Java SpringBoot Roadmap :
➤ 1. Java SE
1.1. OOP.
1.2. Exception Handling.
1.3. Multithreading.
1.4. Collections(List,Set,Map, ... )
1.5. JDBC
1.6. It is required to be familir with hashtag
hashtag#java8 features stream and lambda expressions
➤ 2. DBMS
2.1. Data modeling
2.2. Relational data Model
2.3. Normalization
2.4. Transaction Processing
2.5. Concurrency Control
➤ 3. Java EE
3.1. Servlets
3.2. JSP
3.3. JSTL
➤ 4. Hibernate
➤ 5. Web Service
5.1. RESTful API (The Most popular With Spring)
5.2. It is better to be familiar with SOAP Web Service
➤ 6. Spring
6.1. Spring Core
6.2. Spring Data
6.3. Spring MVC
6.4. Spring RESTful API
6.5. Spring Boot
6.6. Spring Security
➤ 7. Advanced Topics
7.1. Solid principles
7.2. Design Pattern
7.3. Microservices and Spring Cloud
➤ 8. maven and Git
Credit - Rani Dhage
➤ 1. Java SE
1.1. OOP.
1.2. Exception Handling.
1.3. Multithreading.
1.4. Collections(List,Set,Map, ... )
1.5. JDBC
1.6. It is required to be familir with hashtag
hashtag#java8 features stream and lambda expressions
➤ 2. DBMS
2.1. Data modeling
2.2. Relational data Model
2.3. Normalization
2.4. Transaction Processing
2.5. Concurrency Control
➤ 3. Java EE
3.1. Servlets
3.2. JSP
3.3. JSTL
➤ 4. Hibernate
➤ 5. Web Service
5.1. RESTful API (The Most popular With Spring)
5.2. It is better to be familiar with SOAP Web Service
➤ 6. Spring
6.1. Spring Core
6.2. Spring Data
6.3. Spring MVC
6.4. Spring RESTful API
6.5. Spring Boot
6.6. Spring Security
➤ 7. Advanced Topics
7.1. Solid principles
7.2. Design Pattern
7.3. Microservices and Spring Cloud
➤ 8. maven and Git
Credit - Rani Dhage
Wondering where to begin with Java Spring Framework? This roadmap breaks down the essentials so you can learn smarter, not harder.
But why Spring?
- Simplifies code and boosts testability
- Vast ecosystem of pick-and-choose projects
- Seamless compatibility with the Java ecosystem
- From simple APIs to enterprise apps and microservices
- Enables rapid development
In the world of Java development, staying ahead means mastering the right tools.
Spring Framework isn't just a tool—it's your secret weapon.
This document provides the concepts needed to master the Spring Framework.
This roadmap may seem challenging at first, but take it one step at a time—start small:
- Learn the fundamentals
- Build simple projects
- Move on to complex applications
- Practice, practice, practice
Remember, mastering Spring is a marathon, not a sprint.
Before you know it, you'll be miles ahead of the rest.
Happy coding!
Stay tuned for my next post, I will point you to some resources that have helped me immensely.
If you liked this post:
Credit - Lahiru Liyanapathirana
But why Spring?
- Simplifies code and boosts testability
- Vast ecosystem of pick-and-choose projects
- Seamless compatibility with the Java ecosystem
- From simple APIs to enterprise apps and microservices
- Enables rapid development
In the world of Java development, staying ahead means mastering the right tools.
Spring Framework isn't just a tool—it's your secret weapon.
This document provides the concepts needed to master the Spring Framework.
This roadmap may seem challenging at first, but take it one step at a time—start small:
- Learn the fundamentals
- Build simple projects
- Move on to complex applications
- Practice, practice, practice
Remember, mastering Spring is a marathon, not a sprint.
Before you know it, you'll be miles ahead of the rest.
Happy coding!
Stay tuned for my next post, I will point you to some resources that have helped me immensely.
If you liked this post:
Credit - Lahiru Liyanapathirana
What does a typical microservice architecture look like?
The diagram below shows a typical microservice architecture.
🔹Load Balancer: This distributes incoming traffic across multiple backend services.
🔹CDN (Content Delivery Network): CDN is a group of geographically distributed servers that hold static content for faster delivery. The clients look for content in CDN first, then progress to backend services.
🔹API Gateway: This handles incoming requests and routes them to the relevant services. It talks to the identity provider and service discovery.
🔹Identity Provider: This handles authentication and authorization for users.
🔹Service Registry & Discovery: Microservice registration and discovery happen in this component, and the API gateway looks for relevant services in this component to talk to.
🔹Management: This component is responsible for monitoring the services.
🔹Microservices: Microservices are designed and deployed in different domains. Each domain has its database.
Credit - Alex Xu
The diagram below shows a typical microservice architecture.
🔹Load Balancer: This distributes incoming traffic across multiple backend services.
🔹CDN (Content Delivery Network): CDN is a group of geographically distributed servers that hold static content for faster delivery. The clients look for content in CDN first, then progress to backend services.
🔹API Gateway: This handles incoming requests and routes them to the relevant services. It talks to the identity provider and service discovery.
🔹Identity Provider: This handles authentication and authorization for users.
🔹Service Registry & Discovery: Microservice registration and discovery happen in this component, and the API gateway looks for relevant services in this component to talk to.
🔹Management: This component is responsible for monitoring the services.
🔹Microservices: Microservices are designed and deployed in different domains. Each domain has its database.
