Dataiku DSS - Reference documentation

Welcome to the reference documentation for Dataiku DSS. This documentation contains:

• The main documentation of the concepts, interfaces and features of Dataiku DSS

• Information on how to install and configure Dataiku DSS

• Information for administrators on how to operate Dataiku DSS

You might also find these other resources useful:

• The Knowledge Base a variety of topics that can help you to learn more about Dataiku DSS, or find solutions to problems without having to ask for help.

• The Developer Guide contains all information for developers using Dataiku: how to code in Dataiku, how to create applications, how to operate Dataiku through its APIs, numerous code samples and examples, and reference API documentation

• The Dataiku Academy provides guided learning paths for you to follow, upskill, and gain certifications on Dataiku DSS.

• The Dataiku Community is a place where you can join the discussion, get support, share best practices and engage with other Dataiku users.

• DSS concepts
  • Homepage
  • Projects
  • Data
  • Datasets
  • Recipes
  • Building datasets
  • Managed and external datasets
  • Partitioning
• Connecting to data
  • Supported connections
  • SQL databases
  • Amazon S3
  • Azure Blob Storage
  • Google Cloud Storage
  • Upload your files
  • HDFS
  • Cassandra
  • MongoDB
  • Elasticsearch
  • File formats
  • Managed folders
  • “Files in folder” dataset
  • Metrics dataset
  • Internal stats dataset
  • “Editable” dataset
  • kdb+
  • FTP
  • SCP / SFTP (aka SSH)
  • HTTP
  • HTTP (with cache)
  • Server filesystem
  • Dataset plugins
  • Making relocatable managed datasets
  • Clearing non-managed Datasets
  • Data ordering
  • PI System / PIWebAPI server
  • Google Sheets
  • Data transfer on Dataiku Cloud
• Exploring your data
  • Sampling
  • Analyze
• Schemas, storage types and meanings
  • Definitions
  • Basic usage
  • Schema for data preparation
  • Creating schemas of datasets
  • Handling of schemas by recipes
  • List of recognized meanings
  • User-defined meanings
  • Handling and display of dates
• Data preparation
  • How to Copy Prepare Recipe Steps
  • Sampling
  • Execution engines
  • Processors reference
  • Filtering and flagging rows
  • Managing dates
  • Reshaping
  • Geographic processors
• Charts
  • The Charts Interface
  • Sampling & Engine
  • Basic Charts
  • Tables
  • Scatter Charts
  • Map Charts
  • Other Charts
  • Common Chart Elements
  • Color palettes
  • Formatting
  • Filter settings
• Interactive statistics
  • The Worksheet Interface
  • Univariate Analysis
  • Bivariate Analysis
  • Fit curves and distributions
  • Statistical Tests
  • Multivariate Analysis
  • Time Series Analysis
  • Assisted Data Exploration
• Machine learning
  • Prediction (Supervised ML)
  • Clustering (Unsupervised ML)
  • Automated machine learning
  • Model Settings Reusability
  • Features handling
  • Algorithms reference
  • Advanced models optimization
  • Models ensembling
  • Model Document Generator
  • Time Series Forecasting
  • Causal Prediction
  • Deep Learning
  • Models lifecycle
  • Scoring engines
  • Writing custom models
  • Exporting models
  • Partitioned Models
  • ML Diagnostics
  • Computer vision
  • Image labeling
• The Flow
  • Visual Grammar
  • Flow zones
  • Rebuilding Datasets
  • Limiting Concurrent Executions
  • Exporting the Flow to PDF or images
  • How to Manage Large Flows with Flow Folding
  • Flow Document Generator
• Visual recipes
  • Prepare: Cleanse, Normalize, and Enrich
  • Sync: copying datasets
  • Grouping: aggregating data
  • Window: analytics functions
  • Distinct: get unique rows
  • Join: joining datasets
  • Fuzzy join: joining two datasets
  • Geo join: joining datasets based on geospatial features
  • Splitting datasets
  • Top N: retrieve first N rows
  • Stacking datasets
  • Sampling datasets
  • Sort: order values
  • Pivot recipe
  • Generate features
  • Push to editable recipe
  • Download recipe
  • List Folder Contents
• Recipes based on code
  • The common editor layout
  • Python recipes
  • R recipes
  • SQL recipes
  • Hive recipes
  • Impala
  • Spark-Scala recipes
  • PySpark recipes
  • Spark / R recipes
  • SparkSQL recipes
  • Shell recipes
  • Variables expansion in code recipes
• Code notebooks
  • SQL notebook
  • Python notebooks
  • Predefined notebooks
  • Containerized notebooks
  • Installing Jupyter Extensions
• MLOps
  • Feature Store
  • Models evaluations
  • Model Comparisons
  • Drift analysis
  • MLflow Models
  • Experiment Tracking
• Webapps
  • “Standard” web apps
  • Shiny web apps
