Wendelin technology transparently scales up PyData scripts created
by data scientists to match the requirements posed by big data processing.
A simple turorial is available online: https://nexedi.com/wendelin-Core.Tutorial.2016
Wendelin.core is a library that solves the common problem
that many pydata analysts find as soon as data grows: RAM limit.
Wendelin.core implements a distributed, shared, transactional virtual
memory manager that combines the RAM and storage of a redundant array of inexpensive
servers (RAIC) as if it were a single server with huge RAM and storage
Most of the time, Wendelin.core does not require to change the source of pydata libraries.
ndarrays managed by wendelin.core appear to PyData as standard ndarrays. The use
of wendelin.core is thus transparent.
Wendelin is a industrial big data platform based
on wendelin.core library and NEO NoSQL database that
provides a ready to run production environment to deploy
PyData scripts and analyse large quantities of data.
NEO NoSQL database can be extended at runtime by adding inexpensive
servers with additional storage. Storage size thus becomes limitless.
Wendelin platform relies on fluentd standard for reliable and scalable data ingestion.
Wendelin platform includes a parallel processing engineer
based on the "Actalk" model, a generalization of MapReduce that
was actually created before MapReduce itself. Wendelin parallel
processing can be plugged into the PyData libraries such as joblib
or simply invoked directly from any script thanks to the activate()
method which distributes computation over all nodes of the cluster.
Wendelin.core supports transactional processing. This means that
there is no risk in Wendelin to process data in-place. If an in-place processing fails
due to a software or hardware error, all modifications are reverted. Data remains consistent.
Transaction support also ensures in a production system that real data is either
fully ingested or not ingested at all. Many corners cases resulting from the lack of transactions
are thus eliminated.
Nexedi provides fulls commercial support for the wendelin.core and Wendelin platforms. For example,
in some rare cases, PyData code relies
explicitely on copying data in RAM. Such code has to be modified, and eventually contributed
back to PyData community since explicit copies are generally a bad thing. Nexedi experts can
support data scientists to put their scripts in production by eliminating all programming
patterns that prevent scaling up PyData code.
Need to store Big Data reliably? Looking for experienced data analysts' advice on data handling?
Need to hit the ground running with a custom Big Data application? Nexedi is here to help!
Successfull implementation examples can be found on both Wendelin. and
For further information and individual offers for your business case please contact us through our website's contact section.
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