One Diagram to Refer for Microservices Roadmap


Companies like Netflix, Amazon, and others have adopted the concept of microservices in their products due to large benefits offered by microservices.

As we understand, many developers want to know how they should start this journey. So I decided to make this journey clearer by defining a road map for this learning curve.

Container - A container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably from one computing environment to another.

Container orchestration automates containers' deployment, management, scaling, and networking. Enterprises that need to deploy and manage hundreds or thousands of Linux® containers and hosts can benefit from container orchestration.

Load balancer is a device that acts as a reverse proxy and distributes network or application traffic across several servers.
Load balancers are used to increase the capacity (concurrent users) and reliability of applications.

Monitoring and Alerting : In a microservice architecture, if you want to have a reliable application or service, you have to monitor the functionality, performance, communication, and any other aspect of your application in order to achieve a responsible application. Promethous is widely popular.

Distributed Tracking - when it comes to microservice architecture, a request may be passed through different services, which makes it difficult to debug and trace because the codebase is not in one place, so here distributed tracing tool can be helpful.

Message Broker - A message broker is software that facilitates the exchange of messages between applications, systems, and services.

Database - in most systems, we need to persist data, because we would need the data for further processes or reporting, etc.


Caching - Caching reduces latency in service-to-service communication of microservice architectures.

Cloud service provider -is a third-party company offering a cloud-based platform, infrastructure, application, or storage services.

API Management: API management is the process of designing, publishing, documenting, and analyzing APIs in a secure environment.

Application Gateway -An application gateway or application level gateway (ALG) is a firewall proxy that provides network security. It filters incoming node traffic to certain specifications, meaning only transmitted network application data is filtered.

Service Registration -A service registry is a database used to keep track of the available instances of each microservice in an application. The service registry needs to be updated each time a new service comes online and whenever a service is taken offline or becomes unavailable.



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🚀 Empower your career with data engineering skills and secure your dream job in this growing field⚙️

✔️ After receiving a lot of queries, I've made some revisions to my original suggestion and I highly recommend following this exact sequence. Trust me, it will make things much easier for you.

♻️ It's easy to get lost in the vast amount of information available when it comes to learning new skills, especially in the field of data engineering.

🔗 However, with a solid roadmap and some dedication, it is possible to enter the field of data engineering in 2023.

Here are some steps you can follow to make sure you're on the right track -

🔶 Learn RDBMS, NoSQL databases, SQL, and data warehousing concepts :

As a data engineer, you'll be working with large amounts of data stored in databases. Familiarize yourself with different types of databases and how to query them using SQL. Learn one RDBMS and one NoSQL database, then familiarize yourself with data warehousing concepts.

1. Learn Data Warehousing concepts - https://lnkd.in/eKnVbFAB
2. Learn MySql - https://lnkd.in/efk-Mi3c
3. Learn and practice SQL - https://lnkd.in/efMKFkfX
4. Learn Azure Cosmos DB - https://lnkd.in/eNVyc6Mq
   
🔶 Learn Python and PySpark :

These are essential tools for data engineers.

1. Python: https://lnkd.in/e5rCbvP8
2. PySpark : https://bit.ly/3Vu34Ev

🔶 Learn Bash, Airflow and Kafka :

These tools will help you automate and streamline data processing tasks. You will also lear how to create data pipelines.🔩

https://lnkd.in/eyN6u2yd

🔶 Learn Git and the basics of CICD :

These skills will help you work collaboratively on data engineering projects and ensure smooth deployment.

Git - https://lnkd.in/eX_Q8s99
Basics of CICD - https://lnkd.in/epKGivFY

🔶 Learn Azure Databricks and Data Lakes :

Azure Databricks - https://lnkd.in/eBij4akJ
Learn Data Lakes - https://lnkd.in/eQ9xxAJT

👉 Create a standout resume by showcasing your skills and experience in data engineering.

👉 Enhance your portfolio by completing various data engineering projects and sharing them on GitHub.

👉 Seek out opportunities in data engineering by applying to relevant positions

Optional Learning -

1. Learn Data Structure and Algorithms
2. Learn AWS or Azure services dedicated for data engineering works.
   
By following these steps and dedicating yourself to learning and gaining experience, you can become a data engineer in 2023. Good luck on your journey!

I have created a Telegram channel for sharing knowledge and have included the link in the comments section.

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