Ben Stopford
where else to find them
what they talk about
all 6 in the filters
where they keep going back
no series more than once — all 8 in the filters
on a stage with
- Cornelia Davisonce
who talks about the same things
- David Ostrovsky20 talks
- Robin Moffatt28 talks
- Neha Narkhede6 talks
- Ricardo Ferreira16 talks
- Viktor Gamov42 talks
- Jay Kreps13 talks
| title | conference | date |
|---|---|---|
| A Data Mesh Of Streams | State of Data Mesh 2022 | 2022-02-23 |
Shows a concrete 10-step implementation: use Kafka/Confluent Cloud with >100 connectors, Schema Registry and a rolling upgrade window for incompatible schema… | ||
| Microservices, Events, and Breaking the Data Monolith with Kafka | Cloud Foundry Summit Europe 2019 | 2019-09-11 |
Explains how Kafka becomes the durable backbone for microservices by storing events (hundreds of terabytes on large clusters) that can be replayed to rebuild… | ||
| Event Streaming and the Future of Applications | JAX 2019 | 2019-05-06 |
Argues that Apache Kafka is both messaging and storage, running across hundreds of machines and holding hundreds of terabytes, with an immutable audit trail… | ||
| The Future of Streaming: Global Apps, Event Stores and Serverless | W-JAX 2018 | 2018-11-05 |
Argues that Apache Kafka powers global streaming with clusters of hundreds (some installations pushing thousands) and topics holding hundreds of terabytes… | ||
| The data dichotomy: Rethinking data and services with streams | NDC London 2018 | 2018-01-15 |
Argues that service architectures must accept a data dichotomy and use a shared immutable log to both notify and replicate state, showing how Kafka plus… | ||
| Building Event Driven Services with Apache Kafka and Kafka Streams | Devoxx Belgium 2017 | 2017-11-06 |
Explains how Apache Kafka plus Kafka Streams turn event streams into local state and scalable services, using CDC connectors, Avro schema registry and Kafka… | ||
| PREVIEW: Event-Driven Services with Stateful Streams | Kafka Summit San Francisco 2017 | 2017-08-28 |
Makes the concrete claim that you can build stateful, request‑response capable services on top of stream processing rather than separate databases, using Kafka… | ||
| Contemporary Approaches to Data at Scale | Code Mesh 2015 | 2015-11-03 |
Explains that storage performance hinges on sequential vs random I/O and compares three concrete engines: in-memory indexes (fast until indexes exceed RAM)… | ||
| nothing matches | ||