Prof. Dr. Tilmann Rabl

Panos Parchas

Affiliation: Amazon
Title: Amazon Redshift re-invented



In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift, the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools. This cloud service was a significant leap from the traditional on-premise data warehousing solutions, which were expensive, not elastic, and required significant expertise to tune and operate. Customers embraced Amazon Redshift and it became the fastest growing service in AWS. Today, tens of thousands of customers use Redshift in AWS’s global infrastructure to process exabytes of data daily. In the last few years, the use cases for Amazon Redshift have evolved and in response, the service has delivered and continues to deliver a series of innovations that delight customers. Through architectural enhancements, Amazon Redshift has maintained its industry-leading performance. Redshift improved storage and compute scalability with innovations such as tiered storage, multi- cluster auto-scaling, cross-cluster data sharing and the AQUA query acceleration layer. Autonomics have made Amazon Redshift easier to use. Amazon Redshift Serverless is the culmination of autonomics effort, which allows customers to run and scale analytics without the need to set up and manage data warehouse infrastructure. Finally, Amazon Redshift extends beyond traditional data warehousing workloads, by integrating with the broad AWS ecosystem with features such as querying the data lake with Spectrum, semi-structured data ingestion and querying with PartiQL, streaming ingestion from Kinesis and MSK, Redshift ML, federated queries to Aurora and RDS operational databases, and federated materialized views.

Short CV

Panos Parchas is a senior software development engineer with Amazon Redshift, and one of the founding members of Redshift Berlin R&D team. Before joining Amazon Panos received his PhD degree in Hong Kong University of Science and Technology, focusing on query processing in uncertain graphs. Recently, he has been working on query processing, query rewriting, materialized views, data distribution and DBMS auto-tuning.