Data Warehouse Automation
Automatically start a new environment within minutes
Automatically convert data models into working ELT code
Automatically convert data models into operational jobs
Built for Agile and Devops: develop, retest and deploy new features within a day
Extend jobs automatically if new elements are added to the data model
Enable automated testing
Self-generated Documentation of actual Solution

Metadata-Storage
Direct storage of metadata in database-objects
No need for separate Metadata Repository

Version Control
Every change is tracked and can be restored
A complete protocol is provided with timestamp and user information

Included Software
Modeling Tool
ETL / ELT Tool
SQL Editor
Data Profiling Tool
Interactive Documentation
Operations including Scheduler
Database in some editions

Server – Part
On Premise
Private Cloud
SAAS
Desktop: Windows, Mac, Linux

Client – Part
Runs on any platform running Chrome or Chromium
Windows all versions
Mac OS X all versions
Linux all distirubtions

Built in ELT Engine
Using the full power of the underlaying data base
Automatical parallelization of work loads
Any Data Transformation, Text parsing, Calculations based on common SQL (no new abilities needed)
Automatic generation of slowly changing dimensions

Generel Staging Features
Load Files, JDBC Databases and Rest Interface
Preview actual Source Data
Add hashes in transfer process
Delta and Full loads
Persistent staging
Snapshot all incoming rows

Non JDBC Sources
Load from CSV, TSV and other Delimiter separated Files
Load fixed width files
Load from MS Access and MS Excel Files
Load from REST Services (JSON, XML)
Load from Webpages
Load Cobol Copy Books	with 3rd party driver

JDBC Sources (extract)
Amazon Aurora
Amazon RDS
Amazon Redshift
Apache Derby
Apache Spark
IBM DB2
EXASOL
eXist-db
Firebird
Google Cloud
Greenplum
H2
HSQL DB
Informix
AmazonRDS
Ingres
InterBase
JavaDB
MariaDB
MaxDB
Microsoft SQL Server
MySQL
Netezza
Oracle
ParAccel
PostgreSQL / Postgres
PostgresPlus
SAP Hana
SAS
SQLite
Sybase
Sybase IQ
Teradata
VectorWise
Vertica
Windows Azure
… all other JDBC Data Sources

Modelling
Conceptuel Modelling
Logical Modelling integrated with Conceptual Modeling
Visually develop logical model (for business analysts without IT skill)
Automatic Technical Implementation from Logical Model

Data Vault Specific
Visually implement Hubs, Links and Satellites loads (matching ELT loads are created in real time)
Visually implement Hubs, Links and Satellites loads (matching ELT loads are created in real time)
Wizard to create Hub and Satellite including the loads by only selecting the Business Key
Automatic Creation and Evaluation of Tracking Satellites and Link Tracking Satellites

Technical Implementation
Load data with one click while modelling and get instant feed back
Tables, Indexes and Foreign Keys are created automatically
Automatic ELT Loads for SCD Type 0,1,2
Fully historized loads are created
Loads are fully audible

Browsing
Browse through actual data model
Search model by hubs, subject areas or systems
Graphically indicate data sources in the model

Basic Modeling
Data Vault using Hash Key
Hubs
Links
Satellites (automatic and manual)
Tracking Satellites and Link-Satellites created automatically

Data Vault extended modeling
Automatic Business Vault Loads
Denormalization to Dimensional Model
Bridge Tables
Point in Time Tables (PIT)

Technical Elements
Hashes automatically added to ETL Flows
All loads parallelized automatically
Tracking Satellites loaded and evaluated automatically
Full SCD Type 2 History created automatically
Full SCD Type 0 and 1 views available out of the box

Automatically denormalize business objects (facts and dimensions)
Automatic grain checks
Visual consolidation of data
Visual harmonization of column names
Taking care of all necessary join operations without writing one line of code

Add any business rule you want
Full power of SQL
Editor with autocomplete support
Build multi layer rules
Access all core layer objects

Advanced Features
Preview output and make it immediatelly available to recipients like reporting tools
Prepare complex rules for materialization in the Business Vault
Add comments which are automatically transferred into the documentation
Define Error Rulesets to assure Data quality

Automated views for missing relations and values
Create custom rules to be monitored
Based on single rows or sets

Automatically merge data from different sources
Define priority on row and column level
Reload free by providing a virtual layer

Show “Where-Used” Data Lineage from Source to Target
Lineage and Impact Analysis
Browse and Filter your Data Lineage
Access Data Lineage with your favorite Reporting Tool or integrate views in your corporate Wiki

Prepared views to identify Business Key
Create Simple Charts
Analyze Data with Heatmaps and Treecharts
Access all DWH Layers to verify your data
Access the metrics vault to analyze the load performance

Monitor current state of created loads
Automatically created Jobs for Continuous Integration
Customize automatically created Jobs
Add individual jobs
Add custom loading schedules
Easily define job dependencies
Start jobs via Rest API with your Enterprise Scheduling Tool (Enterprise Edition)
Automatic parallelization of loading processes
Trigger SQL Jobs to push the data to files or target databases

Start small – grow big
Group changes by stories
Start model from different starting points and connect them later
Loose coupling by Data Vault Modeling
Data model supports partial roll outs
Data model limits retesting to a minimum
Support for automated testing

Master jobs are created automatically
Master jobs extend if new elements are modeled
Test new elements automatically on a defined schedule (nightly builds or continuous integration)
Automated roll out package creation
Data model supports partial roll outs
Data model limits retesting to a minimum
Support for automated testing

	SMB	Enterprise
Deploy new objects automatically
Deploy changed objects automatically
Return to previous versions of the model
Simplified Deployment process directly from one environment to the other
Enterprise Deployment process using GIT Workflow
Deploy packages to different target environments / Multitenancy Support

	SMB	Enterprise
Use continous mini batches for single entities, complete systems or the full data model
Load data staged by other applications as they arrive
Offers an API to push single records into the model

	SMB	Enterprise
Built in Database (based on Postgres)
Microsoft SQL Standard Edition
EXASOL
Microsoft SQL Enterprise Edition
Oracle (12.2)