KNIME is a modern data analytics platform developed at the University of Konstanz since 2004 that allows users to perform sophisticated statistics, data mining, and predictive analytics on data. It combines data access, transformation, investigation, analytics, and visualization in a visual workbench. KNIME also enables developing reports and automating new insights back into production systems. The platform stores results permanently at each processing node, allowing work to be stopped and resumed while inspecting intermediate data.

1. KNIME
The Konstanz Information Miner (KNIME) is being developed by
the Nycomed Chair for Bioinformatics and Information Mining at the
University of Konstanz since 2004. It Is a modern data analytics
platform that allows to perform sophisticated statistics and data mining
on your data to analyze trends and predict potential results. Its visual
workbench combines data access, data transformation, initial
investigation, powerful predictive analytics and visualization. KNIME
also provides the ability to develop reports based on your information or
automate the application of new insight back into production systems.
KNIME Desktop is open-source .
With KNIME, the user can model work flows, which consist of
nodes that process data that is transported via connections between the
nodes. A flow usually starts with a node that reads in data from some
data source, which are usually text files, but data bases can also be
queried by special nodes. Imported data is stored in an internal table-
based format consisting of columns with a certain data type (integer,
string, image, molecule, etc.) and an arbitrary number of rows
conforming to the column specifications.
Advantages
Each node stores its results permanently and thus work flow
execution can easily be stopped at any node and resumed later on.
Intermediate results can be inspected at any time and new nodes
can be inserted and may use already created data without preceding
nodes having to be re-executed.
The data tables are stored together with the work flow structure
and the nodes' settings.

2. Disadvantages
This concept is that preliminary results are not available as
soon as possible as if real pipeling were used.