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DATASHEETYour Path toEnterprise AI
To succeed in the world’s rapidly evolving
ecosystem, companies (no matter what theirindustry or size) must use data to continuously
develop more innovative operations,processes, and products. This means
embracing the shift to Enterprise AI, using
the power of machine learning to enhance- not replace - humans.
Dataiku is the centralized data platform thatmoves businesses along their data journeyfrom analytics at scale to Enterprise AI,
powering self-service analytics while also
ensuring the operationalization of machinelearning models in production.

Key Features
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Seamless connectivity to any data, no matter
where it’s stored or in what format.
Faster data cleaning, wrangling, mining,and visualization.
The latest in machine learning technology(including AutoML and deep learning) all in
one place and ready to be operationalizedwith automation environments, scenarios, andadvanced monitoring
Every step in the data-to-insights process can bedone in code or with a visual interface.
Enterprise-level security with fine-grained accessrights.

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DATASHEET

FeatureOverview
A Centralized,
Controlled Environment■■■

Connect to existing data storage systems and leverageplugins and connectors for access to all data from one,central location.
Maintain enterprise-level security with data governancefeatures like documentation, projects, task organization, change
management, rollback, monitoring, etc.
Reuse and automate work (via data transformations, code snippets,
centralized best practices, automated assistants, and more), to gofrom raw data to insights faster.

A Common Groundfor Experts and Explorers■■■

Built from the ground up for data scientists, analysts, IT/dataengineers, and data team managers.
From code-free data wrangling using built-in visual processors to
the visual interface for machine learning, non-technical users aren’t
left in the dust when it comes to contributing to machine learning
projects.
But with interactive Python, R, and SQL notebooks,
coders get what they want too.

A Shortcut to ML/AI
Operationalization■■

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Centrally manage models and update them from one location,
integrating with an API without having to modify or inject anything
into existing applications.
Prevent model drift with the ability to easily monitor performanceand easily make necessary adjustments.
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DATASHEET

Connectivity
Dataiku allows you to seamlessly connect to your data no matter
where it’s stored or in what format. That means easy access foreveryone - whether technical or not - to the data they need.

■SQL Databases■☑ MySQL☑ PostgreSQL☑ Vertica☑ Amazon Redshift☑ Pivotal Greenplum☑ Teradata☑ IBM Netezza☑ SAP HANA☑ Oracle☑ Microsoft SQL Server (incl. SQL DW)☑ Google BigQuery☑ IBM DB2
☑ Exasol☑ MemSQL☑ Snowflake☑ Custom connectivity through JDBC■

☑ CSV☑ Parquet☑ ORC☑ SequenceFile☑ RCFile■■

Cloud Object Storage☑ Amazon S3☑ Google Cloud Storage☑ Azure Blob Storage☑ Azure Data Lake Store Gen1 & Gen2NoSQL Databases■

Custom Data Sources - extended
connectivity through Dataiku Plugins☑ Connect to REST APIs,☑ Create custom file formats☑ Connect to databases

Hadoop & Spark Supported Distributions☑ Cloudera☑ Hortonworks☑ Google DataProc☑ MapR☑ Amazon EMR☑ DataBricks

Remote Data Sources
☑ FTP
☑ SCP
☑ SFTP☑ HTTP☑ MongoDB☑ Cassandra☑ ElasticSearch■

Hadoop File Formats■

Optimized sync between:☑ Snowflake and WASB
☑ S3 and Amazon Redshift☑ Snowflake and S3■Native Support for Snowflakein Spark Driver

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DATASHEET

Exploratory
Analytics
Sometimes you need to do a deep dive on your data, but other times,
it’s important to understand it at a glance.
From exploring available datasets to dashboarding, Dataiku makes thistype of analysis easy.

Data Analysis■■■

Data Cataloging

Automatically detect dataset schemaand data types
Assign semantic meanings to yourdatasets columns
Build univariate statistics
automatically & derive data quality
checks■■Dataset audit☑ Automatically produce data quality and statisticalanalysis of entire Dataiku datasets☑ Support of several backends for audit (in-memory,
Spark, SQL)

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Time series

Create standard charts (histogram,bar charts,etc) and scale charts’computation by leveraging underlying
systems (in-database aggregations)
Create custom charts using:☑ Custom Python-based or R-based Charts☑ Custom Web Applications (HTML/JS/CSS/Flask)☑ Shiny Web Applications ®
☑ Bokeh Web Applications (Python)

Leverage predefined Python-based
Jupyter Notebooks☑ Univariate analysis and statistical tests on a single
population.
☑ Statistics and tests on multiple populations☑ Correlations analysis☑ Principal Components Analysis☑ High dimensional data visualization (t-SNE)☑ Topic modeling☑ Time-Series analytics☑ Time series forecasting

Explore data from all your existing
connections

Data Visualization■■

Search for data, comments, features,or models in a centralized catalog.

