BigchainDB allows developers and enterprise to deploy blockchain proof-of-concepts, platforms and applications with a scalable blockchain database, supporting a wide range of industries and use cases. It is a decentralization ecosystem: a decentralized database, at scale. It can perform 1 million writes per second throughput, store petabytes of data, and sub-second latency.
 
The BigchainDB design starts with a distributed database (DB), and through a set of innovations adds blockchain characteristics: decentralized control, immutability, and creation & movement of digital assets. BigchainDB inherits characteristics of modern distributed databases: linear scaling in throughput and capacity with the number of nodes, a full-featured NoSQL query language, efficient querying, and permissioning. Being built on an existing distributed DB, it also inherits enterprise-hardened code for most of its codebase.

BigchainDB allows developers and enterprise to deploy blockchain proof-of-concepts, platforms and applications with a scalable blockchain database, supporting a wide range of industries and use cases.  It is a decentralization ecosystem: a decentralized database, at scale. It can perform 1 million writes per second throughput, store petabytes of data, and sub-second latency.

BigchainDB - The Scalable Blockchain Database

Graphite is an enterprise-scale monitoring tool that runs well on cheap hardware. It is a highly scalable real-time graphing system. It stores numeric time-series data and renders graphs of this data on demand. 

Graphite - A highly scalable real-time graphing system

TinyDB is a lightweight document oriented database optimized for your happiness. It's written in pure Python and has no external dependencies. The target are small apps that would be blown away by a SQL-DB or an external database server. TinyDB neither needs an external server nor any dependencies from PyPI. You can easily extend TinyDB by writing new storages or modify the behaviour of storages with Middlewares.

TinyDB - Lightweight Document Oriented Database

ZODB provides an object-oriented database for Python. It does not require separate language for database operations. It has very little impact on your code to make objects persistent. It also does not require database mapper as using an object-relational mapping is not like using an object database. Its feature include ACID support, Pluggable layered storage, Caching, Binary large objects (Blobs) and lot more.

ZODB - A Python object-oriented database

Gnocchi is an open-source |time series| database. The problem that Gnocchi solves is the storage and indexing of |time series| data and resources at a large scale. This is useful in modern cloud platforms which are not only huge but also are dynamic and potentially multi-tenant. Gnocchi takes all of that into account. Gnocchi has been designed to handle large amounts of aggregates being stored while being performant, scalable and fault-tolerant. While doing this, the goal was to be sure to not build any hard dependency on any complex storage system.
 
Gnocchi takes a unique approach to time series storage: rather than storing raw data points, it aggregates them before storing them. This built-in feature is different from most other time series databases, which usually support this mechanism as an option and compute aggregation (average, minimum, etc.) at query time.

Gnocchi is an open-source |time series| database. The problem that Gnocchi solves is the storage and indexing of |time series| data and resources at a large scale. This is useful in modern cloud platforms which are not only huge but also are dynamic and potentially multi-tenant. Gnocchi takes all of that into account. Gnocchi has been designed to handle large amounts of aggregates being stored while being performant, scalable and fault-tolerant. While doing this, the goal was to be sure to not build any hard dependency on any complex storage system.

Gnocchi - Time series database

AI Tool for querying natural language on tabular data.Built using QA models from transformers.
 
Features: 
<ul>
<li>Supports detection from multiple csvs (csvs can also be read from Amazon s3)</li>
<li>Supports FuzzyString implementation. i.e, incomplete column values in query can be automatically detected and filled in the query.</li>
<li>Supports Databases - SQLite, Postgresql, MySQL, Amazon RDS (Postgresql, MySQL).</li>
<li>Open-Domain, No training required.</li>
<li>Add manual schema for customized experience</li>
<li>Auto-generate schemas in case schema not provided</li>
<li>Data visualisations.</li>
</ul>

AI Tool for querying natural language on tabular data.Built using QA models from transformers.

TableQA - AI Tool for querying natural language on tabular data. 

CodernityDB is pure python (no 3rd party dependency), fast multi-platform, schema-less, NoSQL database. It has optional support for HTTP server version (CodernityDB-HTTP), and also Python client library (CodernityDB-PyClient) that aims to be 100% compatible with embeded version. It is an advanced key-value database, with multiple key-values indexes in the same engine. It has support for Multiple indexes, Custom storage, Sharding.

CodernityDB - Pure python, NoSQL, Fast Database

pickleDB is a lightweight and simple key-value store. It is built upon Python's simplejson module and was inspired by redis.

pickleDB - Key-value store using Python's simplejson module

Discover open source projects across all platforms

Projects