Cayley is an open-source graph inspired by the graph database behind Freebase and Google's Knowledge Graph. Its goal is to be a part of the developer's toolbox where Linked Data and graph-shaped data (semantic webs, social networks, etc) in general are concerned. 
 
It supports built-in query editor and visualizer. It works well with multiple backend stores.

Cayley is an open-source graph inspired by the graph database behind Freebase and Google's Knowledge Graph. Its goal is to be a part of the developer's toolbox where Linked Data and graph-shaped data (semantic webs, social networks, etc) in general are concerned. 

Cayley - An open-source Graph Database 

Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals. It is a native Blueprints enabled graph database and as such, it supports the full TinkerPop stack of technologies.

Titan - Scalable Graph Database 

Atlas was developed by Netflix to manage dimensional time series data for near real-time operational insight. Atlas features in-memory data storage, allowing it to gather and report very large numbers of metrics, very quickly. Atlas captures operational intelligence. Whereas business intelligence is data gathered for analyzing trends over time, operational intelligence provides a picture of what is currently happening within a system.

Atlas - In-memory dimensional time series database

KairosDB is a fast distributed scalable time series database written on top of Cassandra. Data can be pushed in KairosDB via multiple protocols : Telnet, Rest, Graphite. KairosDB stores time series in Cassandra, the popular and performant NoSQL datastore. It supports aggregators which can perform an operation on data points and down samples. Standard functions like min, max, sum, count, mean etc.

Kairosdb - Fast distributed scalable time series database written on top of Cassandra

A cloud-native database for building mission-critical applications. This repository contains the Community Edition of the YugaByte Database.YugaByte offers both SQL and NoSQL in a single, unified db. It is meant to be a system-of-record/authoritative database that applications can rely on for correctness and availability. It allows applications to easily scale up and scale down in the cloud, on-premises or across hybrid environments without creating operational complexity or increasing the risk of outages.
 
YugaByte DB is world's only distributed database that is both non-relational (supports Redis-compatible key-value & Cassandra-compatible flexible schema APIs) and relational (PostgreSQL-compatible distributed SQL API) at the same time. It is purpose-built to power fast-growing online services on public, private and hybrid clouds with transactional integrity, low latency, high throughput and multi-region scalability while also providing unparalleled data modeling freedom to application architects.

A cloud-native database for building mission-critical applications. This repository contains the Community Edition of the YugaByte Database.YugaByte offers both SQL and NoSQL in a single, unified db. It is meant to be a system-of-record/authoritative database that applications can rely on for correctness and availability. It allows applications to easily scale up and scale down in the cloud, on-premises or across hybrid environments without creating operational complexity or increasing the risk of outages.

YugaByte Database - Transactional, high-performance database for building internet-scale, globally-distributed applications

Debezium is a distributed platform that turns your existing databases into event streams, so applications can see and respond immediately to each row-level change in the databases. Debezium is built on top of Apache Kafka and provides Kafka Connect compatible connectors that monitor specific database management systems. Debezium records the history of data changes in Kafka logs, from where your application consumes them. This makes it possible for your application to easily consume all of the events correctly and completely.

Debezium - Stream changes from your databases.

Apache ShardingSphere is an open-source ecosystem consisted of a set of distributed database solutions, including 3 independent products, JDBC, Proxy & Sidecar (Planning). They all provide functions of data scale-out, distributed transaction and distributed governance, applicable in a variety of situations such as Java isomorphism, heterogeneous language and cloud-native.
 
Features: 
<ul>
<li>Database sharding &amp; Table sharding</li>
<li>Replica query</li>
<li>Sharding strategy customization</li>
<li>Centre-less Distributed primary key</li>
<li>Distributed Governance</li>
<li>Data migration &amp; Scale-out</li>
<li>Observability(Tracing/Metrics) Supported</li>
<li>Data Encryption&amp;Decryption</li>
<li>Shadow Table for Performance Testing</li>
<li>Unified Transaction API</li>
</ul>

Apache ShardingSphere is an open-source ecosystem consisted of a set of distributed database solutions, including 3 independent products, JDBC, Proxy & Sidecar (Planning). They all provide functions of data scale-out, distributed transaction and distributed governance, applicable in a variety of situations such as Java isomorphism, heterogeneous language and cloud-native.

Apache ShardingSphere - Distributed Database Ecosphere 

OmniSciDB is the foundation of the OmniSci platform. OmniSciDB is SQL-based, relational, columnar and specifically developed to harness the massive parallelism of modern CPU and GPU hardware. OmniSciDB can query up to billions of rows in milliseconds, and is capable of unprecedented ingestion speeds, making it the ideal SQL engine for the era of big, high-velocity data.
 