Dashboarding■

User-managed reports and dashboards☑ RMarkdown reports☑ Jupyter Notebooks reports☑ Custom Insights (GGplot, Plotly, Matplotlib)☑ Custom interactive, web-based visualisations☑ g☑ Time series data prep with visual recipes forresampling, windowing, extrema extraction, interval
extraction☑ Time series visualization☑ Time series forecastin

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DATASHEET

Data
Preparation
Traditionally, data preparation takes up to 80 percent of the time ofa data project. But Dataiku’s data prep features make that process
faster and easier, which means more time for more impactful (andcreative) work.

Visual Data
Transformation■

Dataset Sampling■

Design your data transformation jobs
using a point-and-click interface☑ Group☑ Filter☑ Sort☑ Stack☑ Join☑ Window☑ Sync☑ Distinct☑ Top-N☑ Pivot☑ Split■■First records, random selection,
stratified sampling, etc.

Interactive Data Preparation■■■

Processors (80 built-in from simple
text processing to custom Python- or
formula-based transformations
Automatically turn data preparation
scripts into Spark or MapReduce jobs
Scale data data preparation scripts
using in-database (SQL) or in-cluster(Spark) processing

Scale your transformations byrunning them directly in distributed
computations systems (SQL, Hive,
Spark, Impala)See and tune the underlying code
generated for the task

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DATASHEET

MachineLearning
Dataiku offers the latest machine learning technologies all in one
place so that data scientists can focus on what they do best: building
and optimizing the right model for the use case at hand.

Automated Machine
Learning (AutoML)■■

Automated ML strategies☑ Quick prototypes☑ Interpretable models☑ High performance■

Features handling for machine learning☑ Support for numerical, categorical, text and vector
features☑ Automatic preprocessing of categorical features(Dummy encoding, impact coding, hashing, custom
preprocessing, etc.)☑ Automatic preprocessing of numerical features(Standard scaling, quantile-based binning, custom
preprocessing, etc.)☑ Automatic preprocessing of text features (TF/IDF,
Hashing trick, Truncated SVD, Custom preprocessing)☑ Various missing values imputation strategies+ Features generation◊ Feature-per-feature derived variables (square,square root…)◊ Linear and polynomial combinations+ Features selection◊ Filter and embedded methods■

Choose between several ML backends totrain your models☑ TensorFlow☑ Keras☑ Scikit-learn☑ XGBoost☑ MLLib☑ H20

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Algorithms☑ Python-based+ Ordinary Least Squares+ Ridge Regression+ Lasso Regression+ Logistic regression+ Random Forests+ Gradient Boosted Trees+ XGBoost+ Decision Tree
+ Support Vector Machine+ Stochastic Gradient Descent+ K Nearest Neighbors+ Extra Random Trees+ Artificial Neural Network+ Lasso Path
+ Custom Models offering scikit-learn compatible API’s(ex: LightGBM)☑ Spark MLLib-based+ Logistic Regression+ Linear Regression+ Decision Trees+ Random Forest+ Gradient Boosted Trees+ Naive Bayes+ Custom models☑ H20-based+ Deep Learning
+ GBM
+ GLM
+ Random Forest+ Naive Bayes

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DATASHEET

MachineLearning
Dataiku offers the latest machine learning technologies all in one
place so that data scientists can focus on what they do best: building
and optimizing the right model for the use case at hand.

Automated Machine
Learning (AutoML)■■■☑ Linear stacking (for regression models) or logistic
stacking (for classification problems)☑ Prediction averaging or median (for regression
problems)☑ Majority voting (for classification problems)Hyperparameters optimisation☑ Freely set and search hyperparameters☑ Cross validation strategies+ Support for several Train/test splitting policies (incl.
custom)+ K-Fold cross testing+ Optimize model tuning on several metrics (Explained
Variance Score, MAPE, MAE, MSE, Accuracy, F1 Score,
Cost matrix, AUC, etc.)☑ Interrupt and resume grid search☑ Visualize grid search results☑ Auto-Recalibration on the predicted probabilities■

Scoring capabilities☑ Real-time serverless scoring API☑ Distributed batch with Spark☑ SQL (in-database scoring)☑ Dataiku built-in engine■

Model export☑ Export trained models as a set of Java classes for
extremely efficient scoring in any JVM application.
☑ Export a trained model as a PMML file for scoring withany PMML-compatible scorer

Analyzing model training results☑ Get insights from your model+ Scored data
+ Features importance+ Model parameters+ Partial dependence plots+ Regression coefficients+ Bias and performance analysis on subpopulations☑ Publish training results to Dataiku Dashboards☑ Audit model performances+ Confusion matrix+ Decision chart+ Lift chart+ ROC curve+ Probabilities distribution chart+ Detailed Metrics (Accuracy, F1 Score, ROC-AUC Score,
MAE, RMSE, etc.)

