DWauto­matic


DWauto­matic is a power­ful, metadata-driven tool for seam­lessly integ­rat­ing new source sys­tems into your data ware­house. The tool includes fea­tures such as delta detec­tion, ver­sion­ing, his­tor­iz­a­tion and simple trans­form­a­tions of incom­ing data, all achieved through a gen­eric metadata-driven approach.

Descrip­tion


DWauto­matic is a power­ful, metadata-driven tool for seam­lessly integ­rat­ing new source sys­tems into your data ware­house. The tool includes fea­tures such as delta detec­tion, ver­sion­ing, his­tor­iz­a­tion and simple trans­form­a­tions of incom­ing data, all achieved through a gen­eric metadata-driven approach.

The Product

What is DWauto­matic?




The devel­op­ment and main­ten­ance of ETL pro­grams is the biggest cost driver of DWH pro­jects. For this reason, many com­pan­ies have decided to use an ETL tool such as Ab Ini­tio, IBM InfoSphere Data­Stage or Inform­at­ica Power­Cen­ter. The strength of such tools lies in their abil­ity to map com­plex busi­ness logic in trans­form­a­tion pro­grams (often a core task in data ware­house projects).


In addi­tion to these tasks, how­ever, we are increas­ingly find­ing in data integ­ra­tion pro­jects the require­ment to integ­rate new source sys­tems into the DWH, where the data struc­tures in the DWH are sim­ilar to the struc­tures of the source sys­tem, but delta detec­tion, versioning/historization and simple trans­form­a­tions are to be car­ried out; in addi­tion, auto­matic detec­tion of struc­tural changes and their propaga­tion is often required. The grow­ing import­ance of these desired func­tion­al­it­ies goes hand in hand with the increas­ing spread of the data vault approach. Some ETL tools are often not par­tic­u­larly suit­able for these tasks.

Advant­ages

Included com­pon­ents !




Metadata driven


Metadata-driven imple­ment­a­tion of trans­form­a­tion and load functions


Auto­matic identification


Auto­matic iden­ti­fic­a­tion of struc­tural changes/extensions


CDC


Iden­ti­fic­a­tion of new data con­tent using CDC and own control


Gen­eric implementation


Gen­eric imple­ment­a­tion of struc­tural changes/extensions


Auto­mated


Auto­mated his­tor­iz­a­tion and ver­sion­ing of data


Mon­it­or­ing and logging


Cre­ation of an audit­able data warehouse


Data Vault mod­el­ing approach


Adap­ted to the Data Vault mod­el­ing approach


RDBMS optim­ized


Optim­ized for vari­ous RDBMS, for high-per­form­ance par­al­lel processing 

Product use cases preline

Use cases



Sup­ply chain man­age­ment and optimization


In the man­u­fac­tur­ing industry, DWauto­matic can help to seam­lessly integ­rate vari­ous data sources such as pro­duc­tion facil­it­ies, sup­pli­ers, invent­ory and sales inform­a­tion into the data ware­house. Through delta detec­tion and accur­ate his­tor­iz­a­tion, com­pan­ies can bet­ter under­stand their sup­ply chains, identify bot­tle­necks and improve efficiency.


Qual­ity con­trol and error analysis


Qual­ity assur­ance is of cru­cial import­ance in the indus­trial sec­tor. DWauto­matic enables the integ­ra­tion of data from sensors, pro­duc­tion sys­tems and qual­ity inspec­tions. With this tool, com­pan­ies can identify error pat­terns, ana­lyze qual­ity trends and optim­ize pro­cesses to pre­vent or elim­in­ate errors.


Pre­dict­ive Maintenance


By integ­rat­ing data from IoT sensors on machines and sys­tems into the data ware­house, com­pan­ies can develop pre­dict­ive main­ten­ance mod­els. DWauto­matic sup­ports this pro­cess by enabling the con­tinu­ous col­lec­tion and ana­lysis of sensor data to pre­dict main­ten­ance needs and min­im­ize unplanned downtime.

Experts

syn­vert saracus experts


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