Technology is always evolving and shaping up the future of business strategies. Organizations continually try to adopt new technologies to overhaul their business infrastructure and keep up with the pace of changing market dynamics. Leveraging the benefits of AI for process automation has been implemented by several businesses. But, it is still not enough, and there is a lot more to AI that will form the crux of technology disruption in the coming years.

Here is a list of significant technology disruption that is all set to mark a new territory of innovation in the coming five years:

1. Artificial intelligence:

The growth of artificial intelligence in the technology sector has been remarkable, and there have been no signs of its slowing down. In the next five years, we can expect that they will play a significant role in transforming the workplace environment and replacing manual labor. The technology disruption with AI will result in a loss of human jobs at a massive rate as companies tend to prefer economic and efficient systems. The best example of this is the chatbots (chat+robot) that are simulated using natural language processing and artificial intelligence that provide enhanced operational efficiency, 24*7 service and no demands of any working conditions.

2. Insurtech:

The automation in the Insurance sector is gaining momentum currently. For the past few years, the insurance sector was hardly on the map of technology innovation. But, as the technology is getting intertwined in the everyday lives of the people Insurance companies have started adopting technology innovation at a rapid rate. Some of these innovations include Insurance chatbots which are available at all the times to provide solutions to their customers, automated system to perform the internal processes and redundant tasks, AI-based insurance software that processes the claims and service requests quickly.

3. RPA(Robotic process automation):

RPA is the next big thing that will lead to major technology disruption in the coming years. It has already made its way in most of the technology companies, and RPA for insurance is also gaining popularity. With the passage of time, it will just become more advanced and smart replacing a good chunk of human-operated operations. RPA has branched out from three key technologies, i.e., screen scraping, workflow automation and artificial intelligence making it capable of automating business processes and improving the overall work efficiency.

4. Blockchain:

Cryptocurrency based blockchain is the next disrupting technology that has already changed the conventional model of trading and business transaction. Blockchain forms the backbone of cryptocurrencies and Bitcoins. The prominence of Blockchain in the next five years is set to increase by leaps and bounds and will mainstream the payment methods by 2020. Blockchain works on the concept of decentralization where duplicate copies of each ledger are stored on various locations, so there is no focal point of vulnerability that can be exploited by the hackers. It makes blockchain a secure platform altogether. The overall cost of implementing blockchain is quite less because there are no additional costs related to the third parties and is also free from human intervention.

4 Things That Made Blockchain The Most Disruptive Tech In Decades

5. IoT (Internet of things):

The Internet of Things is the interconnection of sensors and software programs embedded physical devices to the Internet that enable them to send and receive data. They play the critical role in the home automation system. Major tech players like Google, Apple and Amazon have already rolled out their smart home products, and their peers like Samsung are in the process to take the plunge in the home automation market. In the next five years, we can presume them to be an integral part of our homes simplifying our lives.

6. Augmented Reality (AR) & Virtual Reality (VR):

As of now, augmented and virtual reality’s existence is limited to the entertainment and gaming industry. But, the future prediction depicts that in the coming years AR will play a significant role in other sectors as well. It will not be surprising to see advancements in AR/VR technology in the healthcare, e-commerce and travel industry.

The above mentioned list was our round-up of the major technology disruption that will happen in the next five years. Though the list can go on and on and you can always find something new to add to the list.

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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