Monal Daxini
where else to find them
no profile links on file
what they talk about
all 7 in the filters
where they keep going back
no series more than once — all 4 in the filters
on a stage with
- Jamie Grieronce
- Maxim Fateevonce
- Ted Dunningonce
all 4 in the filters
who talks about the same things
- Frank Munz8 talks
- David Ostrovsky20 talks
- Yingjun Wu8 talks
- Ben Stopford12 talks
- Petr Zapletal7 talks
- Albert Lewandowski3 talks
| title | conference | date |
|---|---|---|
| Netflix Keystone - Real-time Stream Processing Cloud Service | Devoxx US 2017 | 2017-03-21 |
Netflix Keystone is a multi-tenant, self-serve stream-processing service built to handle 1.3 trillion events/day (≈3 PB in, ≈9 PB fan-out) and keep four nines… | ||
| Deep dive into Unbounded Data Processing Systems | Strange Loop 2016 | 2016-09-16 |
Compares concrete systems (Apache Samza, Spark Streaming, Apache Flink) and why Flink is chosen for Netflix’s cloud pipeline that processes over 700 billion… | ||
| Beaming Flink to the Cloud @ Netflix | Flink Forward Berlin 2016 | 2016-09-12 |
Netflix runs a Stream Processing as a Service that uses Kafka, Flink and Apache Beam to move and transform live events at massive scale, claiming 700 billion… | ||
| Panel: Large Scale Streaming in Production | Flink Forward Berlin 2016 | 2016-09-12 |
Companies report running Flink at production scale: millions of messages per second ingested, Netflix processes 1.4 petabyte/day, and teams at Alibaba and Uber… | ||
| Netflix Keystone - Streaming Data Pipeline @Scale in the Cloud | Data By the Bay 2016 | 2016-05-16 |
Netflix Keystone moves over 700 billion events per day (peaked at a trillion) with at‑least‑once semantics, using fronting and consumer Kafka clusters (4000+… | ||
| DBTB INT Monal Daxini | Data By the Bay 2016 | 2016-05-16 |
Argues that real-time streaming data is ubiquitous and that a correct answer is building reusable pipelines rather than point analyses, describing work on a… | ||
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