Career Guidance Workshop
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Empower Data. Automate Insights. Engineer the Cloud.
The AWS Data Engineering Program at JVSIT & TGS Training Institute is a job-oriented training program designed to help you master the tools and techniques used to build modern data pipelines and analytics solutions on Amazon Web Services (AWS). Learn how to design, process, and transform large-scale data using AWS cloud-native services to deliver actionable business insights.
This program takes you from data fundamentals to advanced AWS data engineering concepts, preparing you for global roles as an AWS Data Engineer, Cloud Data Specialist, or Big Data Developer.
8th October
07:00 AM TO 08:00 AM
80-100 Days
Training
At TGS Training Institute, our AWS Data Engineering Program blends data architecture, ETL pipelines, and analytics automation into a single, hands-on learning journey. You’ll gain in-depth experience with AWS Glue, Redshift, S3, Lambda, EMR, and Athena, building end-to-end cloud data solutions that handle massive data workloads efficiently.
Day 1
Types of applications
Roles in software industry
Responsibilities of each role
Introduction to What WG
Day 2
Software development life cycles
Types of SDLC
Day 3
Water Fall Model
V Model
Circler Model
Day 4
Agile Model
Domain choosing
Project choosing
Modules and functionalities finding
Day – 5
Introduction to UI-UX
Introducing FIGMA tool
Day – 6
Optimal design principles of database, business logic
Time and Space complexity
Day 7
GitHub with sample HTML page
Creating Repos
Creating LinkedIn account
Day 8
Difference between platform and domain
Requirement gathering for 2 real time scenarios using Agile
Day 9
Deployment methods on hostings
Knowledge about servers and load balancers
Day 10
Power of AI and prompting (webinar)
Using Copilot tool
Using ChatGPT
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Form tags and Attributes
MultiMedia tags
Assignment – Naasongs.
Assignment – Sample login, register, forgot password pages
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Hibernate and Spring
Day – 1
1.Introduction to Spring Boot
2.Setting Up Spring Boot and Creating a Basic Program
If you need the sample Spring Boot application code as text:
java
}
Day – 2
text
// Sample application.properties
spring.datasource.url=jdbc:h2:mem:testdb
spring.jpa.hibernate.ddl-auto=update
Day – 3
text
// Sample Actuator Configuration
management.endpoints.web.exposure.include=health
Day – 4
java
// Sample JPA Entity
@Entity
public class User {
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
private Long id;
private String name;
// getters and setters
}
Day – 5
java
// Sample Security Configuration
@Configuration
@EnableWebSecurity
public class SecurityConfig extends WebSecurityConfigurerAdapter {
@Override
protected void configure(HttpSecurity http) throws Exception {
http
.csrf().disable()
.authorizeRequests()
.antMatchers(“/public/**”).permitAll()
.anyRequest().authenticated();
}
}
Day – 6
java
// Sample REST Controller
@RestController
@RequestMapping(“/api/users”)
public class UserController {
@GetMapping
public List getAllUsers() {
// return list of users
}
@PostMapping
public User createUser(@RequestBody User user) {
// create and return user
}
}
Day – 7
text
# Sample Dockerfile
FROM openjdk:11-jre-slim
COPY target/demo-0.0.1-SNAPSHOT.jar app.jar
ENTRYPOINT [“java”, “-jar”, “/app.jar”]
Day – 8
java
// Sample Eureka Server Configuration
@SpringBootApplication
@EnableEurekaServer
public class EurekaServerApplication {
public static void main(String[] args) {
Day – 9
java
// Sample Microservice Configuration
@SpringBootApplication
@EnableDiscoveryClient
public class MicroserviceApplication {
public static void main(String[] args) {
SpringApplication.run(MicroserviceApplication.class, args);
}
}
SpringApplication.run(EurekaServerApplication.class, args);
}
}
Day – 10
java
// Sample Asynchronous Method
@Service
public class MyService {
@Async
public CompletableFuture AsyncMethod() {
return CompletableFuture.completedFuture(“Async Result”);
}
}
Day – 11
text
# Sample Kubernetes Deployment YAML
apiVersion: apps/v1
kind: Deployment
metadata:
name: spring-boot-app
spec:
replicas: 2
selector:
matchLabels:
app: spring-boot-app
template:
metadata:
labels:
app: spring-boot-app
spec:
containers:
– name: spring-boot-app
image: spring-boot-app:latest
ports:
– containerPort: 8080
It’s a professional training program that teaches you how to design, manage, and automate data pipelines using AWS cloud services.
Students, developers, analysts, and IT professionals seeking to start or grow their careers in cloud data engineering
Basic knowledge of SQL, Python, or cloud concepts is beneficial but not mandatory — beginners are welcome.
AWS Glue, Redshift, S3, EMR, Kinesis, Lambda, Athena, and best practices for cloud data pipelines.
You’ll work hands-on with AWS Console, Glue Studio, Redshift, EMR, Athena, S3, and CloudWatch.
Empowering careers with tech and hands-on learning. 15,000+ trained, 10,000+ placed, 20+ global partners.
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