In my last article, I have talked about the challenges and oppurtunities of IOT industry. Now let’s talk about building an IOT product and benefits of it in the market.

How about building an IOT device?

Now let me also talk a bit about the process of building an IOT product. If you are thinking of building an air purifier, or a thermostat, or some smart lighting solutions for home, you are very likely to hit the first stumbling block as to how to go about the whole process. How to get a 3D design for the device, where to go for a prototype design, and how to get the electronics (the PCB part) done, and how to make the device talk and interact with various other devices like your mobile phone, etc.

What you need is professional expertise in not one particular field, but many diverse fields. If you are a software engineer with some experience with coding, you will know writing software is not that difficult as all you need is a computer, and you could create wonders just sitting in home or office. Building a real, physical thing can be really tough & challenging. Not only it requires varied set of skill set, but also can cost much more to prototype, and test it out.

Steps to follow before going ahead

For the benefit of newbies to the field, I have listed down the steps generally followed in any IOT product development process.

Market Research
Conceptualization/Ideation
Design
Prototype (Schematic Design, Layout)
PCB Manufacturing
Procuring components & assembly of electronic circuitry
3D printing of casing & outer facade of the product
Field Trials
Redesign & trials if needed
Marketing & Mass manufacturing

Loads of data is generated, but what to do with it?

Due to the large number of IOT devices around, it is quite as well expected that they will generate a huge volume of data. Question is how to make best use of the data captured, or how to make your device react to events triggered by actions of other users, or may be from the device owner himself through a mobile application.

Standards like the MQTT, AMQP, etc are the general protocols used for an IOT device or the cloud to communicate with each other. Both of them work on basic principle of publish/subscribe. The two parties subscribe to events, and whenever there is an update, or an occurrence of the event, the subscribing parties are notified.

Providers like Microsoft Azure, ABM, and AWS have all come up with their IOT platforms making it easy to monitor and control remote devices from click of a button. Being on the cloud, it gives IOT the ability to scale. The data being captured in the cloud can be analysed, and trends studied using Machine Learning algorithms and Artificial Intelligence.

Today it is possible to auto update the firmware of an IOT device without requiring any intervention from the customer.

How IOT will drive benefits for users?

Data generated from IOT devices are being continuously analysed and machine learning models are built to help in predictive analytics. Earlier emphasis was on preventive maintenance in industries, and anywhere else where machines were deployed. We used to ensure regular and timely checkups to ensure our machines are always in healthy state. But now with advancements in technology, based on the data captured, our machine learning prediction models can warn us in advance of a possible impending breakdown. A corrective action can be immediately triggered, and the machine is restored to good health much before breakdown.

Today IOT driven processes paves the way for improvements in existing processes leading to higher customer satisfaction & safety leading to better profits for businesses. Customers delight and an increasing affiliation are invaluable assets to any business, and when IOT is able to help the business achieve that, its relevance will never be in doubt. No wonder Gartner Research predicts there will be more than 20 billion IOT devices by the year 2020.

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

By : Sampada

Published on Dec 19, 2024

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:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, 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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