Dean Wampler
where else to find them
what they talk about
all 18 in the filters
where they keep going back
- GOTO Chicago5 editions2015–2023
- Scale By the Bay4 editions2017–2023
all 18 in the filters
on a stage with
- Andy Petrella2×
- Chris Freglyonce
- Eva Andreassononce
all 12 in the filters
who talks about the same things
- Andy Petrella14 talks
- Holden Karau39 talks
- Olena Kutsenko31 talks
- Frank Munz8 talks
- Oleg Klimenko4 talks
- Vincent Heuschling8 talks
| title | conference | date |
|---|---|---|
| Generative AI: Say Goodbye to Deterministic Testing? | Functional Conf 2025 | 2025-01-24 |
Generative AI introduces non-determinism so deterministic unit tests fail; practical responses include external tool verification (parsers, compilers, security… | ||
| OSS vs. Open Source Software: What's Different? What's the Same? | Scale By the Bay 2023 | 2023-11-13 |
Running engineering for IBM's Accelerated Discovery Platform shows open-source science prioritizes reproducibility of results and treats models and datasets as… | ||
| PANEL - Streaming. | Scale By the Bay 2023 | 2023-11-13 |
Panel surveys production streaming: why it matters, how teams trade latency, cost and correctness, and which tools actually ship. Examples include using a… | ||
| Reinforcement Learning - ChatGPT, Playing Games & More | GOTO Chicago 2023 | 2023-05-22 |
Reinforcement learning explains how agents learn from sequential interaction to maximize cumulative reward, with landmark results on Atari and AlphaGo and… | ||
| Lessons Learned from 15 Years of Scala in the Wild | Scale By the Bay 2021 | 2021-10-28 |
Scala 3 claims to fix pain points from ~15 years of real use by making implicits explicit with using/given context and true extension methods, adding opaque… | ||
| A Tour of Contextual Abstractions in Scala 3 | Scala Love in the City 2021 | 2021-02-13 |
Live REPL demos show Scala 3 replacing the old implicit mechanism with concrete features: extension methods for adding methods without wrapper objects, given… | ||
| Ray: A System for High-performance, Distributed ML Applications | Scale By the Bay 2020 | 2020-11-12 |
Ray is a lightweight distributed runtime that scales Python from laptop to clusters (users run up to 10,000 nodes), exposes a tiny API — ray.remote for tasks… | ||
| Panel - Tips for Writing an Effective AI and Deep Learning Book | Data Con LA 2020 | 2020-10-23 |
Panel reports practical author choices: publishing route matters (self-publish boosts revenue, a major publisher supplies reviewers and marketing), many… | ||
| Ray: A System for Distributed Applications | GOTO Chicago 2020 | 2020-04-27 |
Ray scales Python from laptop to cluster by turning functions into ray.remote tasks and objects into actors for stateful servers. Demo: Monte Carlo π runs on… | ||
| Ray: A System for High-performance, Distributed Python Applications | PyCon US 2020 | 2020-04-15 |
Ray claims to scale Python from a laptop to a cluster by making functions into remote tasks and actors scheduled across nodes with a global control store and… | ||
| Kafka based Microservices with Akka Streams and Kafka Streams | Scala Days New York 2018 | 2018-06-19 |
Compares two Scala libraries for Kafka-backed microservices with concrete code: a Kafka Streams pipeline that reads a raw topic and a model topic, trains… | ||
| Bash and All That; Why Ancient *NIX Tools Are Still Essential | GOTO Chicago 2018 | 2018-04-24 |
Shows concrete snippets: a bash script launches Marathon/DCOS with a JSON file and exits on missing commands, proving quick operational glue that “gets the job… | ||
| Dean Wampler (Lightbend) interviewed at O'Reilly Software Architecture Conference NY 2018 | O'Reilly Software Architecture Conference New York 2018 | 2018-02-26 |
