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Com­mu­nic­a­tions, Media & Telecom  - Design and Devel­op­ment of a Google Cloud-Based Data Platform

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Com­mu­nic­a­tions, Media & Telecom

By devel­op­ing a Data Lake in Google Cloud and imple­ment­ing best prac­tices in soft­ware and data engin­eer­ing, synvert achieved faster data access and more effi­cient data aggreg­a­tion for ana­lyt­ics, lead­ing to sig­ni­fic­ant time sav­ings and a higher fre­quency of reg­u­lar busi­ness reports, enabling timely cor­por­ate decisions.

Ini­tial situation

The cus­tom­er’s data was stored in an Oracle data­base, but not all teams had access to cent­rally stored data, e.g., web­site data was miss­ing, and there were data silos for each depart­ment, mak­ing cross-depart­mental ana­lyses and report­ing difficult.

The data­base was over­loaded and too com­plex, mak­ing data ana­lysis access slow and requir­ing reports to be pro­cessed in pieces, lim­it­ing the fre­quency of avail­able reports.

Archi­tec­ture

The data lake archi­tec­ture is built on Google Cloud, using tech­no­lo­gies such as Git­lab, dbt, and Google BigQuery.

Gen­er­ated bene­fits

By improv­ing the data ware­house, synvert achieved faster data access and more effi­cient data aggreg­a­tion for ana­lyses. This leads to sig­ni­fic­ant time sav­ings and increased fre­quency of reg­u­lar busi­ness reports, enabling timely cor­por­ate decision-making.

Users are able to cre­ate their own reports in Power BI, with all depart­ments eas­ily and quickly access­ing the same data lake, redu­cing com­plex­ity and time to insights. Most data trans­form­a­tions are already per­formed in the data lake using BigQuery and dbt, allow­ing depart­ments to adjust data and busi­ness logic company-wide.

Ser­vices accom­plished by synvert 

synvert developed a data lake in Google Cloud, and best prac­tices in soft­ware and data engin­eer­ing were intro­duced. This included Git repos­it­or­ies, CI/CD, object-ori­ented pro­gram­ming, lint­ing, and test auto­ma­tion. Data qual­ity tests and mod­el­ing accord­ing to Data Vault 2.0 were also imple­men­ted. Data Mesh enabled users to per­form cross-domain data ana­lyses. Addi­tion­ally, internal con­sult­ants were suc­cess­fully trained in the afore­men­tioned tech­no­lo­gies and methods.

Categories
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Banking
Communications, Media & Telecom
Consumer Goods & Retail
Energy & Resources
Health & Life Science
Industrials
Insurance
Public Sector
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Auto­matic clas­si­fic­a­tion of cars by their model
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Oper­a­tional Work/Support in Data Warehousing
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Migra­tion AWS data plat­form to Google Cloud
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Design and Devel­op­ment of a Google Cloud-Based Data Platform
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Refact­or­ing, shift and new devel­op­ment of Data Ser­vices in Google Cloud

Estab­lish­ment of a Mas­ter Data Man­age­ment Foundation
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Cent­ral Data Lake

Replace­ment of a com­plex IBM Data­stage structure

CRM ana­lyt­ics and lead management

Stand­ard­iz­a­tion of eval­u­ation processes

Devel­op­ment of an ERP data hub

Estab­lish­ment and fur­ther devel­op­ment of a DWH

Devel­op­ment of a group-wide BI platform

Estab­lish­ment of a new sales report­ing system

Source data migra­tion with auto­ma­tion of the source data connection

Cloud self-ser­vice BI for data-driven cor­por­ate management

Devel­op­ment of vari­ous ML use cases with AWS

Replace­ment of an in-house soft­ware development

Com­pany-wide data integration

Imple­ment­a­tion of real-time data links
Con­sumer Goods & Retail

Smart Data Catalog

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