DSS 6.0 Release notes

Migration notes

Migration paths to DSS 6.0

How to upgrade

It is strongly recommended that you perform a full backup of your DSS data directory prior to starting the upgrade procedure.

For automatic upgrade information, see Upgrading a DSS instance.

Pay attention to the warnings described in Limitations and warnings.

Limitations and warnings

Automatic migration from previous versions (see above) is supported, but there are a few points that need manual attention.

Graphics exports

Like with any upgrade, the graphics export feature (exporting Flow or dashboards) must be reinstalled after upgrade. For more details, on how to reinstall this feature please see Setting up Dashboards and Flow export to PDF or images

Models grid search behavior change

DSS 6.0 introduces a minor upgrade to scikit-learn which fixes a bug in the model selection feature. In some rare cases, this can cause grid searching to select a different hyperparameter value when retraining a model on the same data. For more details, please see https://scikit-learn.org/stable/whats_new/v0.20.html#sklearn-model-selection

Support removal notice

As previously announced, DSS 6.0 removes support for the prepare recipe running on the Hadoop Mapreduce engine. We strongly advise you to use the Spark engine instead.

Deprecation notice

DSS 6.0 deprecates support for some features and versions. Support for these will be removed in a later release.

  • Pig support is deprecated. We strongly advise you to migrate to Spark.
  • Support for Spark 1 (1.6) is deprecated. We strongly advise you to migrate to Spark 2. All Hadoop distributions can use Spark 2.
  • Conditional outputs on binary classification models are deprecated.

Version 6.0.1 - November 6th, 2019

DSS 6.0.1 is a minor release. For a summary of major changes in 6.0, see below

Collaboration

  • Fixed non visible discussions on articles after migration

Visual recipes

  • Add ability to rename columns when using SQL pipelines
  • Fixed S3 to Redshift fast path on S3 partitioned datasets
  • Improved support of customized metastore table name of non HDFS datasets when using Spark engine

Coding

  • Make the dkuManagedFolderCopyToLocal R function recursive
  • Fixed dkuManagedFolderCopyFromLocal R function which ignored beginning of each copied file

Webapps

  • Fixed Bokeh webapps that always reused the same port

Misc

  • Fixed possible issue when accessing a non existing table using the DSS internal metastore

Plugins

  • Fixed plugin recipes using dynamically-filled dropdowns

Version 6.0.0 - October, 24th, 2019

DSS 6.0.0 is a major upgrade to DSS with major new features.

New features

Managed Kubernetes clusters

DSS can automatically start, stop and manage for you multiple clusters running on the major cloud providers. This makes it very seamless to deploy Kubernetes clusters with very low setup and administration work.

DSS provides managed Kubernetes capabilities on:

  • Amazon Web Services through EKS
  • Azure through AKS
  • Google Cloud Platform through GKE

For more details, please see Managed Kubernetes clusters and DSS in the cloud

Managed Spark on Kubernetes

DSS can now automatically manage deployment of Spark jobs on Kubernetes. This includes automatically setting up connectivity to cloud storages, building container images, handling multiple code environments, providing security and isolation.

Thanks to this feature, you can now deploy Spark jobs on a unified Kubernetes infrastructure, handling both Spark and non-Spark jobs. Multiple Kubernetes clusters are supported.

For more details, please see Spark on Kubernetes and DSS in the cloud

Partitioned models

DSS can now build partitioned models, that is, train a separate model for each partition of an input dataset. Training separate models (also sometimes referred to as “stratified models”) is useful when you expect data to be significantly different between partitions, or when you need incrementality. For example, you may want to train one model per country, per business unit, per factory, …

Once trained, partitioned models can be used to score other partitioned data, or unpartitioned data containing partition identifiers. For more information, see Partitioned Models.

Time series visualization

DSS now includes a dynamically zoomable line chart for time series.

For more details, please see Time Series

New plugins experience

The plugins store has a brand new look, allowing you to find plugins much more easily.

We have also strongly improved the plugin installation experience, with guided steps to install plugins, code envs and other dependencies.

The plugin development experience has been overhauled for better productivity.

Plugins now feature a predefined parameters system, which allows you to reuse parameters between plugins, and to have sensitive information for plugins managed by the administrator.

For more details, please see Plugins

Support for AWS Athena and Glue metastore

DSS now supports experimental connection with AWS Athena. This connection provides the following capabilities:

  • Running interactive SQL notebooks on Athena based on previously-built S3 datasets
  • Using Athena as charts engine for S3 datasets
  • Running SQL queries on Athena based on previously-built S3 datasets (execution and data read through Athena, write through DSS)

DSS also adds support for leveraging AWS Glue as a metastore catalog.

For more details, please see DSS in AWS (overview, reference architecture), AWS Athena and Glue metastore (details).

SQL pipelines

DSS provides pipeline functionality for a flow that uses a SQL engine and consists of consecutive recipes sharing the same connection. SQL pipelines can minimize or avoid unnecessary writes and reads of intermediate datasets in a flow, thereby boosting workflow performance.

For more details, please see SQL pipelines in DSS.

Global search toolbar

A new unified contextual search toolbar has been added to the DSS navigation bar. Use it for contextual search in project objects, wikis, help topics, and much more

Pluggable algorithms

You can now add custom algorithms for the in-memory Visual ML component as plugins, making them available without any code.

