Skip to main content

Blogs

MonetDBLite for Python

We are releasing MonetDBLite for Python. Using MonetDBLite for Python, you can easily use MonetDB's powerful features from within your Python client without having to run and maintain a separate database server. As both MonetDB and Python run within the same process, data transfer between the two is extremely efficient, as no actual transfer takes place.

MonetDB Goes Headless

In the beginning of MonetDB (in the 90's) there were BATs, Binary Association Tables, which is considered the minimal relationship to be maintained in an RDBMS [1]. A BAT consisted of BUNs, Binary UNits, which in turn consisted of a HEAD and a TAIL value. All head and tail values of a BAT were of the same type, but the head and tail types were independent of each other. There were (and still are) a whole bunch of built-in types from which to choose, and types could (and still can) be added in extension code.

MonetDB autumn 2016 feature release outlook

The major feature release of June2016 is behind us, and now it's time to look ahead for the next one. The list of topics looked into and reported on Bugzilla covers the full range of functional SQL enhancements, quality assurance, kernel algorithms, code cleaning, and packaging. Too much to tackle all at once. Therefore, our prioritized list contains the following topics for consideration of inclusion in the next feature release.

MonetDB/Python Loader Functions

The primary purpose of a database is to store and manage data. Without data, a database is not very useful. As such, the first thing you will do when you launch a database is to load your data into the database. In MonetDB, the primary way of loading large amounts of data into your database is using the `COPY INTO` statement. Using the COPY INTO statement, you can quickly load large CSV files into your database.

DataFungi, from Rotting Data to Purified Information

Martin Kersten presented his idea to cope with the ever increasing big data growth in a keynote at the 32nd IEEE International Conference on Data Engineering, May 16-20, 2016, in Helsinki, Finland.

Subscribe to Blogs