Radhika Ravirala

Prepare to revolutionize your data infrastructure for the AI era with Amazon EMR, AWS Glue, and Amazon Athena. This session will guide you through leveraging these powerful AWS services to construct robust, scalable data architectures that empower AI solutions at scale.

Dive into data security with this session on implementing fine-grained access control for Apache Spark workloads in AWS. Through live whiteboarding and collaborative problem-solving, we'll explore architectural patterns for securing data across EMR and AWS Glue environments.

Transform your data landscape by building large-scale transactional data lakes using open table formats (OTFs) with AWS analytics services. The rise of generative AI and ML demands robust and scalable data infrastructure, and OTFs offer a cutting-edge solution for modern data architectures.

Organizations use open source data solutions to empower teams with the latest technology and build applications with the most flexibility. Amazon EMR and Amazon Athena are fully managed analytics services that make it faster and easier to run open source frameworks such as Apache Spark, Trino, Hudi, Iceberg, and more.

Amazon EMR Serverless makes it easy to run large-scale distributed data processing jobs using open-source frameworks like Apache Spark and Hive without worrying about configuring, managing, and securing clusters. Learn about running Amazon EMR Serverless jobs cost-effectively, discover new features, and hear Disney discuss their migration to Amazon EMR Serverless.

Amazon EMR makes it easy to run big data analytics using frameworks like Apache Spark, Presto, and Hive. Join this session to learn about the newest developments in Amazon EMR and how AWS is continuously making it easy for users to run big data processing jobs in the cloud.

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 performance and reduce the cost of operating big data analytics workloads.

Join this chalk talk to learn how Amazon Kinesis Data Firehose enables you to reliably load your streaming data into data lakes, data warehouses, and analytics services built on AWS, with AWS Partners, and using open-source tools. This talk includes a demonstration showcasing how Amazon Kinesis Data Firehose easily captures, transforms, and delivers streaming data to a data lake built on...

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 data processing pipelines; for example, transactional data that is ingested from online transaction processing (OLTP) databases.