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Monolith vs SOA vs Microservices vs serverless vs edge

https://rubygarage.org/blog/monolith-soa-microservices-serverless

Monolith

Pros

  • Single self-contained
  • Simpler
    • deployment
  • Fewer cross-cutting concerns
  • Better performance
    • no need for 40 API calls between services

Cons

  • Codebase becomes sphaghetti
    • tight coupling
    • harder to add new features
    • hard to onboard new devs
  • Difficult to adopt new technologies
    • can break things if you update the Django version
  • less reliable
    • breaking one thing can break everything

What it looks like

  • Front End: React
  • Monolithic with all the business logic
  • DB

When to use monolith

  • Forma
    • to get stuff working asap
  • For most use cases????

SOA: Service Oriented Arch

  • Reusable components
    • ????
  • Enterprise Scope
  • Shared ESB
    • Enterprise Service Bus
  • Uses SOAP, AMQP, MSMQ
  • Example: bank,

Pros

  • Reusability of services
  • Better maintainability
    • modules that can be maintainted without knowing the whole system
  • Higher reliability
  • Parallel development

Cons

  • Complex management
  • High investment costs
  • Extra overload

Elastic Load Balance

AWS API Gateway

Services:

Deploy on EC2 (sever) or AWS Lambda (serverless)

Ex for farming sector:

- Soil lab 1

- Soil lab 2

- Farm

- Accountant

- Market

RDS/Redshift,Redis,DynamoDB S3, Glacier

Caching

CDN

Kafka

Sharding

Fraud Detection

Microservice - Reusable components. Application Scope. Uses REST, JMS

Pros:

Easy to develop, test, and deploy

Increased agility

Ability to scale horizontally

Cons:

Complexity

Security concerns

Different programming languages

Architecture:

Front End - Angular?

Elastic Load Balance

AWS API Gateway

Microservices:

Deploy on EC2 (sever) or AWS Lambda (serverless)

Ex for Instagram:

- Upload

- View

- Search

- Account

- Follow

- Trending

- Monitoring

RDS/Redshift,Redis,DynamoDB S3, Glacier

Caching

CDN

Kafka

Sharding

Fraud Detection

Serverless - Cloud computing, no infrastructure management. Uses FaaS (Frontend as a service) and BaaS (Backend as a Service). Accomplishing one-time tasks and auxiliary processes

Pros:

Easy to deploy

Lower costs (No DB, some logic, servers)

Enhanced scalability

Cons:

Vendor lock-in

Not for long-term tasks

Web Service Communication Protocols:

SOAP - ACID compliant, security, stateful

REST - Performs better, caching, less refactoring headache, stateless

graphQL - Quickly evolving product iterations, over/under fetch, stateless

Services May Include Machine Learning:

https://duckduckgo.com/?q=sagemaker+pipeline&t=brave&iax=images&ia=images&iai=https%3A%2F%2Fdatabricks.com%2Fjp%2Fwp-content%2Fuploads%2F2020%2F06%2Fblog-aws-sessions-1.png

Preprocess using PySpark, Build and Train Using Sagemaker, Deploy to EC2

Classifier:

Random Forest -> Easy to overfit

Logistic Regression

Gradient Boost -> Longer to run, overfit

NN -> long train time, need lots of data. Complex hyperparameter

SVC

Naive Bayes

Regression:

RFR

LR

Decision Tree

Gradient Boosting

NN

Recommendation:

SVD -> Collaborative. Example: TasteWright.

Cosine Similarity -> Content. Example: Amazon Products

Hyrbid -> Use both. Example: Netflix

Clustering:

K-means

Images:

CNN

Text:

LDA

NN -> LSTM, CNN, Transformers

Exaples:

Banking (SOA, SOAP):

https://www.dragon1.com/images/ibm-reference-soa-for-banking.png

Farming (SOA, SOAP):

https://www.researchgate.net/figure/Three-layered-SOA-architecture-with-some-illustrative-examples-of-components-from-the_fig3_221953757

Uber (Microservice, REST):

https://media.geeksforgeeks.org/wp-content/cdn-uploads/20201120210648/Uber-System-Design-High-Level-Architecture.png

Instagram (Microservice, REST):

https://www.thetechplatform.com/post/system-design-instagram

Shopping (Microservice, GraphQL):

https://miro.medium.com/max/4000/0*VNcIhiztbake81ex.png

Random (Microservice, GraphQL):

https://cdn-images-1.medium.com/max/2000/1*up1Yb8Kn1rWJGCn1bFYKxg.png

Medical Image Analysis Machine Learning (Serverless):

https://d2908q01vomqb2.cloudfront.net/c5b76da3e608d34edb07244cd9b875ee86906328/2020/12/09/Picture3-2.png

Pipeline Automation Machine Learning (Serverless):

https://miro.medium.com/max/2400/1*3mB27NYBVWEmwhus-w3ULw.png

Sagemaker VS DataBricks

DataBricks is a better platform for Big data(Scala, PySpark) Developing.(unbeatable notebook environment)

SageMaker is better for Deployment. and if you aren't working on big data, SageMaker is a perfect choice working with (Jupyter notebook + SKLearn + Mature containers + Super easy deployment).

SageMaker provides "real time inference," very easy to build and deploy, very impressive. you can check the official SageMaker Github. https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/scikit_learn_inference_pipeline


Last update: 2022-11-04