Integrating Data Science and Relational Systems
The database research community has made tremendous strides in developing powerful database engines that allow for efficient analytical query processing. However, these powerful systems have gone largely unused by data scientists. This poor adoption is caused primarily by the state of database-client integration: current methods of combining databases with analytical tools are slow and cumbersome.
Instead, data scientists have opted to re-invent database systems by developing a zoo of data management alternatives. These systems perform similar tasks to classical database management systems, but have many of the problems that were solved in the database field decades ago.
Bridging the gap
CWI researcher Mark Raasveldt investigated how to bridge this gap by making database management systems easier to use and more efficient for these workloads. The aim was facilitating an efficient and smooth integration of analytical tools and relational database management systems. Raasveldt’s research focused on the three primary methods for database-client integration: client-server connections, in-database processing and embedding the database inside the client application.
Raasveldt proposed several novel techniques that improve upon the state-of-the-art. In his thesis, he demonstrated a new client-server protocol that is optimized for bulk-transfer of large data sets. This allows for more efficient large-scale data analysis when using remote servers. He also showcased so-called vectorized user-defined functions, that improve in-database processing efficiency through vectorized execution.
Furthermore, Raasveldt describes MonetDBLite, an embedded version of the MonetDB database system that was also developed at CWI. MonetDBLite was developed for efficient integration with Python and R.
In his thesis, Raasveldt also showcased DuckDB, a new data management system purpose-built for efficient and painless integration with Python and R (and other analytical tools).
Raasveldt defended his PhD thesis Integrating Analytics with Relational Databases at Leiden University. He performed his research at CWI’s Database Architectures group, supervised by Stefan Manegold and Hannes Mühleisen. Raasveldt will continue his career at CWI as a postdoc, working on the further development of DuckDB.
The ADS Thesis Awards aim to promote excellence in Data Science and AI from students at BSc and Master level. The Awards are open to students from all Amsterdam-based knowledge institutes.
On the 22nd of September LAB42 will host its Grand Opening from 15:00 onwards. LAB42 is an international hub for developing talent in the fields of digital innovation and AI. The building is the result of a partnership between the UvA, the municipality of Amsterdam and the business sector.
The UvA Data Science Centre, part of the University Library, is pleased to host its second yearly Data Science Day on the 13th of October 2022.