Optimizing Data Management in Fintech: A Holistic Approach with Genese and Amazon Redshift

Gandalf Insights

Gandalf Insight is a Generative AI-powered fintech data engineering application that leverages advanced machine learning algorithms to automate and enhance various aspects of data engineering within the financial technology sector. The application, Gandalf Insight, focuses on user-friendly interaction, and generates real-time analytics, providing insights that help drive informed decision-making.

Problem Statement

Gandalf, a data-centric organization utilizing Amazon Redshift, faced several challenges in organizing and optimizing data storage, resulting in unstructured and unoptimized data. This inefficiency led to prolonged query execution times, uneven workload distribution across nodes, resource contention, scalability issues, and data scanning challenges in the data lake. Partitioning the data proved to be a formidable task, hindering efficient data retrieval and analysis.

Despite the use of Amazon Redshift, the absence of role-based access (RBAC) posed a significant problem for Gandalf. Inefficient access management increased security vulnerabilities and data integrity risks. Individual permissions granting and modification led to inconsistencies, potential errors, and unauthorized access to sensitive data. This lack of structured access control elevated the risk of data breaches and internal security threats, complicating access privilege management.

Increased storage costs became a concern for Gandalf due to a surge in data storage requirements. Uncompressed data negatively impacted query performance, necessitating larger data reads from disk during query execution, resulting in prolonged processing times, reduced responsiveness, and increased network traffic.             


Gandalf partnered with Genese to implement a comprehensive solution, addressing not only the challenges the customer was facing but also the complexities related to data scanning in the data lake. They adopted role-based access control (RBAC) within the Amazon Redshift environment, designating roles such as Financial Analyst, Data Scientist, Managerial roles, and System Administrator. Genese’s expertise significantly contributed to mitigating security and access control issues, enhancing operational efficiency.

To tackle the data scanning challenges in the data lake, Gandalf, with Genese’s guidance, incorporated Amazon Glue services. Glue Crawler was employed to automatically discover and catalog metadata from Gandalf’s data lake. This facilitated the creation of a centralized metadata repository in the Glue Database, allowing for efficient data organization and discovery. Glue ETL connections were utilized to transform and prepare the data for Redshift, enabling seamless integration between the data lake and the Redshift environment.


Precise permissions were granted to each role, preventing unauthorized access and minimizing the risk of data breaches. RBAC, implemented with Genese’s guidance, facilitated accurate tracking, auditing, and monitoring of user activities, enhancing accountability and ensuring users only accessed necessary data.

The integration of Workload Management (WLM), guided by Genese, enabled query prioritization through queue configurations. This strategic approach prevented resource contention, ensuring critical workloads received the necessary resources and execution priority.

Concurrency scaling, implemented with Genese’s expertise, further handled workload spikes by automatically provisioning additional clusters, maintaining consistent query performance during peak times.

Genese’s involvement extended to adopting effective compression techniques within the database to address storage challenges. Leveraging Redshift’s compression capabilities, guided by Genese, optimized storage efficiency, reducing costs by compressing similar data together. The addition of Glue services not only streamlined the data lake scanning issues but also enhanced the overall efficiency and effectiveness of Gandalf’s data management system.


Gandalf’s overhaul of the data management system brought about a significant transformation, resembling a major system upgrade. Through the strategic implementation of Role-Based Access Control (RBAC), Workload Management (WLM), and compression techniques, the system underwent a reorganization that resulted in substantial cost savings and significantly enhanced its performance in handling queries, resource utilization, and overall scalability.

The impact of these changes on query speed was impressive, with queries now executing five times faster, leading to a noticeable improvement in overall operational efficiency. Security measures implemented, including fortified data protection and a remarkable 90% reduction in unauthorized access attempts, markedly increased the safety of the data. Financially, the operations became more streamlined and cost-effective, experiencing a 40% reduction in expenses due to optimized storage usage and more efficient data processing.

The system’s adaptability and improved handling of increased data volumes became evident when it successfully managed twice the daily data load without any performance degradation. In essence, Gandalf’s data management overhaul serves as a compelling use case, showcasing not only the financial and operational benefits but also the system’s enhanced speed, security, and scalability in real-world scenarios.