Automatically create ensemble fromseveral models■

Automated model documentation☑ Leverage pre-built templates or create your own forstandardized model documentation without the
manual work

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DATASHEET

MachineLearning
Dataiku offers the latest machine learning technologies all in one
place so that data scientists can focus on what they do best: building
and optimizing the right model for the use case at hand.

Model Deployment■

Model versioning■

Batch scoring■

Real-time scoring

Deep Learning■☑ Expose your models through REST API’s for real-timescoring by other applications■■

Easily manage all your model
deployments☑ One-click deployment of models■■

User-defined model architecture■

Personalize training settings■

Expose arbitrary functions and models
through REST API’s☑ Write custom R, Python or SQL based functions ormodels☑ Automatically turn them into API endpoints for
operationalisation

Support for Keras with Tensorflow
backend

Support for multiple inputs for yourmodels■Support for CPU and GPU■

Support pre-trained models■

Extract features from images■

Tensorboard integration■

Docker & Kubernetes☑ Deploy models into Docker containers for
operationalisation☑ Automatically push images to Kubernetes clusters forhigh scalability☑ Works “out of the box” with Spark on Kubernetes■

Model monitoring mechanism☑ Control model performances over time
☑ Automatically retrain models in case of performancedrift☑ Customize your retraining strategies■

Logging☑ Log and audit all queries sent to your models

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DATASHEET

MachineLearning
Dataiku offers the latest machine learning technologies all in one
place so that data scientists can focus on what they do best: building
and optimizing the right model for the use case at hand.

Unsupervised Learning■

Automated features engineering (similar
to Supervised learning)■

Optional dimensionality reduction■

Outliers detection■

Algorithms☑ K-means☑ Gaussian Mixture☑ Agglomerative Clustering☑ Spectral Clustering☑ DBSCAN☑ Interactive Clustering (Two-step clustering)☑ Isolation Forest (Anomaly Detection)☑ Custom Models

Scale-out
Model Training■

Train models over Kubernetes

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OutputFeatures
After all the work of finding insights, it’s important to effectively
communicate them with stakeholders around the organization to
inspire action. Dataiku puts the power of AI in the hands of everyone
to make intelligence-driven decisions.

ChartsDashboards☑ Bar, line, curve, pie, donut, scatter, boxplot, 2D
distribution, lift

☑ Create interactive insights with charts, tables,
notebook exports, webapps and more

Dataiku WebApps☑ Maps: Scatter, binned, administrative☑ Use code to build highly customized applications thatcan be leveraged as an API for users☑ TablesDataiku Apps
☑ Create user-friendly interfaces on top of projects forusers to customize and parametrize in a few clicks andwithout code
☑ Share apps on Dataiku DSS platform as a recipe or as
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DATASHEET

Automation
Features
When it comes to streamlining and automating workflows, Dataiku
allows data teams to put the right processes in place to ensuremodels are properly monitored and easily managed in production.

Data Flow

Scenarios☑ Keep track of the dependencies between your datasets☑ Manage the complete data lineage☑ Check consistency of data, schemas or data types☑ Organize flows into zones

Partitioning☑ Leverage HDFS or SQL partitioning mechanisms tooptimize computation time

Metrics & Checks☑ Create Metrics assessing data consistency and quality☑ Adapt the behavior of your data pipelines and jobs
based on Checks against these Metrics☑ Leverage Metrics and Checks to measure potential ML
models drift over time

Monitoring☑ Trigger the execution of your data flows andapplications on a scheduled or event-driven basis☑ Create complete custom execution scenarios byassembling a set of actions to do (steps)☑ Leverage built-in steps or define your own steps
through a Python API☑ Publish the results of the scenarios to various
channels through Reporters (Send emails with custom
templates; attach datasets, logs, files,
or reports to your Reporters; send notifications
to Slack or Hipchat)

Automation Environments☑ Use dedicated Dataiku Automation nodes for
production pipelines☑ Connect and deploy on production systems (datalakes, databases)☑ Activate, use or revert multiple Dataiku project bundles☑ Track the status of your production scenarios☑ Visualize the success and errors of your Dataiku jobs

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DATASHEETCodeWork in the tools and with the languages you already know - everything can bedone with code and fully customized. And for tasks where it’s easier to use a
visual interface, Dataiku provides the freedom to switch seamlessly between thetwo.

