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Apartament Grecia - Kavala

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Ce platforme de magazine online cu arhitectura ok se gasesc in prezent?

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3 replies to this topic

#1
dani.user

dani.user

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Una ce mi-a atras atentia:

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#2
MartinAdelberg

MartinAdelberg

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Mai explicit la ce te referi ? Arhitectura custom made sau pre-built ?

#3
MartinAdelberg

MartinAdelberg

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Arhitecturi clasice:
Aici avem  3 mari implementari:

de la Microsoft: business-logic: ASP .NET,  back-end: MS SQL Server, front-end: custom-made(javascript/jQuery/etc) sau Angular (MVW Framework)

Open source:
1).business-logic: JAVA-EE/Spring,  back-end: ANY,  front-end: custom-made(javascript/jQuery/etc) sau Angular   like framework
2)business-logic: Laravel/Simphony,  back-end: ANY,  front-end: custom-made(javascript/jQuery/etc) sau Angular   like framework

Arhitecturi moderne(cloud):
Open source, Google Based:
1)
business logic: Play framework(Scala based framework) with Python on a  GCP Cloud DataProc Cluster, serverless on- premise,  cu scalabilitate pentru trafic prin
Kubernetes containers via Preemptible VMs on-demand.(Google's AI managed traffic flow via  kubernertes pt aplicatia ta)
data ingestion batching: another GCP Cloud DataProc cluster  cu pipeline-uri ETL  via GCP Cloud DataFusion  sau create programatic in  Apache Beam via Scala/Python.
data ingestion streaming: another GCP Cloud DataProc cluster  cu pipeline-uri ETL  via GCP Cloud Pub/Sub. Topicurile /Stivele sunt controlate prin programe (streaming pipelines)
facute in Python sau Java.
2)
front-end: ANY on a GCP Compute Engine VM Instance

3)
back-end:
temporary storage: Google Cloud Storage(bucketing zone)
persistent storage: Google Big Query Serverless Cloud DataWarehouse si/sau Big Query Bigtable
Pentru date nestructurate(NO-SQL) alegi prelucrari prin Google Big Query  -> Bigtable (aici, mare atentie la alegerea coloanelor de partitiionare si clusterizare pentru a preveni hotspotting-ul, doarece esti intr-un mediul NO-SQL)
Pentru date structurate alegi Google Big Query -> Big Query Table
Pentru date de tip Log/statistici pe baza unor metadate: ELK stack via un alt Google DataProc cluster, on demand.
4) Serviciul de raportari(BI)
GCP Cloud DataProc cluster(cel de la punctul 1)  avand   Google BigQuery Engine  API si Google BI Engine API.

raportari si servicii de raportare BI: Google Data Studio Dashboards(optional cu suport BI Engine daca se doreste) si Kibana Dashboards(Timelion/Canvas/ dashboards and real-time log statistics via Kibana's Discover service)

arhitectura moderna =  majoritatea corporatiilor sunt in tranzit catre aceasta arhitectura, parasind arhitectura veche.

Edited by MartinAdelberg, 24 December 2019 - 15:05.


#4
MarianG

MarianG

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una utilizabila nenea Martin

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