Ted Dunning
where else to find them
what they talk about
all 9 in the filters
where they keep going back
- Berlin Buzzwords4 editions2012–2018
all 9 in the filters
on a stage with
- Ellen Friedmanonce
- Jamie Grieronce
- Maxim Fateevonce
all 5 in the filters
who talks about the same things
- Dean Wampler29 talks
- David Ostrovsky20 talks
- Olena Kutsenko31 talks
- David Richards7 talks
- Marcin Szymaniuk11 talks
- Jack Norris9 talks
| title | conference | date |
|---|---|---|
| How Cheap Learning (not deep learning) Finds Faults in Distributed Systems | Jfokus 2019 | 2019-02-04 |
Argues that small, fast statistical tools spot root causes in messy distributed stacks by measuring and comparing tagged distributions rather than training big… | ||
| Progress For Big Data In Kubernetes | Big Data LDN 2018 | 2018-11-28 |
Kubernetes is now ubiquitous (CNCF finds 60–70% of large companies in production) yet it only handles compute; persistent state breaks container fluidity. The… | ||
| The Answer to Life, the Universe, and Everything | Strata Data Conference 2018 - New York, NY | 2018-09-11 |
Claims that only ~15% of big-data projects reach production (Gartner) versus many teams hitting near 100% readiness by building repeatable platforms. Shows… | ||
| Big News for Big Data in Kubernetes #bbuzz | Berlin Buzzwords 2018 | 2018-06-11 |
Kubernetes now supports running a full software-defined storage layer as a cluster-managed service via a new plugin so containers can hold large state without… | ||
| Turning the internet upside down: Driving big data right to the edge | Strata + Hadoop World 2017 | 2017-03-13 |
Argues the Internet flips from cloud-centric to device-first and shows a working stack: MapR Data Fabric spanning cloud, regional cores and tiny edge clusters… | ||
| Keynote: How Can We Take Flink Forward? | Flink Forward Berlin 2016 | 2016-09-12 |
Flink is already a unified streaming-and-batch engine with technical advantages, but the pivotal need is production adoption not more features. A prototype for… | ||
| 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… | ||
| Faster and Furiouser: Flink Drift | Flink Forward Berlin 2016 | 2016-09-12 |
Engineers push Apache Flink on a MapR streaming transport against the Yahoo streaming benchmark (impressions → enrich with campaign ID → keyed windowed… | ||
| Interview with Ted Dunning | Flink Forward Berlin 2016 | 2016-09-12 |
MapR helps bring Stratosphere into Apache and the project becomes Flink as it shifts from research into enterprise use. Key engineering changes include… | ||
| Fast Cars, Big Data - How Streaming Can Help Formula 1 #bbuzz | Berlin Buzzwords 2016 | 2016-06-05 |
A physics-based Spa‑Francorchamps simulator generates realistic F1 telemetry and streams it through MapR Streams (Kafka-compatible) into MapR‑DB, visualized… | ||
| Ted Dunning, Chief Application Architect, MapR | MLconf ATL 2015 | 2015-09-18 |
Explains a practical recipe: compute co‑occurrence with the G test (log‑likelihood ratio), rank surprising pairs, and use those scores for recommendations… | ||
| Practical t-digest Applications #bbuzz | Berlin Buzzwords 2015 | 2015-05-31 |
Algorithm computes approximate quantiles with constant relative accuracy (very tight at tails, looser near median) by merging sorted points into centroids with… | ||
| What and Why and How: Apache Drill 1.0 #bbuzz | Berlin Buzzwords 2015 | 2015-05-31 |
Apache Drill 1.0 claims to run SQL directly over JSON and files without ETL by using late-binding SQL with any type that observes an empirical schema per row… | ||
| Time Series Databases | Strata + Hadoop World Conference in Barcelona 2014 | 2014-11-19 |
The book Time Series Databases lays out how to collect and store high-volume sensor data, recommends NoSQL store patterns, and shows concrete designs and… | ||
| Ted Dunning, Chief Application Architect, MapR | MLconf SF 2014 | 2014-11-14 |
Describes extending co‑occurrence recommenders to near real‑time updates by pruning histories with an interaction cut and capping item frequency (frequency… | ||
| Real Time Datamining and Aggregation at Scale | Berlin Buzzwords 2012 | 2012-06-04 |
Claims that Storm and Hadoop can cooperate to deliver sub-5 ms typical latencies and <5 s worst-case for a sample metrics app while keeping years of durable… | ||
| nothing matches | ||