  • Bokeh web apps
  • Dash web apps
  • Publishing webapps on the dashboard
  • Public webapps
  • Webapps and security
  • Scaling webapps on Kubernetes
  • Introduction to DSS webapps
  • Example use cases
• Code Studios
  • Concepts
  • Requirements
  • Create your first Code Studio
  • Preparing Code Studio templates
  • Running Code Studios
  • Operations
  • Publish a Code Studio as a webapp
  • Project exports and bundles
• Code reports
  • R Markdown reports
• Dashboards
  • Dashboard concepts
  • Display settings
  • Exporting dashboards to PDF or images
  • Filters
  • Filtering a dashboard using a query parameter in the URL
  • Insights reference
• Workspaces
  • Sharing DSS objects into a workspace
  • Managing Workspaces
  • Discussions
• Data Catalog
  • Data Collections
  • Datasets & Indexed Tables
• Dataiku Applications
  • Application tiles
  • Application-as-recipe
  • Introduction
  • Using a Dataiku application
  • Developing a Dataiku application
  • Application-as-recipe
  • Sharing a Dataiku application
  • Initiating an application instantiation request
  • Managing an application execution request
• Working with partitions
  • Partitioning files-based datasets
  • Partitioned SQL datasets
  • Specifying partition dependencies
  • Partition identifiers
  • Recipes for partitioned datasets
  • Partitioned Hive recipes
  • Partitioned SQL recipes
  • Partitioning variables substitutions
  • Partitioned Models
  • The two partitioning models
• DSS and SQL
  • SQL datasets
  • SQL write and execution
  • Partitioning
  • SQL pipelines in DSS
• DSS and Python
  • Installing Python packages
  • Reusing Python code
  • Using Matplotlib
  • Using SpaCy
  • Using Bokeh
  • Using Plot.ly
  • Using Ggplot
  • Using Jupyter Widgets
• DSS and R
  • Installing R packages
  • Reusing R code
  • Using ggplot2
  • Using Dygraphs
  • Using googleVis
  • Using ggvis
  • Installing STAN or Prophet
  • RStudio integration
• DSS and Spark
  • Usage of Spark in DSS
  • Spark configurations
  • Interacting with DSS datasets
  • Spark pipelines
  • Limitations and attention points
  • Setting up Spark integration
• Code environments
  • Operations (Python)
  • Operations (R)
  • Base packages
  • Using Conda
  • Automation nodes
  • Non-managed code environments
  • Plugins’ code environments
  • Custom options and environment
  • Troubleshooting
  • Code env permissions
• Collaboration
  • Wikis
  • Discussions
  • Markdown
  • Tags
  • Working with Git
  • Version control of projects
  • Importing code from Git in project libraries
  • Importing Jupyter Notebooks from Git
  • Requests
• Time Series
  • Understanding time series data
  • Format of time series data
  • Time series preparation
  • Time series visualization
  • Time series forecasting
• Geographic data
  • Geographic data types
  • Geographic data
  • Visualizing geographic data
  • Geographic data preparation
  • Geo join
  • Geocoding and reverse geocoding
  • Georouting and isochrones
  • Geographic formula functions
• Text & Natural Language Processing
  • Language Detection
  • Named Entities Extraction
  • Sentiment Analysis
  • Translation
  • Text summarization
  • Key phrase extraction
  • Ontology Tagging
  • Spell checking
  • OpenAI GPT Text Completion
  • Machine Learning with Text features
  • OCR (Optical Character recognition)
  • Speech-to-Text
  • Text cleaning
  • Text Embedding
  • NLP using AWS APIs
  • NLP using Azure APIs
  • NLP with Crowlingo API
  • NLP using Deepl API
  • NLP using Google APIs
  • NLP with MeaningCloud API
• Images
• Audio
• Video
• Automation scenarios, metrics, and checks
  • Definitions
  • Scenario steps
  • Launching a scenario
  • Reporting on scenario runs
  • Custom scenarios
  • Variables in scenarios
  • Step-based execution control
  • Metrics
  • Checks
  • Custom probes and checks
• Production deployments and bundles
  • Setting up the Deployer
  • Creating a bundle
  • Deployment infrastructures
  • Deploying bundles with the Project Deployer
  • Manually importing bundles
• API Node & API Deployer: Real-time APIs
  • Introduction
  • Concepts
  • Installing API nodes
  • Setting up the API Deployer and deployment infrastructures
  • First API (with API Deployer)
  • First API (without API Deployer)
  • Types of Endpoints
  • Enriching prediction queries
  • Security
  • Managing versions of your endpoint
  • Deploying on Kubernetes
  • APINode APIs reference
  • Operations reference
• Governance
  • Definitions
  • Installing and setting up Govern
  • Govern Projects, Models and Bundles
  • Sign-off Scenario
  • Model Registry
