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There is no ‘good’ or ‘bad’ in design. But, there are right choices that you can make to strike the right balance. The right choices always revolve around the balancing of elements and how to go about incorporating them into your design. Design is largely intrinsic, something that depends on how you look at it.Utilizing strong design principles will go a long way in transforming your UX desgin for your users.

 

But, how do I improve it?

The vital ingredient of any design is a discernable pattern. Patterns are universally observed, and by incorporating the right examples in your designs, it can evoke a desired reaction or response to a specific interaction. So the challenge is to decide – how do you want the user to perceive the design while simultaneously solving the usability problem.

Let’s look at some simple steps.


Hierarchy
This is level zero. By setting visual hierarchy, you are communicating to the end-user where to look first. The entire sequence, along the visual journey, has to be laid out first. For example: making an element bigger to draw the attention and set a focal point for the user. Hierarchy can also be set by using white space or bright colours to highlight crucial parts of your interface.

In Fig A, the design has all the information laid out for the user, but it’s set in no particular hierarchy, meaning there is no indication of what is important and what is less important, so a user can feel lost in the visual journey of what message the design actually intended to say.

      

Fig A                                                                                                                          Fig B

In Fig B, by using intentional white space, we bring the most important message to the fore – so what a user sees first is that the game night is between who, where and when, and everything else is kept secondary to it.

Keeping things simple and consistent
By keeping the elements in your design minimal, placing them in your layout will be easier to manage – making it easy for users to navigate through your design. Too many elements in one design can be off-putting and confusing to look at. Consistent use of elements is a better approach, that usually sets the users mind at peace – like the style of a button or the placement of a close button. In this way you are guiding the users on what to see first and where to click next. Interaction consistency is also as important as visual consistency. Always try to minimize the number of ‘clicks’ in your design – no one likes to engage in redundant clicks to get quick information.

In the examples below, the design on the right can be improved by simply reducing the number of clicks from 10 clicks to 5, by reducing redundancies in the information design.

Reducing redundancies in the information design.

 

Mind the space
Spacing is vital for great composition. Using whitespace and negative space correctly, plays a crucial role in your design. It is just like your living room, when you decide what to keep in a particular area and where to leave space – the same applies to your design also. For example, when there is only a line or two of text, try to put the text in the one-third

of your art-board either from top or bottom. If however, there is more text to work with try to group them and set the hierarchy by increasing or decreasing spacing between each group. By incorporating enough white space in your design, there will be sufficient breathing area for users to relax their eyes into.

White space is not just empty space. It’s about creating enough room for your text and design elements to co-exist.

 

Typography
Sensible use of typography can really enhance your design. Selecting the right typography involves certain decisions that include a choice of font family, weight & size, leading, tracking, kerning and scale. Avoid using too many fonts from different font families. Instead, use one or two font family and play around with font weight and size to find what works best for your design. Also remember, If no one can read the text on your design, it defeats the purpose of putting all that effort into your designs. Lastly, avoid using font colour which may clash with your background colour For example, ‘Red’ text on an Orange background, is an extreme choice.

 

Contrast
Emphasizing certain elements of your design is both visually appealing and functional. Finding the right color mix for temperature, saturation, hue, and intensity can help you set hierarchy for the elements you want to bring out in your design. However, contrast isn’t just a colour thing. It also involves shapes, edges, textures, scaling, and size. Albeit, like with almost any other design concept, it can be overdone. You should make sure that the contrast in your design isn’t so dramatic that it’s jarring unless that’s your specific intent.

 

Not a good way to use contrast

 

A more balanced contrast

 

Balance the Elements
This is where you draw the line between your design and your users. A design is not useful if it doesn’t solve a problem. Likewise, it is also not so useful if the user didn’t get the message right. Information is important to get across – it should have a higher priority in your design approach and draw the user’s attention first.

In the images below, the content is the same but what makes the right image better is the complete balancing of all the elements, relaxing the design using appropriate spacing and placement without overwhelming the user with all that textual information.

Making the right design choices for enhancing a user’s experience is all about creating a seamless link between the user and the applications they use. Every designer has their own style and while these design principles are important to consider – it’s more important to stay original and keep practicing.

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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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