Support of multiple
languages for coding“Recipes”☑ Python☑ R☑ SQL☑ Shell☑ Hive☑ Impala☑ Spark Scala☑ Spark SQL☑ PySpark☑ SparkR☑ Sparklyr

Create and use custom
code environments☑ Support for multiple versions of Python (2.7, 3.4, 3.5, 3.6)
☑ Support for Conda☑ Install R and Python libraries directly from Dataiku’s interface☑ Open environment to install any R or Python libraries☑ Manage packages dependencies and create reproducible
environments

Scale code execution☑ Scale your code by submitting Python or R jobs to
Kubernetes cluster, either on-premises or throughcloud services (EKS, AKS, GKE)Interactive Notebooks
for data scientists

Python & R Libraries☑ Create your own R or Python libraries or helpers☑ Share them within all the Dataiku instance☑ Easily use your pre-existing code assets☑ Benefit from Git integration to streamline development
workflowCreate reusable custom
components☑ Dataiku Plugins to package and ship complex code-based
functions in a visual interface to less-technical users☑ Extend native Dataiku capabilities through code-based
Plugins (Custom connectors, custom data preparation
processor, custom web applications for interactive
analysis and visualization, etc.)☑ Create Python-based custom steps for your Dataiku
scenarios☑ Dataiku Plugins to package and distribute arbitrarily
complex code-based functions to less-technical users

APIs☑ Manage the Dataiku platform through CLI or Python
SDK☑ Train and deploy ML models programmatically☑ Expose custom Python & R functions through REST
API’s

Leverage your favorite☑ Full integration of Jupyter notebooks with Python, R or IDE to develop and test
PySpark kernels
code☑ Use pre-templated Notebooks to speed up your work

☑ Interactively query databases or data lakes throughSQL Notebooks (support for Hive)☑ Run Jupyter Notebooks over Kubernetes☑ RStudio for R code
☑ Sublime Text
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DATASHEETCollaboration
Dataiku was designed from the ground up with collaboration in mind. Fromknowledge sharing to change management to monitoring, data teams including scientists, engineers, analysts, and more - can work faster andsmarter together.

Shared platform (fordata scientists, dataengineers, analysts,
etc.)

Share custom, codebased capabilities with
less-technical users ina visual interface

Version control

Shared code-based
components☑ Git-based version control recording all changes madein Dataiku

Knowledge management
and sharing☑ Distribute reusable code snippets for all users☑ Package arbitrary complex function, operation orbusiness logic to be used by less-technical users☑ Integrate with remote Git repositories such as Github☑ Create Wikis to document your projects☑ Engage with other users of the platform throughDiscussions☑ Tag, comment and favorite any Dataiku objects

Team Activity Monitoring
Global search to quickly
find all project
assets, plugins, wiki,reference docs, etc.
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DATASHEET

Governance & Security
Dataiku makes data governance easy, bringing enterprise-level security with
fine-grained access rights and advanced monitoring for admins or project
managers.

User profiles

Platform management☑ Integrate with your corporate workload management
tools using Dataiku CLI and APIs

Role-based access(fine-grainedor custom)

Custom policy frameworkfor data protection andexternal regulations
compliance

Authentication
management☑ Use SSO systems☑ Connect to your corporate database (LDAP, Active
Directory…) to manage users and groups☑ Implement GDPR rules and processes directly☑ Framework capabilities◊ Document data sources with sensitive information,and enforce good practices◊ Restrict access to projects and data sources withEnterprise-grade
security

sensitive information◊ Audit the sensitive information in a Dataiku

instance☑ Track and monitor all actions in Dataiku using an audit
trail☑ Authenticate against Hadoop clusters and databases
through Kerberos☑ Supports users impersonation for full traceability andcompliance

Resources management☑ Dynamically start and stop Hadoop clusters from
Dataiku☑ Control server resources allocation directly from theuser interface

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Architecture
Dataiku was built for the modern enterprise, and its architecture ensures thatbusinesses can stay open (i.e., not tied down to a certain technology) and thatthey can scale their data efforts.

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No client installation for Dataiku
users
Dataiku nodes (use dedicated Dataiku
environments or nodes to design, run,and deploy your ML applications)Integrations☑ Leverage distributed systems to scale computations
through Dataiku☑ Automatically turn Dataiku jobs into SQL, Spark,
MapReduce, Hive, or Impala jobs for in-cluster orin-database processing to avoid unnecessary data
movements or copies

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Modern architecture (Docker,
Kubernetes, GPU for deep learning)
Traceability and debugging throughfull system logs
Open platform☑ Native support of Jupyter notebooks☑ Install and manage any of your favorite Python or R
packages and libraries☑ Freely reuse your existing corporate codebase☑ Extend the Dataiku platform with custom components(Plugins - see full list)

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