Value investing is one of the best known stock-picking methods. The concept is actually very simple: find companies trading below their inherent worth. In other words investment strategy based on buying stock (shares), which appears cheap (is trading at less than its true worth) when the issuer’s current earnings and assets are taken into account. Value-based investing is a fascinating way to generate wealth.
There are many renowned value investors in India who look for stocks with strong fundamentals – including earnings, dividends, book value, and cash flow. They share educational content about this investing and also recommendations in the form of blog posts. Although the content by them is available on the internet but many users have difficulty in finding authentic content and updates on investment.
Equity Robo is designed and developed as a mobile application to solve the problems faced by the user in accessing information about investment-related topics.
Equity Robo has been created to aggregate and bring to the user leading insights from various investment gurus of India. This is a free service meant to let the user know whenever a value investor posts content on his blog. Equity Robo notifies the user and keeps in ahead of the curve when it comes to information related to value-based investing. Value Investing.
The key features of this application are:
Card-based layout for easy reading and navigation through multiple articles.
Initial synopsis of the article with an option to tap and read the complete article on the web
using any of the mobile browsers.
User friendly navigation to return to the main application after reading an article on the web.
Option to turn on/off value investing insights based on interest levels.
Notification on new content arrival and an option to turn on/off notifications.
Bookmarking feature to ensure user can start from where they left off.
Bookmarks to keep curated blog posts in a separate folder.
Backend Technology:
Android studio IDE (Integrated Development Environment) was used in building the app
CI (continuous integration) by Jenkins for creating APK
Java framework 1.8
Redrofit for server integration
Picasa for image uploading
Parse for notification
Green Robot for sending events inside our codes (eventbus)
Flurry for the analytics of an app
Branch for sharing and deep linking data
The app has been developed to keep users up-to-date with value-based investing and other investment related news and information.
To know more about Equity Robo and other app development projects contact us at hello@mantralabsglobal.com
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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground
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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