Perhaps a bit late on this post, but there is a somewhat hack-ish way to load the Julia kernel inside Dataiku. Please note that I have only accomplished this on a single-user setup, although it is likely to work similarly for a multi-user setup.
julia -E 'using Pkg; Pkg.add("IJulia")'This will install the IJulia package and add the IJulia kernel to Dataiku's list of kernels when run
In addition, it is possible using the PyCall and DataFrames packages to load Dataiku data into Julia. It may be possible to save it back in a similar fashion, but I have not attempted this yet.
For loading Dataiku datasets, the following template should work, but requires the PyCall.jl, Pandas.jl, and DataFrames.jl packages to be installed.
#this forces PyCall to reference the built-in python environment for Dataiku
ENV["PYTHON"] = "/data/dataiku/bin/python" Pkg.build("PyCall")
using PyCall, Pandas, DataFrames
#load Dataiku Python libraries
dataiku = pyimport("dataiku")
pd = pyimport("pandas")
#load the dataset into both a Pandas dataframe and a Julia Dataframes dataframe
mydataset = dataiku.Dataset("test")
pd_df = Pandas.DataFrame(mydataset.get_dataframe())
jl_df = DataFrames.DataFrame(df)
#test the load