Hybrid real-time data processing

Latest Research


Hybrid real-time data processing

There are multiple ways in which databases that support real-time operational/transactional and analytic processing may be implemented. This report compares representative samples of each.

Authors: Philip Howard
Date Published: 2019 Type: Case Studies

VoltDB

VoltDB uses a shared-nothing architecture to achieve database parallelism, with both data and processing distributed across all the CPU cores within the servers composing a VoltDB cluster.

Authors: Philip Howard
Date Published: 2019 Type: Case Studies

TigerGraph

TigerGraph is a native graph parallel database that is available both in on-premises and cloud versions.

Authors: Philip Howard
Date Published: 2019 Type: Case Studies

Redis Enterprise

Redis is an in-memory, multi-model database platform.

Authors: Philip Howard
Date Published: 2019 Type: Case Studies

NuoDB

NuoDB looks relational and leverages SQL but isn’t relational under the covers.

Authors: Philip Howard
Date Published: 2019 Type: Case Studies

Neo4j

Neo4j, is a labelled, property graph database with a native engine that is targeted at operational and hybrid operational/transactional and analytic use cases.

Authors: Philip Howard
Date Published: 2019 Type: Case Studies