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Scalable Data Pipelines for Pharma Work Order Analytics Using Databricks

Author(s) Srikanth Reddy Katta, Sudheer Devaraju, Harikanth Devulapalli
Country United States
Abstract Specifically, work orders are one of the critical types of data that the pharmaceutical industry encounters in its work; yet, this data is rather bulky and diverse, which contributes to the appearance of certain challenges. In this paper, the architecture for the scalable data pipeline using Databricks for enhanced pharma work order analytics is described. It is a highly scalable solution utilizing Apache Spark, Delta Lake and deep machine learning techniques for data ingestion, processing and real time analysis. Some of the benefits associated with automation are enhanced operation output, reduced time for processing data, and enhanced quality of data being processed. This paper outlines a specific case of a large pharmaceutical company to illustrate how the solution achieves a 70% cut in how long it takes to execute a query and a 50% boost to the data processing rate. Data latency, fault tolerance, scalability, and other factors are taken into account. Furthermore, issues such as data heterogeneity and regulation rules in the pharma industry have also been discussed in this research to address the integrity as well as security issues in the domain of data management. This paper will seek to give different approaches to building scalable analytics pipelines with Databricks for pharmaceutical work order management.
Keywords Scalable Data Pipelines, Databricks, Pharma, Apache Spark, Delta Lake, Work Order Management.
Field Engineering
Published In Volume 4, Issue 10, October 2023
Published On 2023-10-10
Cite This Scalable Data Pipelines for Pharma Work Order Analytics Using Databricks - Srikanth Reddy Katta, Sudheer Devaraju, Harikanth Devulapalli - IJLRP Volume 4, Issue 10, October 2023. DOI 10.5281/zenodo.14769913
DOI https://doi.org/10.5281/zenodo.14769913
Short DOI https://doi.org/g83kwn
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