Dataiku DSS provides a bunch of API and coding environments to access and process the data using your favorite language:
|PySpark||SparkR||Spark SQL||Scala Spark|
You can use notebooks to prototype your recipes in Python, R, SQL and Scala. These sandbox environments will help you develop iteratively and quickly. Once your code is ready, just copy it to a recipe in your flow!
Find out how to use Jupyter notebooks in DSS here.
Different projects (or even recipes within a project!) may require different versions of Python or R. You can create an arbitrary number of code environments, each of which runs a specific version of Python or R and has its own set of packages. You can then set the environment used to process code at the DSS instance, project, or recipe/notebook/web app level.
If you know how to code you can fully customize your insights by creating custom web apps! Read our tutorial on how to create web apps here, and find out more about web apps in the data visualization portal.
Whether your are a seasoned coder or a new one, visual tools can tremendously accelerate your work. Take a look at what can be done through the visual interface, especially regarding visual machine learning and the visual recipes.
Work with advanced spatial data in PostgreSQL by using the PostGIS extension.
You can define Deep Learning architectures using Keras and Tensorflow in Dataiku’s Visual Machine Learning for a variety of applications, such as image processing, text analysis, and time series, in addition to models for structured data.