Govindaiah Simuni is passionate about addressing enterprise data architecture challenges, improving batch processing, and helping customers strive toward their desired business outcomes. He has worked on designing comprehensive solutions as a solution architect, considering a variety of elements like hardware, software, network infrastructure, data management, and batch systems.

As a Data Architect, he evaluates multiple technological options and makes informed decisions based on compatibility, cost, and recognized industry practices. He often contributes to the implementation process, providing guidance to development teams and addressing technical issues as needed. He also offers recommendations for technologies that align with the organization’s long-term strategy. His focus is on ensuring solutions are designed to adhere to best practices, meet quality expectations, and support business objectives.

Here are few of the challenges and solutions for Technology transformation from Legacy to Cutting Edge Technologies.

Challenges

• Continued Growth and Escalating Costs: Data growth is expected to surpass the capacity of current database licensing and storage infrastructure. With increasing data, scalability becomes difficult, and both batch and real-time data processing times increase significantly.
• Outdated Technology: The existing database infrastructure stack is categorized as a legacy system that does not align with modern technological demands.
• Increasing Demands for Data and Insights: Organizations require more timely and accurate data to support better and faster decision-making.

Technology Transformation

• Application Migration: Applications were transitioned from legacy systems to modern, cloud-based platforms. This involved designing data processes with batch execution capabilities and implementing end-to-end automated execution workflows.
• AI/ML Integration in Automation: The data process was enhanced using an AI/ML-based framework, facilitating automated batch processing and monitoring.
• Data Synchronization and Migration: Innovative solutions were implemented to streamline data synchronization and migration processes.
• Optimization of Data Transfer Paths: In complex system architectures, AI/ML algorithms were utilized to compute optimal data transmission paths and reduce data hops. This approach aims to lower transmission costs and improve data communication efficiency.
• System Design and Integration: Collaboration with cross-functional teams, including product managers, developers, and data analysts, helped design and integrate automation solutions into cloud platforms. A thorough analysis of existing manual workflows identified inefficiencies and enabled the proposal of appropriate automation strategies to streamline operations.
• Workflow Automation: Automated workflows were implemented using scripting languages and workflow management tools. The end-to-end automation solution supports faster application processing.
• Batch Processes: Significant batch processes were developed using modern technologies, which help make batch processing more efficient, cost-effective, and better suited for cloud platforms. This included design, testing, and implementation of end-to-end batch processes.

Benefits

• MIPS Reduction: Reducing MIPS (Million Instructions Per Second) can contribute to cost savings for organizations.
• Enhanced Resilience and Availability: The updated systems are designed to improve resilience and ensure higher availability, minimizing downtime.

Due to the rapid growth in business needs and process regulations, organizations often integrate additional systems to manage the data required for operations. This can lead to increased system dependencies and more points of data transfer between systems. In some cases, a downstream system may require data from multiple intermediaries. If any system fails to process critical data, the impact may extend to connected systems, potentially leading to SLA (Service Level Agreement) challenges.

AI/ML technologies can be applied to better understand system dependency maps and compute optimized paths for data transmission and hops between data sources and destinations. These computations can be aligned with data management guidelines set by the organization, regulatory bodies, or legal requirements. Additionally, AI/ML algorithms may assist in categorizing data elements by transformation types (e.g., pass-through, computed, etc.) based on metadata and historical load patterns. These insights can help organizations reduce data transmission costs and improve overall efficiency.

Published by Nicholas A.