Swathi Garudasu Research Profile

About

Swathi Garudasu is a highly experienced Data Engineer and Analytics professional with extensive expertise in big data analytics, ETL solutions, and cloud-based data platforms. Currently serving as a Senior Lead Analytics Engineer at ADP, she has a strong background in data engineering, business intelligence, and cloud technologies. Swathi has worked extensively with Azure Data Bricks, Azure Data Lake, Azure Blob Storage, and Azure SQL, utilizing tools like Spark SQL, Python, Scala, and R for data processing and predictive modeling. She has a proven track record of developing robust ETL solutions for OLTP, OLAP, and ODS systems, optimizing data transformation processes, and enhancing data accessibility for analytics and reporting. Before joining ADP, she held senior positions at Charles River Laboratories and Microsoft, where she played a critical role in migrating Power BI reports to live connections, implementing row-level security, building complex DAX measures, optimizing data quality rules, and orchestrating Azure Data Factory (ADF) pipelines to automate ETL processes. Swathi also has deep experience with Microsoft SQL Server, business intelligence tools like Power BI, and various reporting solutions, having developed complex reports using SSRS and Power BI with OLTP and OLAP databases. Her tenure at People Tech Group and HCL Technologies further solidified her expertise in database design, T-SQL, stored procedures, performance tuning, database migration, and ETL package development using SSIS and Azure Data Factory. With a strong foundation in data modeling and administration, she has worked on designing and developing OLAP cubes, implementing data governance strategies, and leveraging SQL Server Profiler for query optimization. Additionally, she has experience with Google Analytics 4 for web activity analysis, Power Apps integration with Power BI, and developing CI/CD pipelines for analytics solutions. She holds a Bachelor of Science in Computer Science from St. Francis College for Women and has further honed her technical skills through certifications, including Tableau Essential Training. Swathi has been recognized as a judge for the Globee Awards for Women in Business, showcasing her leadership and contributions to the tech industry. Her expertise in cloud computing, big data, and analytics, combined with her hands-on experience in database management and business intelligence, makes her a highly skilled professional in the field of data engineering. Experience in handling Development projects and Production Support Projects & working in Agile Environment Experience working with Azure Data Bricks to pull data from different sources like Azure Blob storage, Azure Data Lake using Scala, Processed data using Spark SQL, Python, R (Predictive models) and load it to Azure SQL/Azure Datawarehouse/Azure Data Lake/Azure Storage. Worked with Microsoft SQL, Business Intelligence and dealt with various development and administrative activities. Experience in Developing Robust ETL Solutions which are used for developing/maintenance of OLTP, OLAP and ODS Data Solutions. Experience in Writing Scope Scripts for loading the data from COSMOS Server to Relational Database for use in Reporting Experience in working with PowerBI for creating PowerBI reports and dashboards which present data from Relational or Tabular Models

Skills

Experience

Senior Lead Analytics Engineer

ADP

Feb-2024 to Present

Software Engineer II

Microsoft

Jul-2019 to May-2023

SQL Developer

Vertex Computer Systems

Jun-2008 to Jan-2010

Software Engineer

People Tech Group Inc

Feb-2014 to Feb-2019

Technical Lead Engineer

HCL Technologies Ltd

Jan-2010 to Feb-2014

Senior Analytics Engineer

Charles River Laboratories

May-2023 to Dec-2023

Education

St.Francis College for Women

B.Sc. in Computer Science

Passout Year: 2005

Symbiosis Institute of Technology, Symbiosis Inter...

PGDIT in Computer Science

Passout Year: 2003

Peer-Reviewed Articles

DEVELOPING A DATA-DRIVEN ARCHITECTURE FOR IMPLEMENTING AI-ENABLED DYNAMIC PRICING STRATEGIES IN THE AUTOMOTIVE INDUSTRY

In the Automotive Industry, dynamic pricing is used a lot to make the most money and hold off the competition. The Automotive industry is using AI to build a data-centric framework that will allow dynamic pricing. This research will look at how they are doing it. Automakers can find out about how customers act, how the market is changing, and how competitors plan to beat them by using complicated formulas and strict data collection methods. The aim of this research is to analyze how dynamic pricing protects prices in various industries, with a particular focus on its application in the automotive industry. In addition, the research will discuss about data-driven design approaches incorporating with artificial intelligence (AI), mainly how these technologies could be used to improve pricing strategies by automating choices and letting prices adjust based on the market. Important things like how to use market trends to our advantage, gather and analyze data, and understand how customers behave, and merchandise sales are the focus areas of the paper. As part of the project, AI could also be used to improve pricing methods. Some of these are prediction analytics, machine learning, and reinforcement learning. We can figure out how to make the most money and guess what prices will be in the future by using algorithms that look at past price data. Finally, the study shows that price strategies that are driven by AI and design that is driven by data can have a big impact on the automotive industry. Businesses in the Automotive industry might be able to boost competition, new ideas, and customer trust by using dynamic pricing systems and staying honest all the way through.

Scalable Data Partitioning and Shuffling Algorithms for Distributed Processing: A Review

Scalable data splitting and shuffle algorithms have emerged as crucial elements of effective data processing in distributed computing and big data. This article provides an in-depth analysis of the complex terrain of these algorithms, which play a crucial role in ensuring efficient data distribution, load balancing, and resource optimisation in distributed systems. Among the most important discoveries are the varying functions performed by algorithms like hash-based, range-based, and sort-based techniques. The importance of measurements like data transmission overhead, processing time, and network utilisation in illustrating the impact of various algorithms on performance is emphasised. Challenges, such as algorithmic complexity and the never-ending search for efficiency and adaptation, remain despite their evident importance. The ramifications affect a wide variety of parties. Adaptive algorithms, privacy protection, and energy efficiency are all areas where researchers may make strides forward. Insights for optimised data processing operations, including careful algorithm selection and performance adjustment, might benefit practitioners. Leaders are urged to appreciate the algorithms' strategic value in realising data-driven goals and to invest wisely in the systems and personnel needed for effective distributed processing. As a result, organisations are able to extract meaningful insights, make informed real-time decisions, and navigate the ever-changing world of big data to scalable data division and shuffling algorithms.

Scholar9 Profile ID

S9-112024-1206396

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Article Reviewed

(32)

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Ms Swathi Garudasu