How statistics are calculated
We count how many offers each candidate received and for what salary. For example, if a SAP developer with Microservice Architecture with a salary of $4,500 received 10 offers, then we would count him 10 times. If there were no offers, then he would not get into the statistics either.
The graph column is the total number of offers. This is not the number of vacancies, but an indicator of the level of demand. The more offers there are, the more companies try to hire such a specialist. 5k+ includes candidates with salaries >= $5,000 and < $5,500.
Median Salary Expectation – the weighted average of the market offer in the selected specialization, that is, the most frequent job offers for the selected specialization received by candidates. We do not count accepted or rejected offers.
Trending SAP tech & tools in 2024
Where is Microservice Architecture used?
Netflix's Binge-Buffering Bonanza
- Netflix slays the streaming game by micro-managing microservices, letting couch potatoes seamlessly jump from "Stranger Things" to "The Crown" without hiccups.
Amazon's Shopping Cart Juggernaut
- Microservices run the show at Amazon, making sure your shopping spree goes from 'Add to Cart' to 'It's at your doorstep' without a glitch in the matrix.
Spotify's Groovy Tunes Mixer
- Spotify tunes into microservices to serve up hot playlists and podcasts without skipping a beat, no matter how many dance moves you throw down.
Uber's Cab-Conjuring Wizardry
- With a flick of microservices magic, Uber conjures rides from thin air, making you feel like a VIP summoning your chariot at a tap's notice.
Microservice Architecture Alternatives
Monolithic Architecture
A single-tiered software application where the user interface and data access layers are combined into a single program from a single platform. Example: Traditional WordPress site.
- Simple to deploy and debug.
- Can suffer from scalability issues.
- Tightly coupled components hinder flexibility.
// Monolithic execution of a function
function performAction() {
// Access database
// Process data
// Return response
}
Serverless Architecture
Cloud computing execution model where the cloud provider runs the server, dynamically managing the allocation of machine resources. Example: AWS Lambda functions.
- Auto-scaling and pay-per-use.
- Limited runtime can affect complex applications.
- Vendor lock-in could be a risk.
// Serverless execution with AWS Lambda
exports.handler = async (event) => {
// Your code here
return response;
};
Service-Oriented Architecture (SOA)
Design pattern where services are provided to other components by application components, through a communication protocol over a network. Example: Web services built with SOAP.
- Easy integration with legacy systems.
- Greater overhead for XML and SOAP.
- Can lead to bloated service interfaces.
// SOA service endpoint interface example
@WebService
public interface MyService {
@WebMethod
String performService(String input);
}
Quick Facts about Microservice Architecture
Microservice Architecture: The Jigsaw of Software
Picture building a software skyscraper with Lego blocks—that's Microservices for you, folks! Born from the need to escape the monolithic quicksand around 2011, these tiny, independently-operating codebots allow developers to update a part without collapsing the whole digital edifice. Unlike the clingy Monolith, Microservices are like the cool hipster friends in your app's social circle; they only hang out with each other when necessary.
The Plot Twist: Microservices and DevOps BFFs!
When Microservices waltzed onto the scene, DevOps teams threw a massive party. Why? Because deploying one small feature no longer meant rewriting the script for the whole app! It was like having a team of Oompa-Loompas who only fix the cog you want without touching the chocolate river. No more full system halts; just deploy, scale, and maintain itty-bitty chunks while the rest of your app chills like a boss.
Netflix: The Blockbuster of Microservices Adoption
Streaming giant Netflix wasn't always the cool kid with the slickest tech. Back in the day, a database corruption issue turned their DVDs-by-mail service into a tragic comedy. They flipped the script by pioneering the shift to Microservices in 2009, choosing many tiny engines over one Titanic, and, well, we all know who avoided the iceberg. Want to 'chill' like Netflix? Microservices are your ticket!
// An example: The tiny powerhouse function in a Microservice
function retrieveMovie(id) {
// Let's grab that movie data! Microservice style!
return database.query('SELECT * FROM movies WHERE id = $1', [id]);
}
What is the difference between Junior, Middle, Senior and Expert Microservice Architecture developer?
Seniority Name | Years of Experience | Responsibilities & Activities | Average Salary (USD/year) |
---|---|---|---|
Junior Developer | 0-2 |
| $60,000 - $90,000 |
Middle Developer | 2-5 |
| $80,000 - $120,000 |
Senior Developer | 5+ |
| $110,000 - $160,000 |
Expert/Team Lead | 8+ |
| $130,000 - $200,000+ |
Top 10 Microservice Architecture Related Tech
Languages: Java, Golang, and Node.js
Java is like the granddaddy of microservices, chugging along with its robust ecosystem and JVM powers. Spring Boot here is like the trusty old sword for knights heading to the microservice battle. Golang, or as its friends call it, Go, is the new hotshot with a need for speed and a snazzy concurrency model. Node.js swoops in with its non-blocking IO superpowers, making it perfect for those who want to talk microservices with event-driven flair.
// Spring Boot hello world
@SpringBootApplication
public class Application {
public static void main(String[] args) {
SpringApplication.run(Application.class, args);
}
}
Containers: Docker
Docker is like the container wizard, letting your microservices run anywhere without throwing a fit. With Docker, you can wrap your service in a neat little container package, and voilà! It'll play nice anywhere from your laptop to cloud kingdoms far away.
// Dockerfile snippet for a Java app
FROM openjdk:8-jdk-alpine
EXPOSE 8080
ADD target/myapp.jar myapp.jar
ENTRYPOINT ["java","-jar","/myapp.jar"]
Orchestration: Kubernetes
Kubernetes is like the conductor of a container orchestra, expertly managing your Docker ensembles. Here, containers are like musicians that Kubernetes directs to ensure your microservices perform a symphony without turning into a cacophony.
API Gateway: Zuul, Kong
Zuul and Kong are like the bouncers of your microservice club. They check IDs at the door (read: authenticate APIs), tell services where to groove (read: routing), and keep the riff-raff out (read: security).
Service Mesh: Istio, Linkerd
Service Mesh is like the special forces of the network world. Istio is leading the pack with its ability to secure, connect, and monitor services. Linkerd is another stealthy operator providing vital services like traffic management without breaking a sweat.
Database: PostgreSQL, MongoDB
PostgreSQL, the seasoned champion of relational databases, brings ACIDic wit and sequel-driven sagas. MongoDB, the mongoose in the database world, offers a NoSQL approach with a document model so flexible it could do yoga.
Continuous Integration/Continuous Deployment (CI/CD): Jenkins, GitLab CI
In the world of CI/CD, Jenkins is like the old foreman with a sizable tool belt automating every build, test, and deploy. GitLab CI flashes in with its shiny built-in CI/CD smoother than a buttered pancake glide.
Configuration Management: Consul, Spring Cloud Config
Consul, with its slick service discovery and charming key/value storage, is a favorite. Spring Cloud Config plays well with the Spring ecosystem, basically like choosing the matching belt for your outfit.
Monitoring: Prometheus, Grafana
Prometheus, not the guy who gave us fire, but the tool that steals the metrics from the Gods of your system. Combine it with Grafana for monitoring that's so beautiful, it could hang in an art museum.
Logging: ELK Stack, Splunk
The ELK Stack is like the Holmes and Watson of logging, sleuthing through logs to uncover the mysteries of your system. Splunk, on the flip side, is like a data magician pulling insights out of the proverbial hat with ease.