Systems with these properties are emerging from the NewSQL, NoSQL, and Hadoop communities, but different systems make different trade-offs, often based on their starting assumptions. For organizations that want to act on fast data in real time, these tools can remove much of the complexity involved in understanding data with velocity.
Kafka provides a safe and highly available way to move data between myriad producers and consumers, while offering performance and robustness to put admins at ease. An in-memory database can offer a full relational engine with powerful transactional logic, counting, and aggregation, all with enough scalability to meet any load. More than acting as a relational database, this system should serve as a processing engine complementary to Kafka's messaging infrastructure.
Whatever your organization's needs, it's likely that some combination of these tools can help you do more faster and know more than you know today, often while replacing more fragile or disparate systems.
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