Vincent Gromakowski

Struggling with fragmented data across multiple platforms? Join us for an interactive session where we'll explore strategies for breaking data silos and achieving interoperability between AWS and external data sources such as data in SAP, Google BigQuery, Snowflake, and Oracle.

In today’s fast-evolving landscape, data professionals are challenged to build solutions that can quickly scale, deliver cost-effective results, and support the needs of a variety of data users.

In this chalk talk, learn how AWS serverless offerings can help you implement your Apache Spark workloads without worrying about the underlying infrastructure. Explore different Spark use cases and the available options to run these workloads with AWS analytics services.

Is your organization considering a move to Kubernetes and Amazon EKS and wondering how to run Apache Spark applications on Amazon EKS? In this chalk talk, learn how Amazon EMR on EKS simplifies running Spark applications on Amazon EKS.

Have you ever felt that building data science applications, data engineering pipelines, or machine learning models was hard with Apache Spark on Amazon EMR? Join this workshop to learn how Amazon EMR Studio makes it simple to do these things.

Amazon EMR simplifies running open-source data processing applications such as Apache Spark, Apache Hive, and Presto on AWS, enabling users to run ETL, ML, real-time processing, data science, and low-latency SQL at petabyte scale. This session covers the latest on Amazon EMR and how Amazon EMR runtimes provide excellent performance to open-source versions of such engines without breaking API...