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Why use NEXT.JS?

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Web development starts with index.html. The major components of web development are HTML, CSS, and Javascript(JS). HTML is used to interact with users, CSS is used for styling HTML elements and Javascript is to run the process in the background.

With JS, we can create/read/update/delete HTML elements. React is an open-source JS framework library that can be used to implement component-based development where the entire website is split into small components(JSX) like building blocks for re-usability, processes based upon life-cycle events, easy maintenance, etc. React will convert components into plain JS to render components in the browser.


Source: https://developer.ibm.com/tutorials/wa-react-intro/

Client Side Rendering:

Client Side Rendering(CSR) is a mechanism in which the JSX render mechanism is completely run at the browser level. Virtual dom is the mechanism created by React which will be handled in the system memory before it renders in the actual dom. So the mechanism will process the following steps,

  1. React source will be built from actual source code for better performance. The Built source code will be placed on the server.
  2. When a client requests the server, then the entire source will be downloaded from the server and cached in the browser.
  3. On every user interaction other than the backend server request, every page render will happen on the browser as client-side rendering.
  4. Libraries used for routing and state management are being handled at the client level.

Server Side Rendering


Server Side Rendering 

Server Side Rendering(SSR) will serve the website with ready-to-be-rendered HTML on the browser. So when there is a request to the server, the component will be rendered on the server side and it will give the data to the browser to render on the web page. It will reduce half of browser performance as it is being handled on the server. So the lifecycle of ssr will be like this,

  1. The server will be ready to serve pre-rendered HTML content from React component.
  2. The client sends a request to the server.
  3. On every request, the server will render components and give the HTML-rendered page as a response along with JSON data and required JS files.
  4. On the browser side, non-interactive content which is plain HTML will be shown to the client as the initial phase, and after that hydration will happen with the existing rendered page to make it interactive by the client.

CSR vs SSR

  • SSR servers pre-render HTML that will support fast loading on the client browser. And also it will reduce usage of system memory as most of it is handled on the server.
  • Let’s consider a scenario where a page has to display and before that, we need to collect details from the backend server. In that case, SSR has a high hand compared to CSR. SSR will make a call through VPC in its private subnet and collect the required details from the backend server. So the data communication time will be reduced, and the page will be rendered on the server and shared with the client.
  • When it comes to a simple website with minimal data processing, CSR will have higher performance as all the libraries, CSS and HTML are already cached in the system. It will quickly render on the virtual dom and present to the client.

Search Engine Optimization

Most organizations come to SSR mechanism only because of high support for SEO. Search engines like Google will crawl through the website to collect the details. So when the user searches on the website, it will appear in the list. SEO works the same for both CSR and SSR. But it will help to improve the other web performance vital metrics such as page load time etc and also increase the web rankings.

Security

When it comes to CSR, all the secured details will be sent from the backend and it will be used to perform follow-up operations. Those operations can be controlled with SSR to give a rendered page by keeping secret data in the server.

How to find CSR or SSR:

Checking whether the website is CSR or SSR can be done by viewing the website source. If it is SSR, then the source will have the rendered pages. If it is CSR then the page will have a simple body just like below,

If the page is rendered with SSR, then we can see the complete rendered HTML page in the source. To view the source, we can either right-click on the website and choose view source option or add the view source key before the website like,

Why Next.JS?

Next.JS is a structured framework that builds for SSR and keeps React as its core. It gives support to routing, image optimization, font optimization, etc., as default. Next.JS is an open-source framework that acts as a middle layer to connect the client and server.

Nest.Js

With Next.JS, the team can build a fully performant web application and configure web applications as per the business requirements. The features supported by the Next.JS team

Kickstart on Next.JS

With the latest Node environment, kickstarting any framework/library has become very easy. The steps go like this,

  1. Install NodeJS.
  2. Open the command prompt and navigate to the respective folder location where we want to create a setup for the Next.JS project and one command is all it takes to kickstart on Next.JS. In the following command, it will create next.js-blog folder.
  1. Navigate to next.js-blog using the command, 
  1. Start the development using the following command and it will open the URL localhost:3000 in the browser.

Micro-frontend

Micro-frontend is the mechanism that will help us to develop an entire application into multiple pieces. So the team can be divided and work on their module without disturbing other modules.

Some of the key benefits of micro-frontend,

  • Technology Agnostic
    • Not completely depend on any framework/library/technology. Based on the system requirements, we can wisely choose to align with the system
  • Isolate module
    • Team can develop their particular module instead of having a dependency on the entire website. So the development, testing, and deployment can be taken at their convenience.
    • One major issue that we’ll face is to avoid duplication. So the components have to be moved to a common module and have to be used everywhere etc. CSS duplication can be avoided with complete tailwind CSS integration. So a decision has to be made on every part of the development for the integration of shared pieces on every module.
  • Testing
    • Testing will be easy as we isolate modules. So the regression testing process will occupy less time for the testers. Building automation testing will be very easy as we’re developing only for a particular module.

Conclusion

Like many developers advised, it all goes with business requirements. Carefully consider all the business requirements before making major decisions on the technical architecture such as choosing SSR or CSR micro-frontend or monolithic or micro-frontend etc. One extra thing is to keep everything aggregated. Even if the business requirements change, we can reduce the amount of time for the migration.

About the Author: Naren is working as a Senior Technical Lead at Mantra Labs. He is interested in creating good architecture and enjoys learning at every step.

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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