For more details, please see Component: Prediction algorithm.

Pluggable webapps

Webapps can now be packaged as plugins, shared and reused.

For more details, please see Component: Web Apps.

Pluggable chart types

New chart types can now be packages as plugins, shared and reused.

Pluggable custom view for folders and models

Managed folders and models now support a concept of pluggable custom views. Use cases can include:

  • A custom view representing the content of a folder (for example, a neural network visualizer)
  • A custom view on the results of a saved model (for example, to display interpretability results)

Time series preparation

DSS provides a preparation plugin that includes visual recipes for performing the following operations on time series data:

  • Resampling into equispaced time intervals
  • Performing analytics functions over a moving window
  • Extracting aggregations around a global extremum
  • Extracting intervals where values lie within an acceptable range

This plugin is fully supported by Dataiku. For more details, please see Times series preparation.

Native Python processor in preparation

The Python processor in data preparation can now use a real Python process, which allows usage of Python 3 and of any additional package through the usage of the DSS code environments feature.

The Python processor now supports vectorized operation using Pandas for fast operation.

For more details, please see Python function

Scenario reporting to Microsoft Teams

Scenarios can now report on completion and custom events to Microsoft Teams.

Other notable enhancements

Improved project folders

The project folders feature has been strongly enhanced with the following capabilities:

  • Drag & drop to add folders in projects on the “projects list” page
  • Ability to view project folders on the personalized home page
  • Security on project folders
  • Ability to have empty project folders
  • Per-folder view of the graph of projects

Time-aware cross-validation and evaluation

When running prediction tasks on time-oriented datasets (for example, a daily sales dataset), it is useful to use time-aware cross-validation for optimizing and evaluating your model. This allows you to ensure that by only looking at past data, your model is able to adequately predict future data.

For more details, please see Advanced models optimization.

Enhanced Snowflake integration

Thanks to the new native Spark integration, you can now directly access Snowflake datasets in any Spark-powered recipe (either visual or code). This leverages the native Spark Snowflake connector for optimal performance.

In addition, the Sync recipe can now perform fast synchronization between Azure Blob and Snowflake datasets.

For more details, please see Snowflake

ADLS gen2 support in Azure dataset

The Azure dataset now supports access to data using ADLS gen2

Python 3 support for base env

It is now possible to use Python 3 as the builtin environment of DSS.

Note that we do not currently recommend migrating existing instances to this mode due to the need to ensure that all user code using the builtin environment is compatible with Python 3.

New field types for plugins

Plugin components can now declare string lists, dates, and many other new kinds of fields.

For more details, please see Parameters

Redesigned contextual right panel

The right column panel available in Flow, objects lists and object actions has been redesigned to provide faster and more efficient access to the most common actions and information.

Support for HANA Calculation views

The HANA support can now list and read calculation views. The connection explorer can automatically filter by HANA package.

Managed standalone Hadoop libraries

Dataiku now provides fully-managed standalone Hadoop and Spark libraries, allowing full support for Parquet, ORC, S3, ADLS gen1 and gen2, GS, … without any cluster or 3rd party integration required

More native support for Amazon ECR

DSS now natively handles ability to push images to Amazon ECR, removing need for a custom script

Other enhancements and fixes

Hadoop & Spark

  • Added ability to access shared datasets in Pyspark notebooks
  • Added ability to select Hive runtime configuration for exploration and direct read through DSS

Datasets & file formats

  • Added support for ElasticSearch 7
  • Added ability to support ElasticSearch mapping type _doc
  • Added ability to rename columns when importing an Excel file
  • Fixed Snowflake synchronization failure with special characters
  • Fixed Excel export when running on Java 11
  • Fixed reading of booleans in Excel files
  • Fixed “click to configure” button on “Analyze on full data”

Data preparation

  • Added SQL compatibility for the “Round” processor

Flow

  • Added support for Spark engine on SQL input datasets

Visual recipes

  • Split recipe: fixed “drop data” in random dispatch mode on Spark engine
  • Sort recipe: fixed on MS SQL Server
  • Sync recipe: improved S3 to Redshift fast-path on partitioned datasets

Notebooks

  • Automatically install by default Jupyter kernels for containerized execution when updating a code env

Machine learning

  • Fixed UI of prediction and clustering recipes when running on HDFS datasets
  • Better variables ordering for Partial Dependencies Plot
  • Added subsampling on Partial Dependencies Plot and Subpopulation Analysis for faster results
  • Improved performance of Deep Learning training
  • Added support for Partial dependencies and subpopulation analysis on containers
  • Fixed possible non-stability across trainings when using Python 3
  • Added error percentage as a metric that can be output as part of the evaluation recipe

Webapps

  • Fixed issues when exporting/importing projects containing webapps

Automation

  • Added support for variables expansion in SQL triggers
  • Added ability to execute or not, and to create new exports or not when attaching Jupyter notebooks to mails

Collaboration

  • Fixed sending of Slack notifications on job builds
  • Added back “description icon” on Flow

Reliability & Scalability

  • Improved Oracle insertion performance in presence of NULL values
  • Fixed potential issues while reading enormous log files

Security

  • Fixed and clarified issues with code env permissions

API

  • Added ability to terminate a cluster through Python API
  • Fixed ability to update R code environments through API