Pavan Kumar Manneru

英国的支票清算过程发生了重要而必要的变化. 主要的区别在于，他们不用交换纸质支票, 英国各银行已经开始交换这些支票的数字图像.

I was a part of the team that designed a solution to process these images and data at the same speed. 介绍了Microsoft SQL Server内存OLTP的概念, I helped the organizations load and process the images and data of around two million checks with very high throughput.

作为组织范围的数据仓库架构师, I helped the senior management in the decision-making and creating the most useful Tableau dashboards.

在医疗保健行业担任首席BI和数据工程师, 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, 进行了很多实时记录操作，比如ICU监护, diabetes results recording, 还有血压测量记录.

使用Azure服务，如Azure事件中心, 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, 让他们能够随时捕捉和分析数据, just in time. The end goal is to save lives, shorten hospital stays, 通过预防保健建立更健康的社区.

该项目的目标是将1000家乐购英国零售店的数据整合到总部. I served as a BI/ETL engineer working with business analysts to understand and integrate the data using various technologies.

整个工作流包括OLTP和OLAP两个过程.
OLTP流程用于记录和转移销售, prices, 并且促销信息几乎实时地从商店传到总部.

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.

在医疗保健行业担任首席BI和数据工程师, I was given the challenging task of migrating the existing on-premises data warehouse to the cloud, 谷歌云平台(GCP) BigQuery优先.

将Apache Beam与Python结合使用, multiple data pipelines were created to load the data from numerous sources like the CSV files, Excel spreadsheets, Access databases, 和本地SQL Server数据库. 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, 让他们能够随时捕捉和分析数据, just in time. The end goal is to save lives, shorten hospital stays, 通过预防保健建立更健康的社区.

在畜牧业担任数据分析师/工程师, 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. 数据被发送到S3桶. 这将由AWS Lambda函数或AWS Glue ETL Pipeline拾取. 通过相应的转换，它将被加载到AWS Redshift中. 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.