Analyze and Optimize Data Pipelines with Effective Data Models
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© 2024 by IJCTT Journal
Volume-72 Issue-7
Year of Publication : 2024
Authors : Piyush Pandey
DOI :  10.14445/22312803/IJCTT-V72I7P111

How to Cite?

Piyush Pandey , "Analyze and Optimize Data Pipelines with Effective Data Models ," International Journal of Computer Trends and Technology, vol. 72, no. 7, pp.87-92, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I7P111

Abstract
In the current data-driven world, all organizations rely on data warehousing solutions to conduct their daily operations and decision-making. Refreshing the data in analytical data warehouses in a timely manner is one of the critical goals of the data operations team. Technology has advanced a lot over the last couple of decades with the evolution of innovative and powerful processing engines, e.g. Spark, Hadoop, advanced databases, etc. But new challenges like increasing data volume, integration of additional sources, complex transformations, datasets for new use cases and unforeseen issues keep the operation teams on their toes. Generally, teams tend to add more resources (CPU, RAM, etc.), which is an easy way out for temporary respite. However, nothing comes for free – more resources mean increased infra costs. Hence, there is a need to dig deeper and analyze the ETL [1] processes to identify the bottlenecks and suggest corrective actions/ design changes. While doing a deeper analysis, the run history of ETL jobs is crucial for ensuring data integrity, optimizing performance, and maintaining overall system health. There should be enough buffer time to meet SLAs [2] in case of abends or unforeseen issues. Most of the research on ETL performance is focused on the "how" to optimize data refresh times, but there is less research done to identify "what" to optimize. Moreover, analysis and optimization of ETL require not only the technical skillset, but also a functional understanding of the nature of data. This article talks about approaches to analyze an ETL run and identify what are the problematic ETL steps. This article also talks about processes and ways to improve pipeline performance based on appropriate data models [3] and actions, with a knowledge of domain data.

Keywords
Data Pipeline Analysis, Effective data models, ETL Extract Transform and Load, Optimize Datawarehouse.

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