Credit - Alex Xu
SQL vs NoSQL Databases: A Comprehensive Overview
In today’s rapidly evolving technological landscape, database management plays a crucial role in organizing, storing, and retrieving data. Two major categories of databases are SQL and NoSQL databases, each suited for different use cases depending on the nature of the data and the scale of the system. Let’s dive into an overview and comparison between SQL and NoSQL databases, highlighting their features, differences, and use cases.
In today’s rapidly evolving technological landscape, database management plays a crucial role in organizing, storing, and retrieving data. Two major categories of databases are SQL and NoSQL databases, each suited for different use cases depending on the nature of the data and the scale of the system. Let’s dive into an overview and comparison between SQL and NoSQL databases, highlighting their features, differences, and use cases.
SQL Databases
SQL (Structured Query Language) databases are relational databases that store data in tables with predefined schemas. These databases are widely used due to their robust structure and compliance with ACID (Atomicity, Consistency, Isolation, Durability) properties.
Key Features of SQL Databases:
1. Database Models: SQL databases follow a relational model where data is organized into tables (or relations). Each table consists of rows and columns, where each column represents a specific data type and each row represents a record.
2. SQL (Structured Query Language): SQL is the standard language for querying relational databases. It allows users to execute complex queries to retrieve, manipulate, and update data efficiently.
3. Normalization and Denormalization: SQL databases often utilize data normalization techniques to reduce redundancy and improve data integrity. Normalization involves dividing large tables into smaller, more manageable ones, based on relationships between the data.
4. Database Design and Schema: SQL databases rely on a strict, predefined schema that defines how data is structured within tables. The schema must be defined before any data is entered, which ensures a strong level of data integrity.
SQL (Structured Query Language) databases are relational databases that store data in tables with predefined schemas. These databases are widely used due to their robust structure and compliance with ACID (Atomicity, Consistency, Isolation, Durability) properties.
Key Features of SQL Databases:
1. Database Models: SQL databases follow a relational model where data is organized into tables (or relations). Each table consists of rows and columns, where each column represents a specific data type and each row represents a record.
2. SQL (Structured Query Language): SQL is the standard language for querying relational databases. It allows users to execute complex queries to retrieve, manipulate, and update data efficiently.
3. Normalization and Denormalization: SQL databases often utilize data normalization techniques to reduce redundancy and improve data integrity. Normalization involves dividing large tables into smaller, more manageable ones, based on relationships between the data.
4. Database Design and Schema: SQL databases rely on a strict, predefined schema that defines how data is structured within tables. The schema must be defined before any data is entered, which ensures a strong level of data integrity.
NoSQL Databases
NoSQL (Not Only SQL) databases are non-relational and offer flexibility in terms of data storage. They are ideal for unstructured or semi-structured data, and their scalability makes them well-suited for large-scale applications like big data and real-time analytics.
Key Features of NoSQL Databases:
1. Database Models: NoSQL databases are highly versatile and come in various types:
- Column-Family: Organizes data in columns rather than rows (e.g., Cassandra).
- Document: Stores data as documents, usually in JSON or BSON format (e.g., MongoDB).
- Key-Value: Uses a simple key-value pair for storing data (e.g., Redis).
- Graph: Represents data as a graph, where relationships between data points are as important as the data itself (e.g., Neo4j).
2. Transactions and ACID Properties: While some NoSQL databases support ACID properties, many prioritize BASE (Basically Available, Soft state, Eventual consistency) to optimize for distributed and highly available systems.
3. Indexing and Query Optimization: NoSQL databases offer various indexing mechanisms to allow for fast access to data, even when handling large-scale datasets. Query optimization often depends on the database model (e.g., using indexes for key-value stores or graph traversal algorithms for graph databases).
4. Database Security: Security measures in NoSQL databases focus on user authentication, encryption, and securing distributed systems. With the rise of cloud-based services, securing NoSQL databases has become increasingly important, especially for large-scale and real-time data.
5. Transactions and ACID Properties: Though traditionally NoSQL databases favored BASE properties for high availability and fault tolerance, some modern NoSQL systems like MongoDB and Couchbase have started incorporating ACID transactions for more reliable data operations.
NoSQL (Not Only SQL) databases are non-relational and offer flexibility in terms of data storage. They are ideal for unstructured or semi-structured data, and their scalability makes them well-suited for large-scale applications like big data and real-time analytics.
Key Features of NoSQL Databases:
1. Database Models: NoSQL databases are highly versatile and come in various types:
- Column-Family: Organizes data in columns rather than rows (e.g., Cassandra).
- Document: Stores data as documents, usually in JSON or BSON format (e.g., MongoDB).
- Key-Value: Uses a simple key-value pair for storing data (e.g., Redis).
- Graph: Represents data as a graph, where relationships between data points are as important as the data itself (e.g., Neo4j).
2. Transactions and ACID Properties: While some NoSQL databases support ACID properties, many prioritize BASE (Basically Available, Soft state, Eventual consistency) to optimize for distributed and highly available systems.
3. Indexing and Query Optimization: NoSQL databases offer various indexing mechanisms to allow for fast access to data, even when handling large-scale datasets. Query optimization often depends on the database model (e.g., using indexes for key-value stores or graph traversal algorithms for graph databases).
4. Database Security: Security measures in NoSQL databases focus on user authentication, encryption, and securing distributed systems. With the rise of cloud-based services, securing NoSQL databases has become increasingly important, especially for large-scale and real-time data.
5. Transactions and ACID Properties: Though traditionally NoSQL databases favored BASE properties for high availability and fault tolerance, some modern NoSQL systems like MongoDB and Couchbase have started incorporating ACID transactions for more reliable data operations.