Pavan Kumar Manneru

The process of check clearing in the UK had undergone an important and necessary change. The main difference was that instead of exchanging physical paper checks, UK banks had begun to exchange digital images of these checks.

I was a part of the team that designed a solution to process these images and data at the same speed. Introducing the concept of Microsoft SQL Server in-memory OLTP, I helped the organizations load and process the images and data of around two million checks with very high throughput.

Serving as the organization-wide data warehouse architect, I helped the senior management in the decision-making and creating the most useful Tableau dashboards.

Working as a lead BI and data engineer in the healthcare industry, I was challenged to migrate the existing Apache Kafka event capturing and streaming platform to the cloud, preferably Azure.

The existing platform, Apache Kafka Cluster, performed a lot of real-time recording operations like ICU monitoring, diabetes results recording, and blood pressure metric recording.

Using the Azure services like Azure Event Hubs, Data Factory, Data Lake Storage Gen2, and Synapse, I was the prime architect of the entire project from the data producer side to the data consumer side.

This migration project enabled our healthcare provider to apply predictive analytics to data in motion for continuous decisions, allowing them to capture and analyze data—all the time, just in time. The end goal is to save lives, shorten hospital stays, and build healthier communities through preventive care.

This project's aim was integrating 1,000 Tesco UK retail stores data to the head office. I served as a BI/ETL engineer working with business analysts to understand and integrate the data using various technologies.

The whole workflow included both OLTP and OLAP processes.
OLTP processes were used to record and transfer the sales, prices, and promotion information from stores to the head office on a near real-time basis.

OLAP processes to turn the sales and other data into a reportable and presentable data warehouse using various ETL/ELT processes and then presenting them in intuitive dashboards in the Microsoft Power BI.

Working as a lead BI and data engineer in the healthcare industry, I was given the challenging task of migrating the existing on-premises data warehouse to the cloud, preferably BigQuery of Google Cloud Platform (GCP).

Using Apache Beam in conjunction with Python, multiple data pipelines were created to load the data from numerous sources like the CSV files, Excel spreadsheets, Access databases, and on-premises SQL Server databases. The transformations had been seamlessly done using Python before finally moving the data to the BigQuery.

The BigQuery had then been exposed to the Tableau for creating multiple intuitive dashboards.

This migration project enabled our healthcare provider to apply predictive analytics to data in motion for continuous decisions, allowing them to capture and analyze data—all the time, just in time. The end goal is to save lives, shorten hospital stays, and build healthier communities through preventive care.

Working as Data Analyst/Engineer in the livestock industry, I was tasked with ingesting various livestock specie information into the AWS Data warehousing infrastructure, mainly into Redshift. Ingestion was facilitated using multiple Amazon Web Services like AWS Lambda functions and AWS Glue. The data was sent into an S3 bucket. This would either be picked up by the AWS Lambda function or AWS Glue ETL Pipeline. With corresponding transformations, it would be loaded into AWS Redshift. Redshift was then exposed to Tableau to create multiple intuitive dashboards using SQL as the querying language. This data analytics project enabled the company to effectively manage livestock disease tracing and maintain data in one place.