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COVID-19 Lockdown Effects: A Paradigm Shift in Indian Edtech

6 minutes, 16 seconds read

There has been a significant change in the education industry in India in the past couple of years. From syllabus to teaching methods, from enrollment levels to infrastructure available; technology was majorly responsible for this major shift. Educomp, founded in 1994 and one of the earliest Indian edtech, changed academia with multimedia content, computer labs and teacher training. Today, BYJU’S, founded in 2011, is revolutionizing edtech with its m-learning platform. There’s something more that is contributing to the widespread adoption of e-learning platforms.

As the country came to a standstill with a 21-day nationwide lockdown being imposed, online education companies in India sought this as an opportunity to attract students, academicians, schools, colleges, corporates and the media.

Within the first two weeks of the lockdown, many online and edtech players offered their online courses for free — trying to reach as many audiences as possible. The COVID-19 crisis turned out to be an amazing opportunity for edtech to spread its perimeter and reach out to the audience which was earlier ignorant of this sector. But the question is whether the edtech surge is short-term or will it turn out to be a paradigm shift in India.

Why Online Education?

Government awareness programs have shaped the importance of education in people’s minds, which is that education leads to jobs. But the lack of adequate infrastructure, facilities, and teachers have led to decreasing quality of education. Online education is convenient to access, which is why it is gaining popularity amongst the rural population. In places where there is limited infrastructure, many are turning towards online courses, courtesy — access to the internet.

[Also read: What Makes Saas-based Education Technology in India Effective]

While many cannot afford an institutional education, online education has made it monetarily feasible for the population at large. By 2021, $1.96 billion will be the size of the edtech market in India, a KPMG edtech study reveals. In the current crisis where the lockdown has led to massive unemployment, online education concerning skill enhancement has seen an upsurge.

Technology trends in EdTech

From the introduction of hardware such as projectors and computers in the classroom to learning through tabs and laptops at home, the education industry has evolved tremendously. The ideology behind edtech has been to create newer learning experiences keeping with the pace of rapid digitization. 

Gamification has gained significant popularity amongst Indian education service providers as it has made the learning process interesting. Many edtech players have started adopting technologies like Artificial Intelligence and Machine learning which enable teachers and policy makers to get better insights about their students and modify learning methods accordingly. A lot of research is going into technologies like Virtual Reality and Augmented Reality to create interactive learning modules for better understanding of complex subject domains. In case of long-form answers, natural-language processing (NLP) can make the assessor’s job easy by giving detailed and formative feedback.

[Also read: Top 25 Disruptive Augmented Reality Use Cases]

Cloud-based data storage provides convenience to students who can access and share data easily. With the on-going lockdown and social distancing, there could be scope for untapped technologies such as wearable devices and virtual labs which can take learning experiences to another level. 

For instance, Indian edtech startups like Edureka are very serious about customer experience and are taking AI initiatives for Live Chat Analysis and Career Path Research.

[Read Case Study: Customer experience design in Edureka e-learning mobile app]

Opportunities for Indian EdTech amidst the pandemic

Education can be categorized in different segments such as Primary and Secondary education i.e the K-12 segment, Test Prep, Skill Enhancement, and Higher Education. Schools and colleges have been hit quite a bit due to the lockdown as they remain shut till the situation improves. Even though learning has not stopped as teachers have been taking online lessons, will online education replace a traditional one? 

Many edtech experts say that online learning enables students to interact with a larger pool and gives more focus to individual learning. Certainly, technologies can help create innovative and imaginative learning experiences but can they match with learning through human interaction? That is doubtful. However, edtech would be a very powerful aid for teachers to improve the learning process. 

A research by McKinsey states that teachers spend around 20 to 40 percent of their time on activities which could be simplified by automating using current technologies. This time could be optimized by spending on relevant activities focused on student learning. Children are the future citizens of the world. Teachers have a pivotal role in grooming them towards successful personal and professional life. In order to adapt in the post-pandemic world, technology alone cannot bring the change. The learning experience brought in by a teacher is equally important.

The economic slowdown has made the youth cognizant of the unemployment that may hit the world. The upside to this is that the online education industry will see more enrollments in skill-enhancement courses from both rural and urban population. The digital education initiatives will see a monetary boost by the government. This lockdown has enabled people to pursue their passions and take up online tutorials such as cooking, teaching, writing, learning a different language, fitness, learning musical instruments, and other art. This could potentially lead to a thriving passion economy driven by budding entrepreneurs. 

Probable obstacles to Indian EdTech

Edtech will certainly prove to be a booming sector but there are certain challenges on the way. 

Access to internet and bandwidth issues

One of the biggest challenges to the Indian edtech would be accessibility for the population especially in the rural areas. Issues with internet connectivity, bandwidth, hardware might make it difficult to pursue online courses.

Lack of digital literacy

A major part of Indian populace is still digitally illiterate. Especially, the rural population is still not tech-savvy to understand the features of digital devices. Products with simpler UX suitable for the end-user is the need of the hour. Many edtech players still find it difficult to create user-friendly UX that makes technology easy to apply.

Rising competition

EdTech has been making huge progress in the past 10 years and many have recognized its potential to even grow further with the lockdown. The industry is getting crowded with new entrants which makes it difficult for the consumers to remain loyal to one. Subsequently, it is leading to reduced market share for each company.

Investment in advance technologies

This sector definitely has huge potential. However, with the economic slowdown, huge investment in technologies like AI, ML, AR, VR could get affected. There are huge risks in materializing AI projects and might take some time to receive RoI.   

The Bottom Line

Edtech has its pros and cons but there is no doubt that the industry is here to thrive in the long run. This lockdown has proved that the virtual learning systems can operate. Many education boards have understood its potential to grow and will start integrating technology into their syllabus. 

Furthermore, EdTech could significantly improve the quality of educational content and overall learning experience especially for the rural population. For instance, an edtech initiative — EkStep (a non-profit organization) intended to build an advanced, universal, and collaborative platform for K-12 Education space with a focus on rural India. 

Post pandemic, the world will still follow social distancing for some time but the need for human interaction will not diminish but rather see a craving for it. In the short term and the medium term, the edtech industry can reap the benefits of this crisis but to survive in the long run, continuous innovation in technology that does not substitute but rather aid in the classroom learning is needed.

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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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