Neil Mukerje
Employers on the program
- 2019–2025Amazon Web Services4 talks
The year around now
Talks in the past 12 months and announced talks ahead, one row per conference, January to December. Hover a mark for the talk.
Appearances by year
- AWS re:Invent
Talks
| Date | Event | Conference | Role | Title | Links |
|---|---|---|---|---|---|
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | breakout | Innovations in AWS analytics: Data processing as Amazon Web Services Explore the latest advancements in AWS Analytics designed to transform your data processing landscape. | Video |
| Nov 28, 2022 | AWS re:Invent 2022 | AWS re:Invent | breakout | What’s new with Amazon EMR as AWS Amazon EMR makes it easy to run and scale big data applications built using open-source frameworks such as Apache Spark, Hive, Presto, Hudi, and more. It helps…Show lessAmazon EMR makes it easy to run and scale big data applications built using open-source frameworks such as Apache Spark, Hive, Presto, Hudi, and more. It helps users run ETL and ML jobs and accelerate data science adoption, real-time data processing, and low-latency SQL at petabyte scale. | SlidesVideo |
| Nov 29, 2021 | AWS re:Invent 2021 | AWS re:Invent | breakout | Deep dive: Accelerating Apache Spark with Amazon EMR as Amazon Web Services Running Apache Spark workloads on Amazon EMR is becoming faster and more cost-effective. In this session, explore the features that Amazon EMR offers to improve…Show lessRunning Apache Spark workloads on Amazon EMR is becoming faster and more cost-effective. In this session, explore the features that Amazon EMR offers to improve performance and reduce the cost of operating big data analytics workloads. | SlidesVideo |
| Dec 2, 2019 | AWS re:Invent 2019 | AWS re:Invent | workshop | [REPEAT 1] Build & optimize Spark data pipelines for incremental data processing as Amazon Web Services Apache Spark provides a useful framework for building data pipelines, which form the core of any data infrastructure. Often, the need is to build incremental…Show lessApache Spark provides a useful framework for building data pipelines, which form the core of any data infrastructure. Often, the need is to build incremental data processing pipelines; for example, transactional data that is ingested from online transaction processing (OLTP) databases. | Slides |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Radhika Ravirala | 4 | 2 | 2025 |
| Alan Halachmi | 4 | — | 2025 |
| Alex DeBrie | 4 | — | 2025 |
| Ali Saidi | 4 | — | 2025 |
| Amit Kumar | 4 | — | 2025 |
| Andrew Baird | 4 | — | 2025 |
| Andrew Crudge | 4 | — | 2025 |
| Andrew Kane | 4 | — | 2025 |
| Andrew Krug | 4 | — | 2025 |
| Andy Reedy | 4 | — | 2025 |
| Arthur Petitpierre | 4 | — | 2025 |
| Carlos Manzanedo Rueda | 4 | — | 2025 |
| Colm MacCárthaigh | 4 | — | 2025 |
| Curtis Rissi | 4 | — | 2025 |
| Dave Brown | 4 | — | 2025 |
Speakers with the same name are merged into one entry, so this page may combine more than one person. Affiliations are shown as each conference listed them.