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

Google’s Material Design for Android- Trends You Must Follow in 2016

Material Design is a Latest design language developed by Google. Material Design makes more liberal use of grid-based layouts, responsive animations and transitions, padding, and depth effects such as lighting and shadow.

Material Design is a Google’s conceptual design philosophy that outlines how apps should look and work on mobile devices. It breaks down everything — such as animation, style, layout- and gives guidance on patterns, components and usability.  According to Google: “We challenged ourselves to create a visual language for our users that synthesizes the classic principles of good design with the innovation and possibility of technology and science. This is material design.”

Material starts with mobile but extends to any other device. It is rooted in a few principles:

Realistic visual cues: The design is grounded in reality and actually inspired by design with paper and ink.
Bold, graphic and intentional: Fundamental design techniques drive the visuals. Typography, grids, space, scale, color and imagery guide the entire design. Elements live in defined spaces with a clear hierarchy. Color and type choices are bold and deliberate.
Motion provides meaning: Animation is a key component of Material Design, but it can’t just be there for the sake of movement. Animations need to happen in a single environment, serve to focus the design and include simple and easy transitions. Movements and actions should mirror the physical world.

Few points we need to understand about Material Design:

Understanding the “Tactile Surface”
One of the things that comes up a lot when talking about layered interfaces is the “tactile surface.

Think of this as having multiple sheets of paper that are stacked together to create a framework for how everything within the design works. These sheets are a little different from physical sheets of paper in that they can change shape and form — such as stretch or bend — but work in a way that is seemingly realistic.

As explained in Mobile Design Trends for 2015, treat the tactile surface is a container for content and information. The container is flat in design but has a faint shadow to separate it from other containers and layers. Other techniques to create separation between layers – such as textures, gradients or strokes – are unnecessary.Material Design-infographicYou can see the separation in the layers for the Reddit app, above. There is an obvious top menu layer covering a greyed out main content layer. Even the main header image contains elements of layering and shading that emphasize a three-dimensional tactile surface.

As demonstrated in the Android Lollipop UI Kit, a tactile surface clearly established the relationship and function of content within a design. (Each container often has one job, such as a link or video player.) This approach also establishes depth, as elements in other containers are layered, creating a seemingly three-dimensional world.

Material is Made for Adaptive Design:
Layered interfaces
are inherently made for adaptive design. All of the design guidelines actually encourage a designer to work with an adaptive layout (whether you prefer Adaptive or responsive is up for debate, however.)

When thinking about layered interfaces, it is important to consider how all the elements relate to one another.

Google recommends its standards because of a “flexible grid that ensures consistency across layouts, breakpoint details about how content reflows on different screens, and a description of how an app can scale from small to extra-large screens.”

Considerations include:

Breakpoints: Widths include 480, 600, 840, 960, 1280, 1440 and 1600 pixels.
Grid: 12-column layout with margins and gutters (8, 16, 24, or 40 pixels) and a baseline grid.
Surface behaviors: UI adapts to the type of screen so that surfaces are visible or toggled to hide.
Patterns: Function is based on screen size, including reveal, transform, expand, reflow and divide.

These considerations make it easy for designers to ensure their interfaces adapt for any device in any situation. They provide a baseline to help designers as they construct layouts for desktop, tablet and smartphone.

Material and Other Mobile Design Trends:
When it comes to creating layered interfaces, other trends also come into play.

  • Material Design has borrowed plenty of design concepts from the flat aesthetic and other trendy techniques. In fact, some would argue that Material Design is a close cousin to Flat Design 2.0 because many of the visual treatments are quite similar.
  • What separate layered interfaces from totally flat design is that effects are needed to create more three-dimensional spaces and to mimic lighting. In essence, designers are bringing back some of the design tricks eliminated with flat. The difference is that they’re using these tricks to improve usability rather than simply as decorative accents.
  • The colors most closely associated with layered interface design nearly fall within the flat aesthetic. The big difference is the vast number of color options that Google provides. Palette options are in the same vein though – bright, bold and fully-saturated hues.
  • While many designers opted for blues and reds when it comes to designing flat, more layered interfaces seem to feature deep purples and yellows. That’s likely because each of these hues is easy to pair with contrasting white or black text.
  • Layered interfaces also work well in the space of minimalism, particularly when it comes to typography. Type is stacked with clear hierarchy and sans serif options are the preferred choice. Google suggests using Roboto as the dominant typeface and it comes with plenty of choices, from thin to bold to italic to condensed.
  • The variety helps create levels of type that guide users between elements. In the true spirit of minimalism, one font can pretty much do everything you need with proper sizing and scaling.
  • You’d be hard-pressed to find a website without a full-screen image these days and layered interfaces further emphasize the use of vivid, intentional imagery.
  • The apps make the most of a simple photo for this very purpose, showing that Material Design  does not just use color, photos  and effects solely for visual impact — they are an essential part of the design.
  • Finally, layered interfaces are perfectly made for cards, which we discussed in the previous chapter. Looking through the examples showcased, almost every design includes something with a card-like element. From smaller cards to full-screen options, these trends go hand-in-hand.

Material Design looks nice and it works well in a variety of places. Designers will want to take advantage of that and the lite version provides the perfect level of guidance. Material Design Lite is also a good tool for designers and developers that want to create a unified web-app experience, so that apps look, feel and function in the same way regardless of device. Layers are definitely going to stick around, but the overall look may be a little more “layered” and a little less material, so that the design falls somewhere between Material Lite and iOS standards.Material Design 2The Weather Channel iOS app is already using this approach. The app layers cards, colors and images. Where the design concepts overlap most is in the use of cards and the placement of geometric shapes. Where the design is “less Material” is in the lack of depth and shadowing so that the overall look is flatter and streamlined.

Gradients and monochromatic color layers are another way layered interfaces can continue to grow visually. Monochromatic color palettes are a classic design technique that make it easy to create sharp elements to fit almost any type of content.

The Elevate iOS app uses a gradient background with multiple levels of cards in its design. The animations and movements are very Material Design in nature but the use of a gradient is not. This simple evolution highlights how designers will start to break the visual rules of layered interfaces while continuing to leverage its more functional aspects.

Designers will continue to evolve layered interfaces and Material Design concepts with darker colors and hues. Most of the apps available right now feature light and white color schemes, but darker colors will start to emerge. Weather Timeline is a perfect example of this. The simple change to the color palette is enough to really make this app stand out from all the others available. It still uses a style that’s distinctly layered, but the darker interface is simple and elegant. The colors for the entire design are less saturated and toned to match the darker aesthetic.

Today’s layered interfaces are just the start. The simple visual style and high usability of this design style will continue to emerge and grow as designers — not just for Android — will latch on the concepts. What may be even more interesting is that the look of layered interfaces is really just an extension of a lot of the design techniques that have been growing in popularity for several years, including flat and minimalism.

At some point the pendulum may swing back to more “realistic-looking” interfaces, but until then this concept appears to have quite the foothold.

Mantra Labs deep dives into latest trends and innovations in the Web, Mobile, Enterprise and Internet of Things space. The insights generated from these studies helps us provide more value for our clients.

Guest written by P. Sudhakar, our ace Design Lead.

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