Database Management Systems (DBMS)


Large-scale computing services revolve around the management, distribution and analysis of massive data sets. For over 40 years, Berkeley has led the world in recognizing and advancing the centrality of data in computing. Faculty and students at Berkeley have repeatedly defined and redefined the broad database field, combining deep intellectual impact with the birth of multi-billion dollar industries, including relational databases, RAID storage, scalable Internet search and big data analytics. Berkeley also gave birth to many of the most widely-used open source systems in the field including INGRES, Postgres, BerkeleyDB and Apache Spark. Today, our research continues to push the boundaries of data-centric computing, taking the foundations of data management to a broad array of emerging scenarios.

Database group web site:


  • Scalable data analysis and query processing

    Distributed machine learning, graph analytics, physical and logical optimization of machine learning pipelines, query processing on compressed data, performance prediction, streaming applications.

  • Low-latency model serving

    Online model management and maintenance, prediction serving, real-time personalization, latency-accuracy tradeoffs and edge computing for large-scale models.

  • Consistency, concurrency, coordination and reliability

    Coordination avoidance, consistency and monotonicity analysis, transaction isolation levels and protocols, distributed analytics, fault tolerance and fault injection.

  • Declarative languages and runtime systems

    Declarative programming applied to distributed systems, networking, machine learning and interactive visualization.

  • Data storage and physical design

    Hot and cold storage, immutable data structures, indexing, data skipping, versioning, implications of hardware evolution.

  • Metadata management

    Data lineage, versioning, usage tracking and collective intelligence, scalability of metadata management services, metadata representations.

  • Data cleaning, data transformation and crowdsourcing

    Human-data interaction including interactive transformation and crowdsourcing, machine learning for data cleaning, statistical properties of data cleaning pipelines.

  • Secure data processing

    Data processing under homomorphic encryption, data compression and encryption, differential privacy, databases in secure hardware enclaves.

  • Data visualization and interaction

    Interactive querying and transformation, progressive query visualization, predictive interaction, languages for interactive visualization.




Faculty Awards

  • National Academy of Engineering (NAE) Member: Eric Brewer, 2007.
  • American Academy of Arts and Sciences Member: Eric Brewer, 2018.
  • Sloan Research Fellow: Michael Lustig, 2013. Ion Stoica, 2003. Joseph M. Hellerstein, 1998. Eric Brewer, 1997.

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