Since childhood, Damodarrao has been fond of computers and learned programming at an adolescent age. He has always been a highly motivated, focused, and enthusiastic individual and realized the skills he had even when he was just a child. To make the most of those skills, he moved to the US in 2008 to fulfill his American dream, and he completed his Master’s degree in Pittsburgh, PA.
Damodarrao Thakkalapelli is a Microsoft Data Solution Architect, specializing in the architecture of complex solutions in Azure, but also on-premises products such as Active Directory, Exchange, Windows Server products and other Microsoft technologies and solutions.
The role as Data Solution Architect includes maintaining relationships with customers and ensuring a smooth deployment of services to Microsoft Azure. This includes designing proof-of-concepts and implementing those, helping customers with templates and recommended architectures. Translating business and technical requirements into solid cloud-ready deployments is one of the key aspects of the Solution Architect role.
He is very passionate about solving enterprise data architecture problems and ensuring customers achieve their desired business outcomes. His recent research to provide solutions to Current Data processing model issues is really impressive. Some of these solutions are applied to one of the biggest financial institutions in the world, and these solutions are filed as patent applications at the USPTO.
Patent US-11379440-B1: This patent provides a solution for Synchronization issues in a large production environment with an alternative environment to test a fix and release to production with minimal latency. It explains how to identify and synchronize a minimal data set from a large production environment to a smaller alternative environment. It provides Data migration solutions between heterogeneous environments with minimal oversight, resources, and cost.
Patent US-11604691-B2 big data warehouses, there will be thousands of jobs run in nightly batches with strict SLAs that need continuous monitoring. Currently, teams of support personnel manually monitor, often more than one application at the same time, and react to any issues. Manual monitoring is monotonous, routine, laborious and human-resource intensive. The invention uses Machine Learning in a systematic way to build a framework to monitor end-to-end batch process systems that learn, heal, and improve themselves.
The AI/ML system learns the structure, schedules, pace of the run, errors, and fixes of a batch process as it monitors. Improves itself to be more efficient over time and improves reliability, stability, and scalability. Decisions will be taken in no time with decentralized autonomous organization (DAO) to minimize and/or eliminate the need for complex schedules of global teams in multiple time zones to monitor systems.
Patent US-20230229339-A1: In organizations, Data is ingested by batch and/or application processes daily into relational databases. Relational data models enforce referential integrity to maintain data consistency. This is typically accomplished by setting dependencies on relevant jobs/processes. However, dependencies enforced by referential integrity force batch and/or applications to ingest data in a serial fashion. The referential integrity could lead to process or job errors when a parent-child link is missing. Thus, the dependencies lead to frequent SLA breaches.
Patent US-20220358127-A1: SQL query interpreter focuses on predicting query performance based on plans and query performance available in databases. Further details around the usage of machine learning techniques on predicting a query performance in isolation though features which affect run-time are evaluated based on static and dynamic factors and ranks them, buckets them based on % of similarity, uses them to create a tool or interface to the user which acts like an interpreter. Dynamically organize data (automatic normalization, de-normalization, purge, partition etc.) based on the table/column usage and time of day by users or applications and group them based on frequency of execution. The framework will move data out into separate files or merge (directories in case of list bucketing) dynamically.
Damodarrao Thakkalpelli’s visionary brings an extensive background in IoT, telecommunications, and advanced technologies, with nearly two decades of experience. He currently serves as the Data Solution Architect with Bank Of America. In his roles, he has made significant strides in the FinTech and industry domains, leveraging AI, machine learning, and augmented intelligence technologies. With a Master of Engineering degree in Electronics from Pennsylvania and numerous patents in Data Architect, Thakkalapelli continues to lead the way in creating impactful solutions for complex societal challenges.