A curated fast data platform that packages open-source streaming engines, monitoring and production tooling and runs on DC/OS so it deploys cloud or… | ||
| Stream All The Things! | Scale By the Bay 2017 | 2017-11-16 |
Compares concrete streaming engines and trade-offs: Kafka as durable event log, Spark and Flink for cluster-parallel processing, Akka and Kafka Streams as… | ||
| Stream All the Things! | Scala.IO 2017 | 2017-11-02 |
Argues that data-centric and microservice architectures converge and compares four concrete tools: Spark, Flink, Akka Streams, Kafka Streams, with Kafka as the… | ||
| Streaming Data Pipelines on Apache Mesos: Lessons Learned | MesosCon North America 2017 | 2017-09-13 |
Argues that Mesos outperforms YARN for mixed streaming and microservice workloads, showing concrete reasons: Mesos two-level scheduling lets frameworks decide… | ||
| Scala and the JVM as a Big Data Platform Lessons from Apache Spark | Scala Days Berlin 2016 | 2016-06-15 |
Scala-backed Apache Spark uses a Collections-like Scala API and evolves a set of pragmatic fixes for JVM limits: Tungsten builds custom data layouts, manual… | ||
| Scala: The Unpredicted Lingua Franca for Data Science | Scala Days Berlin 2016 | 2016-06-15 |
Argument: Scala gains traction because JVM deployment, functional closures and Spark let teams run distributed analytics without language gaps; claim includes… | ||
| Scala: The Unpredicted Lingua Franca for Data Science | Scala Days Berlin 2016 | 2016-06-15 |
Claims Scala becomes the practical engine behind distributed data science by enabling interactive, production-ready workflows using Spark Notebook and Docker… | ||
| Scala: The Unpredicted Lingua Franca for Data Science | GOTO Chicago 2016 | 2016-05-23 |
Argues that Scala becomes a practical lingua franca because core big‑data tools like Spark use it, then demos working code in Spark notebook demos and the… | ||
| Scala and the JVM as a Big Data Platform | Scala World 2015 | 2015-09-20 |
Apache Spark is implemented in Scala and it’s user-facing Scala API is very similar to Scala’s own collections API. The power and concision of this API are… | ||
| Data Science at Scale with Apache Spark | GOTO Chicago 2015 | 2015-05-11 |
Argues that Apache Spark replaces MapReduce for Hadoop workloads and shows working code: a Scala inverted index in ~30 minutes that reads text files, flatMaps… | ||
| Deep Dive into the Big Data Landscape | GOTO Copenhagen 2014 | 2014-09-25 |
Concrete pipelines and outcomes: structured sales CSVs load with Scoop -> Avro -> Hive -> Impala to produce interactive top-sellers; web clickstreams ingest… | ||
| What's Ahead for Big Data? | Code Mesh 2013 | 2013-12-03 |
Summarizes where Hadoop/MapReduce and NoSQL succeed and fail, gives concrete alternatives (Cascading/Scalding, Spark over MapReduce, Hive/Impala/Presto)… | ||
| From Big Data to Big Information | GOTO Aarhus 2013 | 2013-09-30 |
Hadoop remains the low‑cost, batch engine for massive scans (used at Facebook on ~600 PB and chosen to replace a $1M data warehouse to keep years of history on… | ||
| Beyond MapReduce | Tech Mesh 2012 | 2012-12-04 |
MapReduce on Hadoop delivers cost‑effective scalability but proves batch‑oriented not real‑time: joins and iterative graph algorithms are awkward, and… | ||
| Dean Wampler interviewed at OSCON 2010 | OSCON 2010 | 2010-07-19 |
Scala positions itself as a modern replacement for Java, designed for systems and servers by runs on the JVM while adding functional and modular features that… | ||
| Better Ruby through Functional Programming | RubyConf 2008 | 2008-11-06 |
Argues that immutability and side‑effect‑free functions make Ruby code easier to reason about and scale to many cores, demonstrates an immutable Person via… | ||
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