse → DataSet →

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ArcticDB Main Features

• Speed
  ArcticDB processes billions of rows of data in seconds, making it very fast.
• Time Travel
  ArcticDB allows users to easily manage and modify data over time with built-in versions.
• Flexible Scaling
  You can scale your data from zero to many users without any hassle.
• Add-ons Available
  There are additional features and support options available for Pro and Enterprise users.
• Community Support
  Users can access community support and resources for help.
• No Servers Needed
  ArcticDB is a client-side database, so you don't need dedicated servers to use it.
• Open Source Ethos
  The source code is available on GitHub, allowing users to start using ArcticDB right away.
• DataFrames at Scale
  It can handle huge amounts of data, with hundreds of thousands of columns and petabytes of data.
• Familiar Python API
  It integrates easily with Python data science libraries and can be installed simply using pip.
• Research Ready Data
  It provides seamless data handling for onboarding, sharing, and production in quantitative workflows.
• Time-Series Analysis
  The query engine is optimized for analyzing time series data.
• Quantitative Data Support
  It supports large data frames, making it easy to model entire trading universes.

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