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Dataiku Developer Guide
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  • Getting started
    • Introduction
    • Development environment
    • Basic workflow
    • MLOps lifecycle
    • The Dataiku Python APIs
    • Quickstart Tutorial
      • Step 1: Prepare the input dataset for ML modeling
      • Step 2: Test different Machine Learning models for heart failures prediction
      • Step 3: Create a Dataiku Saved Model using the best-performing model
  • Concepts and examples
    • Datasets
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      • API for plugin recipes
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      • Retrieve AWS Credentials from Dataiku API
    • AI & Machine Learning
      • Generative AI - NLP
        • Programmatic RAG with Dataiku’s LLM Mesh and Langchain
        • Using LLM Mesh to benchmark zero-shot classification models
        • GPT-based zero-shot text classification with the OpenAI API
        • GPT-based few-shot classification with the OpenAI API
        • Comparing zero-shot learning and few-shot learning using Dolly for efficient text classification
        • Using Langchain, Chroma, and GPT for document-based retrieval-augmented generation
      • Experiment Tracking
        • Experiment Tracking with the PythonModel module
        • Experiment Tracking for NLP with Keras/Tensorflow
        • Experiment tracking with Catboost
        • Experiment tracking with LightGBM
        • Experiment tracking with scikit-learn
      • Pre-trained Models
        • Load and re-use a TensorFlow Hub model
        • Load and re-use a PyTorch model
        • Load and re-use a Hugging Face model
        • Load and re-use a SentenceTransformers word embedding model
        • Load and re-use a spaCy named-entity recognition model
        • Load and re-use an NLTK tokenizer
      • Model Import
        • Importing serialized scikit-learn pipelines as Saved Models for MLOps
      • Model Export
        • Wrapping an exported model in a CLI tool
    • Data Engineering
      • Leveraging SQL in Python & R
      • Using Snowpark Python in Dataiku: basics
      • Using Databricks Connect Python in Dataiku: basics
      • Data quality assessments (SQL Datasets)
    • Developer tools
      • Setting up the Dataiku API local environment
      • Usage basics for the Dataiku Python API
      • VSCode extension for Dataiku DSS
      • PyCharm plugin for Dataiku DSS
      • Using VSCode for Code Studios
      • Using JupyterLab in Code Studios
      • Using RStudio in Code Studios
      • Running unit tests on project libraries
      • Using the API to interact with git for project versioning
    • Plugins development
      • Foreword
      • Creating and configuring a plugin
      • Setting up a dedicated instance for developing plugins
      • Git integration for plugins
      • Datasets
        • Creating a plugin Dataset component
      • File Format
        • Writing a plugin File Format component to allow ICal import in Dataiku
      • Macros
        • Creating a plugin Macro component
        • Writing a macro for project creation
        • Writing a macro for managing regression tests
      • Prediction algorithm
        • Creating a plugin Prediction Algorithm component
      • Recipes
        • Creating a plugin Recipe component
        • Writing a custom recipe to remove outliers from a dataset
      • Webapps
        • Turning an existing webapp into a webapp plugin component
    • Webapps
      • Common subjects on webapps
        • Impersonation with webapps
        • Accessible resources from webapps
      • Standard (HTML, CSS, JS)
        • HTML/CSS/JS: your first webapp
        • Adapt a D3.js Template in a Webapp
        • Use Custom Static Files (Javascript, CSS) in a Webapp
        • Upload to Dataiku DSS in a Webapp
        • Download from a Dataiku DSS Webapp
        • How to create a form for data input?
        • Simple scoring application
      • Bokeh
        • Bokeh: Your first webapp
      • Dash
        • Create an empty dash webapp
        • Dash: your first webapp
        • Create a simple admin project dashboard using Dash.
        • Create a simple multi-page webapp
        • Uploading or downloading files with Managed Folders in Dash
        • How to create a form for data input?
        • Using Dash and Azure OpenAI to build a GPT-powered web app assistant
        • Simple scoring application
      • Gradio
        • Gradio: your first web application
      • Streamlit
        • Streamlit: your first webapp
      • Use your own framework.
        • Basic setup: Code studio template
        • Basic setup: Quickstart with Angular & Vue Templates
        • Basic setup: Deploy your web application
        • Advanced setup: Code Studio template creation
        • Advanced setup: Web application creation and publication
  • API Reference
    • Python
      • Index
      • Datasets
      • Managed folders
      • The main API client
      • Interaction with Pyspark
      • Projects
      • Recipes
      • Scenarios
      • Flow creation and management
      • LLM Mesh
      • Machine learning
      • Experiment Tracking
      • Statistics worksheets
      • API Designer & Deployer
      • Project Deployer
      • Static insights
      • Jobs
      • Authentication information and impersonation
      • Importing tables as datasets
      • Wiki
      • Discussions
      • Performing SQL, Hive and Impala queries
      • SQL Query
      • Project libraries
      • Meanings
      • Users and groups
      • Connections
      • Scenarios (in a scenario)
      • Code envs
      • Plugins
      • Project folders
      • Macros
      • Dataiku applications
      • Feature Store
      • Streaming endpoints
      • Unified Monitoring
      • Metrics and checks
      • Model Evaluation Stores
      • Other administration tasks
      • Utilities
      • Index of the dataiku package
      • Index of the dataikuapi package
      • API for plugin components
        • API for plugin recipes
        • API for plugin datasets
        • API for plugin formats
        • API for plugin FS providers
      • Clusters
      • Code Studios
      • Fleet Manager
      • Govern
      • Workspaces
      • Data Collections
      • Data Quality
      • Webapps
      • Dashboards
      • Insights
      • Messaging channels
You are viewing the developer guide for version 12 of DSS.
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  • Concepts And Examples
  • Api For Plugin Components
  • Api For Plugin Datasets
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API for plugin datasets#

Reference documentation#

dataiku.connector.Connector(config[, ...])

The base interface for a Custom Python connector

dataiku.connector.CustomDatasetWriter()

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API for plugin formats
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