Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(8)

Customer Journey(17)

Design(44)

Solar Industry(8)

User Experience(67)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(29)

Technology Modernization(8)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(57)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(146)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(21)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

React 18: From A Developer’s PoV

React 18, the much-awaited version of React is out with exciting features like automatic batching, a new API –startTransition, and streaming server-side rendering with support for Suspense. What’s so special about this newer version is its unique “concurrent rendering” method. In the earlier versions of React, the rendering process was synchronous and non-interruptible where the UI would lock during long render processes and not respond to user input instantly. Using React 18, the rendering process can be made asynchronous and can be interrupted, paused, resumed, and even abandoned enabling developers to create a more fluid user experience.

How to update from React 17 to React 18?

React 18 is currently released in alpha and can be downloaded using the command 

image2.png

A component is usually rendered like this

image4.png

However, to utilize the latest features, components need to be rendered like this:

image3.png

What’s there in React 18?

Automatic Batching

Automatic Batching is the rendering that occurs at the same time whenever triggered to update with multiple states at once. The previous version of React could only batch updates inside React event handlers. If the multiple state updates are triggered by a promise or a callback, their responses are not rendered at the same time. But with React 18, all renders are batched, meaning they’ll occur at the same time, regardless of their trigger.

image12.png

Here’s an example with a promise:

image5.png

Suspense

With Suspense, React 18 makes major performance improvements to SSR by making serving parts of an app asynchronously possible. Suspense helps in specifying what React should show when a part of the tree isn’t ready to render. For instance, in case there are four components: a Header, a Sidebar, a Banner component, and the Main component. If all four of them are stacked on each other like this 

image9.png

Then, the server would try to render them at once, slowing the entire page down. If the Header, the Sidebar, and the Main are more important for the readers, one can prioritize these over the Banner by wrapping the Banner component in Suspense tags:

image6.png

As a result, the server would first serve the Header, Side Bar, and Main component, and then a spinner would be displayed while the Banner waits to load.

Transitions

React apps are interactive, however, to make an app update itself as people interact with it, constant updating might cause the app to slow down significantly and give a poor user experience. Transition is a new React feature that differentiates between urgent and non-urgent updates. Transition updates transform the UI from one view to the next.

  • Urgent updates are the direct interactions like typing, clicking, pressing, etc., that need immediate response to match one’s intuition about how physical objects behave. Otherwise, they feel “wrong”. However, transitions are different because the user doesn’t expect to see every intermediate value on the screen. 

Single-user input should typically result in both an urgent and a non-urgent update for the best user experience. StartTransition API can be used inside an input event to tell React which updates are urgent and which are “transitions”.

image8.png
  • Non-urgent updates are wrapped in ‘startTransition’ and would get interrupted if more urgent updates, such as clicks or keypresses, emerge. If a user interrupts a transition (for example, by typing multiple characters in a row), React will discard any stale rendering work and render only the most recent update.

New Hooks

  1. UseId

useId is a new hook for creating unique IDs on both the client and the server while avoiding hydration mismatches. It’s most beneficial for component libraries that need to integrate with accessibility APIs that require unique IDs. This addresses a major problem that existed in React 17 and in its previous versions, but it’s even more critical in React 18 because of how the new streaming server renderer delivers HTML out-of-order.

image10.png

 

  1. useTransition

‘useTransition’ and ‘startTransition’ would allow marking some state updates as not urgent. By default, other state updates are considered urgent. React would allow urgent state updates (for example, updating a text input) to interrupt the non-urgent state updates (for example, count results).

image11.png

 

  1. useSyncExternalStore

‘useSyncExternalStore’ is a new hook that enables external stores to support concurrent reads by forcing updates to the store to be synchronous. It eliminates the necessity for useEffect when implementing subscriptions to external data sources, and is recommended for any library that integrates with a state external to React.

image7.png

 

  1. useDeferredValue

‘useDeferredValue’ helps to delay re-rendering a non-urgent part of the tree. It works in the same way as debouncing, although it offers a few advantages. React will attempt the deferred render directly after the initial render is reflected on the screen because there is no predetermined time delay. The deferred render is interruptible and doesn’t block user input.

image13.png

 

 

  1. useInsertionEffect

‘useInsertionEffect’ is a new hook that allows CSS-in-JS libraries to address the performance difficulties of injecting styles in the render. Unless you’ve already built a CSS-in-JS library we don’t expect you to ever use this. This hook will activate after the DOM is mutated, but before layout effects read the new layout. useInsertionEffect is even more important in React 18 because React yields to the browser during concurrent rendering, giving it a chance to recalculate the layout.

image1.png

Conclusion

With the introduction of React 18, there has been a drastic change in the world of web applications because of its unique offerings like concurrent mode and server-side rendering. The latest feature would make it easier to develop and maintain a code as well as make apps faster and more responsive to user interactions.

About the author:

Manikandan is a Technical Lead at Mantra Labs working on React/Angular-related projects. He is interested in learning about stock analyst trading algorithms, and in his free time, he loves to swim, cook, and play cricket and chess.

Cancel

Knowledge thats worth delivered in your inbox

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

By :

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.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot