Kamil Owczarek
where else to find them
github:saevel
what they talk about
where they keep going back
- Scalar2 editions2018–2020
all 4 in the filters
on a stage with
never shared a talk
who talks about the same things
- Andy Petrella14 talks
- Matei Zaharia30 talks
- Jonathan winandy5 talks
- Reynold Xin15 talks
- Oleg Klimenko4 talks
- Dean Wampler29 talks
| title | conference | date |
|---|---|---|
| SPARK-CYPHER: HOW WE WILL BE DOING GRAPH ANALYTICS FROM 2020 ONWARDS | Scalar 2020 | 2020-05-15 |
Spark 3 promises up to 17× faster execution (Alibaba’s GTPc benchmarks) and built‑in features like adaptive execution and GPU support, but the planned unified… | ||
| Data Streaming: Which horse to bet on? | GeeCON 2019 | 2019-05-15 |
Compares Akka Streams, Kafka Streams, Flink, Spark Streaming and Beam and claims there is no silver bullet: low latency vs high throughput is the core… | ||
| Orchestration of Big Data systems: A survivor's guide | JDD 2018 | 2018-10-08 |
Describes concrete tradeoffs: arrangement is handled by Spark/Impala so orchestration focuses on coordination and dependency management, not deployment… | ||
| SPARK AND AKKA FOR BIG DATA SYSTEMS, IN PRACTICE | Scalar 2018 | 2018-04-06 |
Compares concrete tradeoffs: use Akka Cluster and actors for low latency stream or custom graph processing with built in back pressure via reactive drivers and… | ||
| CONTINOUS APPLICATIONS WITH SPARK 2.0 | Scalar Conference 2017 | 2017-04-07 |
Spark 2.0 adds continuous applications that read Kafka and file sources into DataFrames/Datasets, run SQL transforms, windowed aggregations and ML pipelines… | ||
| nothing matches | ||