title	conference	date
Apache Spark Lakehouse Pushdowns with Transpilation	Jfokus 2026	2026-02-02
Claim: adding type information often makes Spark 10–400% slower because typed UDFs force full-row reads, so the project transpiles Python/Scala/JVM UDFs into… Talk on
AI For Good: Fighting Health Insurance with AI	AI By the Bay 2025	2025-11-17
A nonprofit pipeline turns published state independent medical review decisions into training pairs, fine-tunes Gemma/Mistral-derived models, and serves them… Talk on
Health Insurance Appeals with Generative AI: From Lab to Production	GOTO Chicago 2024	2024-10-21
A system generates patient appeals from denials by fine‑tuning open‑license LLMs on California IMR records and synthetic examples, using OxaloTLE-style… Talk on
Scaling Python for Machine Learning: Beyond Data Parallelism	GOTO Chicago 2023	2023-05-22
Compares Spark, Dask, and Ray for scaling ML, showing that data parallelism breaks down for stateful work like model weights and reinforcement learning and… Talk on
Keynote: Distributed Computing 4 Kids -- with Spark	PyData Seattle 2023	2023-04-26
A concrete claim: kids can learn distributed systems concepts first and syntax later by mapping work to garden gnomes and using Spark as a teaching vehicle… Talk on
Tower of Babel: Making Apache Spark, Kubeflow, and Kubernetes Play Nice	KubeCon + CloudNativeCon Europe 2022	2022-05-16
Uses Apache Spark + Mahout to run a distributed SVD that lets denoising avoid a single-node 500 GB RAM requirement, reads DICOM CT lungs from S3 into PVCs and… Talk on
Scaling Apache Spark on Kube to Apple Scale	KubeCon + CloudNativeCon Europe 2021	2021-05-04
Apple migrates large Spark workloads from YARN/Mesos to Kubernetes and reports concrete results: small-to-medium ETL jobs run well after increasing resource… Talk on
Upgrade Tooling w/Spark: It's not just for language versions	Scala Love in the City 2021	2021-02-13
A set of migration rules converts common Spark 2 patterns to Spark 3 equivalents using ScalaFix rules for Scala and Bowler interactive mode for Python… Talk on
Why & how you can be reviewing OSS PRs	ITNEXT Summit 2019	2019-10-30
Reviewing OSS PRs accelerates learning and relieves reviewer bottlenecks: data shows large projects like Spark sit with 525 open PRs and maintainer burnout… Talk on
Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)	Open Source Summit + Embedded Linux Conference North America 2019	2019-08-21
Kubeflow packages training, experiment tracking, and serving so models move from laptop to cluster with a few commands: kfctl init / generate / apply. It wires… Talk on
State of diversity in open source communities	Berlin Buzzwords 2019	2019-06-16
Data from 50 projects (about 30 GB of commits plus 11 GB for geographic data) and roughly $300 of human review uses GitHub extraction, Spark notebooks… Talk on
Building Cross-Cloud ML Pipelines with Kubeflow with Spark & Tensorflow	KubeCon + CloudNativeCon Europe 2019	2019-05-20
Shows how Kubeflow, Spark and TensorFlow form a repeatable cross‑cloud pipeline: use JupyterHub for exploration, Spark for distributed feature prep, then… Talk on
Interview	Scale By The Bay 2018	2018-11-15
Builds and contributes to Apache Spark at Google and works across Airflow, SystemML, and Mahout, focusing on practical data pipelines that run in cloud… Talk on
Validating Big Data Pipelines & ML (w Spark & Beam)	Strange Loop 2018	2018-09-26
Focus on concrete checks: run Spark & Beam examples that emit counters and compare counts or percentages against historical baselines; flag if input parse… Talk on
Bringing the Jewels of the Python World to Scala with Spark	Lambda World Seattle 2018	2018-09-17
Scala code calls Python NLP (spaCy/NLTK) inside Spark by flipping PySpark’s usual flow: JVM starts Python via Py4J and pipes, registers Python UDFs and ships… Talk on
Simplifying Training Deep & Serving Learning Models...using Tensorflow	PyData Berlin 2018	2018-07-06
