Compare KX and QuestDB 
- Users report that KX excels in query latency, achieving a score of 9.8, which reviewers mention significantly enhances performance for real-time analytics. In contrast, QuestDB also performs well but does not match this level of efficiency.
- Reviewers mention that QuestDB shines in ease of use with a score of 8.9, making it more accessible for small businesses. KX, while still functional, has a lower ease of use rating at 6.8, which some users find challenging.
- G2 users highlight that QuestDB offers superior quality of support, scoring 9.8, which reviewers say contributes to a better overall user experience. KX, while still providing good support at 8.7, does not reach the same level of satisfaction.
- Users on G2 report that KX has a robust data replication feature with a score of 9.0, which is crucial for enterprise-level applications. QuestDB, while effective, does not emphasize this feature as strongly.
- Reviewers mention that KX's integrated cache feature scores 9.1, which users say helps in optimizing performance for frequently accessed data. QuestDB, while it has its strengths, does not highlight caching as a key feature.
- Users say that both products have strong security features, but KX's role-based authorization and encryption scores of 8.0 and 8.2 respectively, are seen as slightly less robust compared to QuestDB's overall security framework, which is rated higher in user reviews.
- Fastest query speed
- In-memory compute engine
- Extreme scalability
Q/Kdb+ is extremely fast in large databases processing. Its code is fast and clean and it is integrated into Python via PyKX.
QuestDB is mind-boggling fast both on ingress and egress, enabling new high-frequency use cases in IIOT. Today industrial time series data are highly compressed to achieve fast access for reporting, dashboarding and analytics. It leaves out a lot of use...
- Error handling could improve (particularly for ILP) - Some inconsistency in how well some tools integrate with the database over Postgres database connectors. - Limited support for data sharding/partitioning The issues above are on the roadmap for...
Q/Kdb+ is extremely fast in large databases processing. Its code is fast and clean and it is integrated into Python via PyKX.
QuestDB is mind-boggling fast both on ingress and egress, enabling new high-frequency use cases in IIOT. Today industrial time series data are highly compressed to achieve fast access for reporting, dashboarding and analytics. It leaves out a lot of use...
- Error handling could improve (particularly for ILP) - Some inconsistency in how well some tools integrate with the database over Postgres database connectors. - Limited support for data sharding/partitioning The issues above are on the roadmap for...
