Move over Hadoop, here come the ice cool Data Lakes

While Hadoop is often associated with the early days of big data, it's crucial to recognize its foundational role in shaping the modern data lake landscape. A sneak peek into Hadoop's role in Data Lake Evolution

1. Pioneering Distributed Storage: Hadoop's HDFS (Hadoop Distributed File System) was a groundbreaking technology, demonstrating the feasibility of storing massive datasets across multiple nodes. This concept laid the groundwork for the distributed storage architectures that underpin today's data lakes.

2. Introducing Distributed Processing: MapReduce, Hadoop's processing engine, introduced the idea of parallel data processing across clusters. This parallel processing paradigm is essential for handling the scale and complexity of data lake workloads.  

3. Democratizing Data Access: Hadoop's open-source nature fostered a vibrant ecosystem of tools and technologies, making big data accessible to a wider audience. This democratization of data access has been instrumental in driving data lake adoption.

4. Handling Diverse Data Formats: Hadoop's ability to store and process various data formats (structured, semi-structured, and unstructured) was a significant leap forward. This flexibility is a cornerstone of modern data lakes, which often deal with a mix of data types.  

Hadoop, while groundbreaking, encountered several limitations as data volumes and complexities grew. These challenges paved the way for the development of new data table formats

1. Performance Bottlenecks:

   • Data Serialization: The process of converting data into a format suitable for storage or transmission was inefficient, impacting query performance.

   • Data Locality: Moving data between nodes for processing often resulted in performance degradation.

   • Write Amplification: Frequent updates to data led to excessive write operations, impacting system performance.

2. Schema Evolution:

   • Rigid Schema: Hadoop's schema-on-write approach made it challenging to handle evolving data structures.

   • Data Inconsistencies: Changes to data schemas could lead to data inconsistencies and errors.

3. Complex Data Management:

   • Metadata Management: Managing metadata for large-scale datasets was cumbersome and error-prone.

   • Data Governance: Ensuring data quality, security, and compliance was challenging.

4. Limited Real-Time Capabilities:

   • Batch Processing Focus: Hadoop was primarily designed for batch processing, making it less suitable for real-time analytics.

The banking industry is entering the Data Lake era, driven by the rise of modern data formats like Parquet and ORC, and their evolution into advanced table formats such as Delta Table, Hudi, and Iceberg. In this new landscape, banks need more than just storage—they need flexibility, scalability, and seamless integration across diverse systems.

Drona Pay’s Data Platform is built to help Banks & Insurance players transition to this new era with ease. Leveraging Apache Iceberg or Delta Lake, our platform enables seamless integration across critical systems such as Core Banking, Card Management, Loan Management, Co-lending Platforms, Policy Management, Claims Platform, Treasury, Internet and Mobile Banking, Corporate Banking, and Payments. Whether deployed on or off premise, Drona Pay’s comprehensive solution provides a future-proof data management infrastructure, ready to meet the demands of today and tomorrow.

Drona Pay's Data Platform helps Banks with use cases including Early Warning System (EWS), Regulatory Reporting, Operational Reporting, VaR modelling and Complex What-If modelling.