Karthik Prabhakar
Employers on the program
- 2024–2026Amazon 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 |
|---|---|---|---|---|---|
| Nov 30, 2026 | AWS re:Invent 2026 | AWS re:Invent | breakout | Accelerate analytics with GPUs at lower cost: Spark, DuckDB, and more as Amazon Web Services Feature engineering, ETL, and interactive analytics for AI push CPU-based clusters to their limits. In this session, learn how Amazon EMR with the NVIDIA cuDF…Show lessFeature engineering, ETL, and interactive analytics for AI push CPU-based clusters to their limits. In this session, learn how Amazon EMR with the NVIDIA cuDF Plugin for Apache Spark runs workloads up to 3.7x faster on Amazon EC2 G7 instances with no code changes. | |
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | chalk talk | [NEW LAUNCH] Breaking Free from Local Storage: A New Era for EMR Serverless as Amazon Web Services Managing local storage for Apache Spark workloads has traditionally required careful capacity planning and configuration—get it wrong, and jobs fail due to disk…Show lessManaging local storage for Apache Spark workloads has traditionally required careful capacity planning and configuration—get it wrong, and jobs fail due to disk constraints or you overprovision and waste money. | |
| Dec 1, 2025 | AWS re:Invent 2025 | AWS re:Invent | chalk talk | Mastering data transformations with Amazon Athena as Amazon Web Services Learn best practices for transforming data in Amazon S3 using SQL with Amazon Athena. | |
| Dec 2, 2024 | AWS re:Invent 2024 | AWS re:Invent | chalk talk | Optimize Apache Spark workloads with Amazon EMR Serverless [REPEAT] as Amazon Web Services Enterprises aim to build comprehensive data platforms that serve their Data Engineering divisions. Discover how the Amazon EMR runtime for Apache Spark, a…Show lessEnterprises aim to build comprehensive data platforms that serve their Data Engineering divisions. Discover how the Amazon EMR runtime for Apache Spark, a performance-optimized runtime environment for Spark, delivers out-of-the-box peak performance with Amazon EMR Serverless. | |
| Nov 27, 2023 | AWS re:Invent 2023 | AWS re:Invent | chalk talk | Optimizing Apache Spark workloads with Amazon EMR ServerlessEnterprises aim to build comprehensive data platforms that serve their data engineering divisions. Discover how AWS customers simplify infrastructure…Show lessEnterprises aim to build comprehensive data platforms that serve their data engineering divisions. Discover how AWS customers simplify infrastructure complexities, scale swiftly, and build fault-tolerant multi-AZ Apache Spark and Apache Hive applications with Amazon EMR Serverless. |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Aaron Chen | 4 | — | 2026 |
| Aaron Sempf | 4 | — | 2026 |
| Abbas Mooraj | 4 | — | 2026 |
| Abhishek Pandey | 4 | — | 2026 |
| Abhishek Sharma | 4 | — | 2026 |
| Abhishek Srivastav | 4 | — | 2026 |
| Adam Hill | 4 | — | 2026 |
| Addy Upreti | 4 | — | 2026 |
| Aditi Garg | 4 | — | 2026 |
| Aditya Challa | 4 | — | 2026 |
| Aditya Samant | 4 | — | 2026 |
| Adnan Bilwani | 4 | — | 2026 |
| Ahuva Hazan-Fuchs | 4 | — | 2026 |
| Alak Eswaradass | 4 | — | 2026 |
| Alex Goff | 4 | — | 2026 |
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.