Finance has always been the core of any business being done, it caters as platform on which other sectors work upon. With years and years of research towards achieving maximum efficiency in this sector, new age technologies like AI, Machine Leaning and Data Science are now taken into account. This has resulted in birth of an advance AI based system that adapts and learns, from its surrounding.
ForwardLane is driving a new wave of financial innovation through leveraging advances in cognitive computing and data analytics.
ForwardLane brings personalized high net worth investment intelligence to numerous investors around the globe. This is done, by mimicking and accelerating the human investment process using artificial intelligence and combining it with professional risk analytics. It is a B2B platform used by private banks, wealth managers, digital banks and insurance companies.The team behind ForwardLane is comprised of wealth management specialists with a combined experience of over 175 years. It is backed by a team of highly qualified and experienced engineers that execute the core technologies deployed in serving the customers. The company is supercharging the financial advisor and bringing superior financial advice to mass affluent clients.
The Platform offers vast range of functions such as:
In order for such a complex and highly intelligent system to work as planned, ForwardLane rely on data processing, this is achieved thorough extracting relevant information from clusters of data the platform collects. Mantra Labs is using Data Science by providing a dedicated team of problem solvers that assist ForwardLane’s innovative finance management goals. This enables efficient Data processing and timely deployment of resources that the platform truly depends upon.
Knowledge thats worth delivered in your inbox
Next Post
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.
Knowledge thats worth delivered in your inbox
Next Post
Loading More Posts
Connect with Us!
Thanks for reaching out
Our Sales Team will be in touch with you shortly.
Hello Stranger! Please fill in a few details,and you’ll receive a link to this case study.