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Dataiku Developer Guide
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  • Getting started
    • Introduction
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    • The Dataiku Python packages
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      • 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
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        • Using LLM Mesh to benchmark zero-shot classification models
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        • Voilà: First application
        • Voilà: LLM based agent
    • Machine Learning
      • Using LIME for Model Explainability
      • Predictive Model Maintenance for Binary Classification
      • Using Reinforcement Learning for Hyperparameter Tuning
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        • 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
        • Deploying a model to an edge device with AWS IoT Greengrass
      • Distributed ML Training using Ray
    • Plugins development
      • Foreword
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      • Custom agent
        • Creating a custom agent
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        • Creating a custom tool
      • LLM Guardrails
        • Creating an LLM Guardrail
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        • Git integration for plugins
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      • 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
      • Processor plugin component
        • Creating a plugin Processor component
      • 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
        • Accessible resources from webapps
        • Creating an API endpoint from webapps
        • Querying the LLM from an headless API
        • Impersonation with webapps
      • Bokeh
        • Bokeh: Your first webapp
      • Dash
        • Create an empty dash webapp
        • Dash: your first webapp
        • Display a Bar chart using Dash
        • 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?
        • Simple scoring application
        • Creating an API endpoint for using an LLM-Based agent
        • Using Dash and LLM Mesh to build a GPT-powered web app assistant
        • Creating a Dash application using an LLM-based agent
      • Gradio
        • Gradio: your first web application
        • Creating a Gradio application using an LLM-based agent
      • 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
      • 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
      • Voilà
        • Voilà: your first web application
        • Creating a Voilà application using an LLM-based agent
  • API Reference
    • Python
      • Index
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      • 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
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      • Connections
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      • Code envs
      • Plugins
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      • Streaming endpoints
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      • Databricks Connect
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      • 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 13 of DSS.
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SQL Query#

Reference documentation#

dataikuapi.dss.sqlquery.DSSSQLQuery(client, ...)

A connection to a database or database-like on which queries can be run through DSS.
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Performing SQL, Hive and Impala queries
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