OmniSciDB optimizes the memory and compute layers to deliver unprecedented performance. OmniSciDB was designed to keep hot data in GPU memory for the fastest access possible. Other GPU database systems have taken the approach of storing the data in CPU memory, only moving it to GPU at query time, trading the gains they receive from GPU parallelism with transfer overheads over the PCIe bus.
 
OmniSciDB natively supports standard SQL and returns query results hundreds of times faster than CPU-only analytical database platforms. Analysts and data scientists can still rely on their existing SQL knowledge, querying data using industry-standard SQL.

OmniSciDB is the foundation of the OmniSci platform. OmniSciDB is SQL-based, relational, columnar and specifically developed to harness the massive parallelism of modern CPU and GPU hardware. OmniSciDB can query up to billions of rows in milliseconds, and is capable of unprecedented ingestion speeds, making it the ideal SQL engine for the era of big, high-velocity data.

OmniSciDB - Open Source Analytical Database & SQL Engine

SpiceDB is a database system for managing security-critical application permissions. Developers create a schema that models their permissions requirements and use a client library to apply the schema to the database, insert data into the database, and query the data to efficiently check permissions in their applications.
 
The data used to calculate permissions have the most critical correctness requirements in the entirety a software system. Despite that, developers continue to build their own ad-hoc solutions coupled to the internal code of each new project. By developing a SpiceDB schema, you can iterate far more quickly and exhaustively test designs before altering any application code. This becomes especially important as you introduce backwards-compatible changes to the schema and want to ensure that the system remains secure.

SpiceDB is a database system for managing security-critical application permissions. Developers create a schema that models their permissions requirements and use a client library to apply the schema to the database, insert data into the database, and query the data to efficiently check permissions in their applications.

SpiceDB - Inspired by Google's Zanzibar paper, SpiceDB is a database system for managing security-critical application permissions

Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility.
As the Internet grew and evolved, unstructured data became more and more common, including emails, papers, IoT sensor data, Facebook photos, protein structures, and much more. In order for computers to understand and process unstructured data, these are converted into vectors using embedding techniques. Milvus stores and indexes these vectors. Milvus is able to analyze the correlation between two vectors by calculating their similarity distance. If the two embedding vectors are very similar, it means that the original data sources are similar as well. &nbsp; Millvus is capable of indexing vectors on a trillion scale. Unlike existing relational databases which mainly deal with structured data following a pre-defined pattern, Milvus is designed from the bottom-up to handle embedding vectors converted from unstructured data.
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Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility.



Milvus - An open-source vector database for embedding similarity search and AI applications

FlockDB is much simpler than other graph databases such as neo4j because it tries to solve fewer problems. It scales horizontally and is designed for on-line, low-latency, high throughput environments such as web-sites. Twitter uses FlockDB to store social graphs (who follows whom, who blocks whom) and secondary indices. As of April 2010, the Twitter FlockDB cluster stores 13+ billion edges and sustains peak traffic of 20k writes/second and 100k reads/second.

FlockDB - A distributed, fault-tolerant graph database from Twitter

The Apache Accumulo sorted, distributed key/value store is a robust, scalable, high performance data storage and retrieval system. Apache Accumulo is based on Google's BigTable design and is built on top of Apache Hadoop, Zookeeper, and Thrift. Apache Accumulo features a few novel improvements on the BigTable design in the form of cell-based access control and a server-side programming mechanism that can modify key/value pairs at various points in the data management process.

Apache Accumulo - Key Value Store based on Google BigTable

ArangoDB is a multi-purpose open-source database with flexible data model for documents, graphs, and key-values. Build high performance application using a convenient sql-like query language or JavaScript/Ruby extensions. Its key features are  Schema-free, Convenient querying using AQL, Extendable through JS, Space efficiency, Supports modern storage hardware, like SSD and large caches and lot more.

ArangoDB - The Multi-purpose NoSQL DB

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

ForestDB is a key-value storage engine developed by Couchbase Caching and Storage Team, and its main index structure is built from Hierarchical B+-Tree based Trie, called HB+-Trie. ForestDB paper has been published in IEEE Transactions on Computers.
 
Compared with traditional B+-Tree based storage engines, ForestDB shows significantly better read and write performance with less storage overhead. ForestDB has been tested on various server OS environments (Centos, Ubuntu, Mac OS x, Windows) and mobile OSs (iOS, Android). The test coverage stats for ForestDB are available in ForestDB Code Coverage Report.

ForestDB is a key-value storage engine developed by Couchbase Caching and Storage Team, and its main index structure is built from Hierarchical B+-Tree based Trie, called HB+-Trie. ForestDB paper has been published in IEEE Transactions on Computers.

ForestDB - A Fast Key-Value Storage Engine Based on Hierarchical B+-Tree Trie

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