Dataiku DSS

Welcome to the reference documentation for Dataiku Data Science Studio (DSS).

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• The reference documentation 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.
• Dataiku Academy contains self-learning tutorials and use cases.
• Dataiku Community is a place where you can join the discussion, get support, share best practices and engage with other Dataiku users.

Reference Doc Contents

• Installing DSS
  • Requirements
  • Installing a new DSS instance
  • Upgrading a DSS instance
  • Updating a DSS license
  • Other installation options
  • Setting up Hadoop and Spark integration
  • Setting up Dashboards and Flow export to PDF or images
  • R integration
  • SageMaker Integration
  • Customizing DSS installation
  • Installing database drivers
  • Java runtime environment
  • Python integration
  • Installing a DSS plugin
  • Configuring LDAP authentication
  • Working with proxies
  • Migration operations
• DSS concepts
  • Data
  • Datasets
  • Recipes
  • Building datasets
  • Managed and external datasets
  • Partitioning
• Homepage
  • Navigating the Homepage
• Projects
  • How to Copy a Dataiku Project
  • Creating projects through macros
• 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
  • Managed folders
  • “Files in folder” dataset
  • Metrics dataset
  • Internal stats dataset
  • 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
• 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
  • 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
  • Pig recipes
  • Impala recipes
  • 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
• Webapps
  • “Standard” web apps
  • Shiny web apps
  • Bokeh 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
  • Introduction
  • Using a Dataiku application
  • Developing a Dataiku application
  • Application-as-recipe
  • Sharing a Dataiku application
• DSS in the cloud
  • DSS in AWS
  • DSS in Azure
  • DSS in GCP
• 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 Hadoop
  • Setting up Hadoop integration
  • Connecting to secure clusters
  • Hadoop filesystems connections (HDFS, S3, EMRFS, WASB, ADLS, GS)
  • DSS and Hive
  • DSS and Impala
  • Hive datasets
  • Multiple Hadoop clusters
  • Dynamic AWS EMR clusters
  • Hadoop user isolation
  • Distribution-specific notes
  • Teradata Connector For Hadoop
  • Dynamic Google Dataproc clusters
• DSS and Spark
  • Usage of Spark in DSS
  • Spark on Kubernetes
  • Setting up (without Kubernetes)
  • Spark configurations
  • Interacting with DSS datasets
  • Spark pipelines
  • Limitations and attention points
• 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
• Metastore catalog
  • Hive metastore (through HiveServer2)
  • Glue metastore
  • DSS as virtual metastore
• 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
• Running in containers
  • Concepts
  • Setting up (Kubernetes)
  • Unmanaged Kubernetes clusters
  • Managed Kubernetes clusters
  • Using Amazon Elastic Kubernetes Service (EKS)
  • Using Microsoft Azure Kubernetes Service (AKS)
  • Using Google Kubernetes Engine (GKE)
  • Using Openshift
  • Using code envs with containerized execution
  • Dynamic namespace management
  • Customization of base images
  • Troubleshooting
  • Using Docker instead of Kubernetes
• Collaboration
  • Wikis
  • Discussions
  • Markdown
  • Tags
  • Working with Git
  • Version control of projects
  • Importing code from Git in project libraries
• 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
• Automation node and bundles
  • Installing the Automation node
  • Creating a bundle
  • Importing a bundle
• API Node & API Deployer: Real-time APIs
  • Introduction
  • Concepts
  • Installing an API node
  • Installing the API Deployer
  • First API (without API Deployer)
  • First API (with API Deployer)
  • Types of Endpoints
  • Enriching prediction queries
  • Security
  • Managing versions of your endpoint
  • Deploying on Kubernetes
  • APINode APIs reference
  • Operations reference
• Time Series
  • Topics
• Unstructured data
  • Text
  • Images
  • Video
  • Graph
  • Audio
• Plugins
  • Installing plugins
  • Managing installed plugins
  • Developing plugins
• 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
  • Static insights
  • Jobs
  • Authentication information and impersonation
  • Importing tables as datasets
  • Wikis
  • Discussions
  • Performing SQL, Hive and Impala queries
  • Meanings
  • Users and groups
  • Connections
  • Code envs
  • Plugins
  • Dataiku applications
  • Metrics and checks
  • Other administration tasks
  • Reference API documentation of dataiku
  • Reference API documentation of dataikuapi
  • API for plugin components
• 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
• File formats
  • Delimiter-separated values (CSV / TSV)
  • Fixed width
  • Parquet
  • Avro
  • Hive SequenceFile
  • Hive RCFile
  • Hive ORCFile
  • XML
  • JSON
  • Excel
  • ESRI Shapefiles
• Security
  • Main project permissions
  • Connections security
  • User profiles
  • Exposed objects
  • Dashboard authorizations
  • User secrets
  • Audit Trail
  • Advanced security options
  • 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
• 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
  • Compute resource usage reporting
• Advanced topics
  • Sampling methods
  • Formula language
  • Custom variables expansion
• 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 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