  • Bundle Registry
  • Blueprint Designer
  • Custom pages
  • Public REST API
• Python APIs
• R API
  • Using the R API inside of DSS
  • Using the R API outside of DSS
  • Reference documentation
  • Authentication information
  • Creating static insights
• Public REST API
  • Features
  • Public API Keys
  • The REST API
• Additional APIs
  • The Javascript API
  • The Scala API
• Installing and setting up
  • Dataiku Cloud Stacks for AWS
  • Dataiku Cloud Stacks for Azure
  • Dataiku Cloud Stacks for GCP
  • Custom Dataiku install on Linux
  • Other installation options
• Elastic AI computation
  • Concepts
  • Initial setup
  • Managed Kubernetes clusters
  • Using Amazon Elastic Kubernetes Service (EKS)
  • Using Microsoft Azure Kubernetes Service (AKS)
  • Using Google Kubernetes Engine (GKE)
  • Using code envs with containerized execution
  • Dynamic namespace management
  • Customization of base images
  • Unmanaged Kubernetes clusters
  • Using Openshift
  • Troubleshooting
  • Using Docker instead of Kubernetes
• DSS in the cloud
  • DSS in AWS
  • DSS in Azure
  • DSS in GCP
• DSS and Hadoop
  • Setting up Hadoop integration
  • Connecting to secure clusters
  • Hadoop filesystems connections (HDFS, S3, EMRFS, WASB, ADLS, GS)
  • Hive
  • Impala
  • Spark
  • Hive datasets
  • Hadoop user isolation
  • Distribution-specific notes
  • Teradata Connector For Hadoop
  • Multiple Hadoop clusters
  • Dynamic AWS EMR clusters
  • Dynamic Google Dataproc clusters
• Metastore catalog
• Operating DSS
  • dsscli tool
  • The data directory
  • Backing up
  • Audit trail
  • The runtime databases
  • Logging in DSS
  • DSS Macros
  • Managing DSS disk usage
  • Understanding and tracking DSS processes
  • Tuning and controlling memory usage
  • Using cgroups for resource control
  • Monitoring DSS
  • HTTP proxies
  • DSS license
  • Compute resource usage reporting
• Security
  • Project Access
  • Main project permissions
  • Connections security
  • User profiles
  • Shared objects
  • Authorized objects
  • User secrets
  • Audit Trail
  • Govern Security: Roles and Permissions
  • Configuring LDAP authentication
  • Single Sign-On
  • Multi-Factor Authentication
  • Passwords security
  • Advanced security options
• User Isolation
  • Capabilities of User Isolation Framework
  • Concepts
  • Prerequisites and limitations
  • Initial Setup
  • Troubleshooting
  • Reference architectures
  • Details of UIF capabilities
  • Advanced topics
• Plugins
  • Installing plugins
  • Managing installed plugins
  • Developing plugins
• Streaming data
  • Concepts
  • Kafka
  • AWS SQS
  • HTTP Server-Sent Events
  • Continuous sync
  • Continuous Python
  • Streaming Spark Scala
• Formula language
  • Basic usage
  • Reading column values
  • Variables typing and autotyping
  • Boolean values
  • Operators
  • Array and object operations
  • Object notations
  • DSS variables
  • Array functions
  • Boolean functions
  • Date functions
  • Math functions
  • Object functions
  • String functions
  • Geometry functions
  • Value access functions
  • Control structures
  • Tests
• Custom variables expansion
  • Defining variables
  • Using variables in the code of a recipe
  • Using variables in configuration fields
  • Using override tables
  • Modifying the value of variables
• Sampling methods
  • Generic sampling methods
  • Exploration / Visual data preparation
• Accessibility
  • Global Shortcuts
  • Project Navigation
  • Within the Flow
  • Within a Dataset
  • Within a Prepare Recipe
  • Within a Code Recipe
  • Within any Recipe
  • Within any Code Editor (Excluding Notebooks)
  • Within any Flow Object
  • Within Plugins Development
  • Within a Dataset Insight
• Troubleshooting
  • Diagnosing and debugging issues
  • Obtaining support
  • Support tiers
  • Common issues
  • Error codes
• Release notes
  • DSS 12 Release notes
  • DSS 11 Release notes
  • DSS 10.0 Release notes
  • DSS 9.0 Release notes
  • DSS 8.0 Release notes
  • DSS 7.0 Release notes
  • DSS 6.0 Release notes
  • DSS 5.1 Release notes
  • DSS 5.0 Release notes
  • DSS 4.3 Release notes
  • DSS 4.2 Release notes
  • DSS 4.1 Release notes
  • DSS 4.0 Release notes
  • DSS 3.1 Release notes
  • DSS 3.0 Relase notes
  • DSS 2.3 Relase notes
  • DSS 2.2 Relase notes
  • DSS 2.1 Relase notes
  • DSS 2.0 Relase notes
  • DSS 1.4 Relase notes
  • DSS 1.3 Relase notes
  • DSS 1.2 Relase notes
  • DSS 1.1 Release notes
  • DSS 1.0 Release Notes
  • Pre versions
• Other Documentation
  • Older DSS versions
  • Other Dataiku products
• Third-party acknowledgements
  • DSS
  • Mac version only
  • Dataiku Cloud