Hands-on pipeline work: building feature prep that computes constants with TF Transform, packaging Python into Docker via Beam’s portability layer and running… Talk on
Keeping the “fun” in Apache Spark Datasets and FP	Scala Days New York 2018	2018-06-19
Covers concrete limits and wins of Spark Datasets: RDD→Dataset migration yields roughly 2x storage reduction in the presenter’s tests and faster shuffles via… Talk on
Working with Tensorflow from the JVM #bbuzz	Berlin Buzzwords 2018	2018-06-11
Shows how Java jobs start TensorFlow per partition using TF on Spark and pandas UDF wrappers, forcing a Java→pickle→socket→Python→C++ chase that doubles… Talk on
Keeping the fun in Apache Spark Datasets and FP	Scala Days Berlin 2018	2018-05-14
Apache Spark Datasets add typed functional operators on top of faster, SQL-like DataFrames but lose type safety at groupBy and custom aggregation points… Talk on
Debugging PySpark -- Or trying to make sense of a JVM stack trace when you were minding your own bus	PyCon 2018	2018-05-11
Shows concrete patterns: search logs for the org.apache.spark.api.python Python exception, run Spark 2.2/2.3 to get better Jupyter tracebacks, and expect… Talk on
Validating Big Data Jobs - Stopping Failures before Production	QCon São Paulo 2018	2018-05-09
Big data pipelines use Apache Spark and Apache BEAM plus helper libs (spark-validator, counters, property-based generators) to run automated schema and… Talk on
Keynote: Making the Big Data ecosystem work together with Python	PyData London 2018	2018-04-26
Shows concrete fixes: JVM↔Python double-serialization via Py4J and custom pickling causes huge overhead and copying; replacing that path with Apache Arrow and… Talk on
Scaling with Apache Spark (or a lesson in unintended consequences)	Strange Loop 2017	2017-09-28
Examines Apache Spark’s scaling tradeoffs with concrete examples: RDD pipelining and laziness speed things up but produce 400‑line stack traces and late… Talk on
Improving PySpark Performance: Spark performance beyond the JVM	PyCon AU 2017	2017-08-03
Shows that Python Spark suffers from double serialization between Python and the JVM and that the fastest wins come from changing primitives and where work… Talk on
Extending Spark Machine Learning Beyond Linear Regression	Scala Days Copenhagen 2017	2017-05-30
Shows how to add custom Transformer and Estimator stages to Spark ML pipelines in Scala (Python bindings not supported yet), with runnable examples: a simple… Talk on
Introduction to Spark Datasets	Scala Days Copenhagen 2017	2017-05-30
Datasets claim to combine relational and functional APIs and deliver measurable wins: benchmarks show Dataset/DataFrame runs far faster than RDDs and a sample… Talk on
The Magic Behind PySpark, How It Impacts Perf & the "Future"	PyData Barcelona 2017	2017-05-19
Explains the concrete claim that Python is not Spark's bottleneck when data movement and serialization are fixed, shows actual benchmarks comparing RDD vs… Talk on
Debugging PySpark - Pretending to make sense of JVM stack traces	PyData Amsterdam 2017	2017-04-07
Covers concrete debugging tactics for PySpark: where to find useful output (use the spark history server or worker stderr rather than the console), how to… Talk on
Improving PySpark Performance Spark performance beyond the JVM	PyData DC 2016	2016-10-07
Explains concrete ways to speed PySpark by cutting Python–JVM overhead and shrinking shuffles: uses the py4j bridge, pickling avoidance, RDD cache/persist for… Talk on
Beyond Shuffling: Scaling Apache Spark	Scala Days Berlin 2016	2016-06-15
Shows concrete fixes and measurements: use reduceByKey or aggregateByKey instead of groupByKey to get map‑side reduction (example: shuffle read drops from ~48… Talk on
Improving PySpark Performance: Spark performance beyond the JVM	PyData Amsterdam 2016	2016-03-12
PySpark bottlenecks center on the double serialization cost between Python and the JVM and DataFrames move heavy work inside the JVM so Python code runs much… Talk on
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-- 31 of 31 talks indexed --