thatDot

Name: thatDot
Brand: thatDot

Streaming Graph | 1,000,000+ events per second | Novelty Detection

Est 2019

LinkedIn 4K|Website|FTEs 17|HQ Portland, Oregon, US

01Competitive Landscape

Narrative analysis

Quine vs Neo4j (graph database approach) - Neo4j has discussed streaming-graph patterns and the convergence of streaming and graph domains. Neo4j is primarily a database, while Quine is framed as a streaming system that builds a graph state and runs continuous queries. Quine vs Flink/Kafka Streams (stream processing approach) - Flink and Kafka Streams are optimized for stream transformations, joins, windows, and stateful operators, but they do not provide a graph-first query model out of the box. - Quine is frequently positioned as a complementary component in Kafka-based architectures to provide graph-native correlation capabilities (vendor conference presentation). Market adoption and share - No authoritative public market-share numbers were found for a "streaming graph" category specifically. The category is emerging and may not map cleanly to standard analyst quadrants.

	thatDot (Quine / Quine Enterprise)	Neo4j	Apache Flink
Starting Price	Open-source edition available; enterprise pricing provided by vendor on request	Community edition available; enterprise pricing varies by deployment and vendor	Open-source edition available; managed/enterprise offerings available from vendors with varying pricing
Best For	Continuous multi-hop pattern detection on streaming connected data; standing queries that trigger actions	Graph data storage and traversal queries; graph analytics on persisted graphs	General-purpose stream processing (joins/windowing/stateful operators)
Key Strength	Graph-native continuous computation over streaming data; integrates with Kafka/Kinesis; Cypher-based patterning	Mature graph DB ecosystem, tooling, and community; strong graph query semantics	Mature stream processing ecosystem; broad connectors; established operational patterns
Key Weakness	Limited independent review coverage; potential operational complexity; enterprise security/compliance information not clearly public	Not purpose-built as a streaming engine; integrating streaming often requires additional components/patterns	Graph-native multi-hop pattern detection is not native; implementing complex graph correlation can be non-trivial

01Competitive Landscape

Narrative analysis

Quine vs Neo4j (graph database approach)

Neo4j has discussed streaming-graph patterns and the convergence of streaming and graph domains. Neo4j is primarily a database, while Quine is framed as a streaming system that builds a graph state and runs continuous queries.

Quine vs Flink/Kafka Streams (stream processing approach)

Flink and Kafka Streams are optimized for stream transformations, joins, windows, and stateful operators, but they do not provide a graph-first query model out of the box.
Quine is frequently positioned as a complementary component in Kafka-based architectures to provide graph-native correlation capabilities (vendor conference presentation).

Market adoption and share

No authoritative public market-share numbers were found for a "streaming graph" category specifically. The category is emerging and may not map cleanly to standard analyst quadrants.

	thatDot (Quine / Quine Enterprise)	Neo4j	Apache Flink
Starting Price	Open-source edition available; enterprise pricing provided by vendor on request	Community edition available; enterprise pricing varies by deployment and vendor	Open-source edition available; managed/enterprise offerings available from vendors with varying pricing
Best For	Continuous multi-hop pattern detection on streaming connected data; standing queries that trigger actions	Graph data storage and traversal queries; graph analytics on persisted graphs	General-purpose stream processing (joins/windowing/stateful operators)
Key Strength	Graph-native continuous computation over streaming data; integrates with Kafka/Kinesis; Cypher-based patterning	Mature graph DB ecosystem, tooling, and community; strong graph query semantics	Mature stream processing ecosystem; broad connectors; established operational patterns
Key Weakness	Limited independent review coverage; potential operational complexity; enterprise security/compliance information not clearly public	Not purpose-built as a streaming engine; integrating streaming often requires additional components/patterns	Graph-native multi-hop pattern detection is not native; implementing complex graph correlation can be non-trivial

thatDot

01Competitive Landscape

Narrative analysis

02Market Map

01Competitive Landscape

Narrative analysis

02Market Map