Dataiku DSS

Welcome to the Product Documentation for Dataiku Data Science Studio (DSS). This site contains information on the details of installing and configuring Dataiku DSS in your environment, using the tool through the browser interface, and driving it through the API.

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Reference Doc Contents

• DSS concepts
  • Homepage
  • Projects
  • Data
  • Datasets
  • Recipes
  • Building datasets
  • Managed and external datasets
  • Partitioning
• Connecting to data
  • Supported connections
  • Upload your files
  • Server filesystem
  • HDFS
  • Amazon S3
  • Google Cloud Storage
  • Azure Blob Storage
  • FTP
  • SCP / SFTP (aka SSH)
  • HTTP
  • SQL databases
  • Cassandra
  • MongoDB
  • Elasticsearch
  • File formats
  • Managed folders
  • “Files in folder” dataset
  • Metrics dataset
  • Internal stats dataset
  • kdb+
  • HTTP (with cache)
  • Dataset plugins
  • Data connectivity macros
  • Making relocatable managed datasets
  • Data ordering
• 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 processing
• Charts
  • The Charts Interface
  • Sampling & Engine
  • Basic Charts
  • Tables
  • Scatter Charts
  • Map Charts
  • Other Charts
  • Common Chart Elements
  • Color palettes
• Interactive statistics
  • The Worksheet Interface
  • Univariate Analysis
  • Bivariate Analysis
  • Fit curves and distributions
  • Correlation matrix
  • Statistical Tests
  • Principal Component Analysis (PCA)
• 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
  • Deep Learning
  • Models lifecycle
  • Scoring engines
  • Writing custom models
  • Exporting models
  • Partitioned Models
  • ML Diagnostics
  • ML Assertions
• 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
• 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
  • Splitting datasets
  • Top N: retrieve first N rows
  • Stacking datasets
  • Sampling datasets
  • Sort: order values
  • Pivot recipe
  • Download recipe
• 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
• 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 reports
  • R Markdown reports
• Dashboards
  • Dashboard concepts
  • Display settings
  • Exporting dashboards to PDF or images
  • Insights reference
• Dataiku Applications
  • Application tiles
  • Application-as-recipe
  • Introduction
  • Using a Dataiku application
  • Developing a Dataiku application
  • Application-as-recipe
  • Sharing a Dataiku application
• 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
• 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
• Python APIs
  • Using the APIs inside of DSS
  • Using the APIs outside of DSS
  • Datasets (introduction)
  • Datasets (reading and writing data)
  • Datasets (other operations)
  • Datasets (reference)
  • Managed folders
  • Interaction with Pyspark
  • The main DSSClient class
  • Projects
  • Project folders
  • Recipes
  • Interaction with saved models
  • Scenarios
  • Scenarios (in a scenario)
  • Flow creation and management
  • Machine learning
  • Statistics worksheets
  • API Designer & Deployer
  • Project Deployer
  • Static insights
  • Jobs
  • Authentication information and impersonation
  • Importing tables as datasets
  • Wikis
  • Discussions
  • Performing SQL, Hive and Impala queries
  • SQL Query
  • Meanings
  • Users and groups
  • Connections
  • Code envs
  • Plugins
  • Dataiku applications
  • Metrics and checks
  • Other administration tasks
  • Utilities
  • Reference API documentation of dataiku
  • Reference API documentation of dataikuapi
  • API for plugin components
  • Clusters
• 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
  • 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
  • Main project permissions
  • Connections security
  • User profiles
  • Exposed objects
  • Dashboard authorizations
  • User secrets
  • Audit Trail
  • Advanced security options
  • Configuring LDAP authentication
  • Single Sign-On
  • Multi-Factor Authentication
  • Passwords security
• User Isolation
  • Capabilities of User Isolation Framework
  • Concepts
  • Prerequisites and limitations
  • Initial Setup
  • 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
  • Continous sync
  • Continuous Python
  • Streaming Spark Scala
• Time Series
  • Topics
• Unstructured data
  • Text
  • Images
  • Video
  • Graph
  • Audio
• 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
  • 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 any Recipe
  • Within any Code Editor (Excluding Notebooks)
  • Within any Flow Object
  • Within Plugins Development
• Troubleshooting
  • Diagnosing and debugging issues
  • Obtaining support
  • Support tiers
  • Common issues
  • Error codes
• 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
  • Java libraries
  • Python libraries
  • Scala libraries
  • Native libraries
  • R libraries
  • Frontend libraries
  • Data
  • For macOS version only