DEV Community

Sabitha
Sabitha

Posted on

CODE ON JVM PROGRAM AT CONTENTSTACK

Yesterday, I attended the CodeOnJVM program organized at Content stack. It was a great session where they talked about modern web architecture, including JAMstack (JavaScript, APIs, Markup), headless CMS like Strapi, and why these technologies are used in real-world projects like Tastebud Café. They also covered key topics like data pipelines, observability, ETL, data sharing

*Yesterday i learn new something and i heard it for the first time *

I’ve written about that below.

JAMstack = JavaScript, APIs, Markup

JavaScript → UI built using frameworks like React, Vue, or 
Next.js

APIs → Content/data is fetched via REST or GraphQL from headless  
CMS

Markup → Pre-rendered HTML for speed and SEO
Enter fullscreen mode Exit fullscreen mode

Why We Chose Next.js and Strapi for Building Tastebud Café

When we set out to build the website for Tastebud Café, we knew we needed a solution that was modern, fast, scalable, and easy to manage — both from a developer’s perspective and for the content team who would update menus, events, and blog posts regularly.

After evaluating many options, we decided to use Next.js for the frontend and Strapi as our headless CMS backend. Here’s why this combination was a perfect fit for Tastebud Café.

What is Next.js?

Next.js is a React-based web framework that helps developers build fast and SEO-friendly websites by combining the best of static and server-rendered pages.

Why Next.js?

  • Blazing Fast Performance
  • SEO-Friendly
  • Developer Friendly
  • Easy Deployment

What is Strapi?

Strapi is an open-source, API-first headless CMS that allows content creators to manage content in an intuitive admin panel and deliver it via REST or GraphQL APIs to any frontend.

Why Strapi?

  • Headless Architecture
  • Perfect for Restaurants
  • Role-Based Access Control
  • Flexible API
  • Scalability and Localization

Why This Combination Works for Tastebud Café

Choosing Next.js and Strapi gave us the best of both worlds:

  • Fast, SEO-friendly frontend that delivers smooth user experiences on desktop and mobile
  • Flexible, easy-to-manage backend that empowers the content team to update information in real time
  • Independent workflows for developers and content creators — speeding up release cycles and reducing conflicts
  • API-driven architecture that is future-proof and ready to integrate with other systems like marketing tools, analytics, or mobile apps ** Why Choose a Headless CMS?**

A Headless CMS separates content management from the frontend, delivering content via APIs. This gives you:

  • Flexibility: Use any frontend technology (React, Next.js, mobile apps).
  • Omnichannel Delivery: Publish content everywhere — web, apps, devices.
  • Better Performance: Frontends get only the content they need, making sites faster.
  • Improved Security: Backend is isolated from the public-facing frontend.
  • Scalability: Easily adapt as your business grows or technology changes.

what is data pipeline ?

data pipeline is the set of automated process that move,transform,and deliver data from one or more data source to be a destination such as warehouse etc making it ready for annalysis or futher processing

Data Pipeline Architecture and Its Evolution

  • A data pipeline moves data from sources to destinations, transforming it for analysis.
  • Traditional pipelines use batch processing (ETL) — data moves in scheduled chunks, which can be slow.
  • Modern pipelines use real-time streaming (with tools like Kafka and Spark) for faster, continuous data flow.
  • Pipelines evolved from simple, scheduled jobs to complex, scalable, and event-driven systems.
  • Today’s pipelines focus on speed, reliability, and handling large, diverse data (the 5 V’s: Volume, Velocity, Variety, Veracity, Value).

Image description

Evolution of Data Pipeline Architecture

  • Batch Processing (ETL):
  • Data was processed in large batches at scheduled intervals. Simple but slow and not suitable for real-time needs.
  • Real-time Streaming:
  • Introduction of streaming platforms (like Kafka, Spark Streaming) enabled continuous, near real-time data processing.
  • Hybrid & Modern Pipelines:
  • Combining batch and streaming, adding features like zero-ETL, data sharing without copying, and improved scalability and observability. ** What is ETL?**

ETL stands for Extract, Transform, Load — it’s a process used to move data from multiple sources into a data warehouse or database for analysis.

  • Extract: Collect data from different sources (databases, files, APIs).
  • Transform: Clean, format, and convert the data into a usable structure.
  • Load: Load the transformed data into the target system (like a data warehouse).

Data sharing
**
**What is Data Sharing?

Data sharing is the practice of making data available to other systems, teams, or organizations so it can be accessed and used without creating unnecessary copies.

Why Data Sharing Matters:

  • Efficiency: Avoids duplicating large datasets, saving storage and processing resources.
  • Real-Time Access: Enables timely use of data across different applications and users.
  • Collaboration: Different teams can work with the same up-to-date data.
  • Security & Governance: Proper controls ensure only authorized access.

Top comments (0)