The Cambridge Report on Database Research
Anastasia Ailamaki, Samuel Madden, Daniel Abadi, Gustavo Alonso, Sihem Amer-Yahia, Magdalena Balazinska, Philip A. Bernstein, Peter Boncz, Michael Cafarella, Surajit Chaudhuri, Susan Davidson, David DeWitt, Yanlei Diao, Xin Luna Dong, Michael Franklin, Juliana Freire, Johannes Gehrke, Alon Halevy, Joseph M. Hellerstein, Mark D. Hill, Stratos Idreos, Yannis Ioannidis, Christoph Koch, Donald Kossmann, Tim Kraska, Arun Kumar, Guoliang Li, Volker Markl, Renée Miller, C. Mohan, Thomas Neumann, Beng Chin Ooi, Fatma Ozcan, Aditya Parameswaran, Ippokratis Pandis, Jignesh M. Patel, Andrew Pavlo, Danica Porobic, Viktor Sanca, Michael Stonebraker, Julia Stoyanovich, Dan Suciu, Wang-Chiew Tan, Shiv Venkataraman, Matei Zaharia, Stanley B. Zdonik
TL;DR
The Cambridge Report provides a forward-looking synthesis of database research, detailing progress in cloud-native data systems, hardware-aware design, ML/AI integration, and governance. It maps commercial, open-source, and community-health developments while outlining challenges in cloud migration, heterogeneous hardware, GenAI/LLMs, and responsible data management. The document proposes a modular, data-centric research agenda that emphasizes federated data platforms, AI-assisted system design, and human-centric interfaces to sustain real-world impact. Its guidance aims to align academic inquiry with industry needs, policy considerations, and the evolving AI-enabled data ecosystem.
Abstract
On October 19 and 20, 2023, the authors of this report convened in Cambridge, MA, to discuss the state of the database research field, its recent accomplishments and ongoing challenges, and future directions for research and community engagement. This gathering continues a long standing tradition in the database community, dating back to the late 1980s, in which researchers meet roughly every five years to produce a forward looking report. This report summarizes the key takeaways from our discussions. We begin with a retrospective on the academic, open source, and commercial successes of the community over the past five years. We then turn to future opportunities, with a focus on core data systems, particularly in the context of cloud computing and emerging hardware, as well as on the growing impact of data science, data governance, and generative AI. This document is not intended as an exhaustive survey of all technical challenges or industry innovations in the field. Rather, it reflects the perspectives of senior community members on the most pressing challenges and promising opportunities ahead.