It’s very interesting to see and understand how things are really working at the level of bytes and bits. In software, we rarely think about those details, as most of these things are abstracted so a software programmer can focus on just his piece while the hardware engineers and embedded programmers take care of making those intricate and complex circuit boards.

Sometime back when we decided to do something in the space of IOT, we were complete newbies with absolutely no background, academic, or professional. But we learnt many things the hard way by trying, failing, and correcting. But perhaps as many people say, that may also be the best approach towards learning anything new.

Today with an experience of building an actual physical thing that listens, I feel more confident about the space, and our ability to replicate our success story for our clients as well. But what is that we build, and now a question of great debate, and subjectivity. I can perhaps think of some rules that an IOT product or initiative should bear in mind.

Before going forward, give it a thought

Does the device really help its customer? This is a very basic and moot question that every innovator and maker should ask themselves.

Does the product makes our life more safer, convenient, healthier, and happier? If the answer is yes for these questions, the product may find takers in the market.

A product must have a clear cut value proposition for its intended buyers. If the product is just a cool gadget, it will find utility only with a handful of users who will be very quick to move onto something more cooler as and when it’s available in market.

Just having built something and pushing it off to the supply chain may not be of great help in building a sustainable business that will have a long term impact. One should think of constantly reinventing the product to make it better & more useful for its customers. Timely service, and a great customer support will go a long way in winning the confidence of the current active users, and the word of mouth publicity will help in winning more users till the product reaches a critical mass.

There are some challenges too

The challenge that we face today in IOT, especially industrial IOT is that existing chips that help the sensors transmit the data directly into cloud, consume a lot more power than what would be practical for widespread adoption in industries. But recent advancements in technology with the Qualcomm Cat M1 modules, and Verizon’s upgrading its infrastructure to allow ultra low band transmission at really affordable rates can be the right steps in the direction of making IOT really ubiquitous.

Security is another big challenge for mass adoption of IOT. Seeds of doubt about the device being sufficiently protected against hacking is one big reason why customers are still not able to fully give in to the idea of leaving their critical functions to a device. What if my smart locking system is hacked, and an intruder is able to hack his way inside my house?

An intrusion into house, or the smart lighting solution being hacked are still something not as much threatening as a possibility of a smart glucometer or a pacemaker being hacked. Risk of this nature can have life threatening consequences, and cannot be taken lightly.

These are valid questions which the IOT community will have to tackle head on. But I believe these questions or challenges are always there with any new technology. It takes time for ecosystem to mature to a level where issues of security are addressed, questions of viability, feasibility, and usability are addressed, and then mass adoption follows. The stage in which the current IOT development possibly is where developers and engineers worldwide are working in the direction of making IOT safer, and more useful for everyone. Soon it will be IOT for everyone.

Stay tuned for next article about some specific steps and questions to create an IOT Product.

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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