ESP Journal of Engineering & Technology Advancements |
© 2021 by ESP JETA |
Volume 1 Issue 2 |
Year of Publication : 2021 |
Authors : Santosh Kumar Singu |
: 10.56472/25832646/ESP-V1I2P119 |
Santosh Kumar Singu, 2021. "Designing Scalable Data Engineering Pipelines Using Azure and Databricks", ESP Journal of Engineering & Technology Advancements, 1(2): 176-187.
Data engineering pipelines can be seen as the fundamental structure of today’s modern data-driven organizations, as they are responsible for processing large amounts of data and preparing it for analysis. Since today’s organizations are investing more in cloud solutions for their pipelines, these have to be scalable and flexible. The focus of this paper is the actual design of scalable data engineering pipelines using Microsoft Azure and Databricks as the two setup platforms in the handling of large-scale data operations. Azure is an advanced and highly scalable cloud solution that comes with such services as Azure Data Lake, Azure Synapse Analytics, and Azure Data Factory. However, Databricks provides a unified analytics data-n Architecture that assimilates with Azure and provides Apache Spark analytics and modish machine learning applications. Combined, the above technologies and methods form a strong compilation of data pipeline technologies and methods to be used by organizations in building highly scalable and efficient data processing pipelines that are not prone to bottlenecks. In this paper, the basic architectural concerns and elements needed to construct fault-tolerant pipelines are discussed. The subjects covered include data ingestion solutions, data storage using Azure Data Lake, real time processing with Databricks, and data management using Azure Data Factory. Particular emphasis is placed on data coherency, latency, as well as pipeline throughput. Other issues include scalability, which looks at the issues of managing large amounts of data, providing redundancy in the system and efficient resource usage in distributed systems. The interactions between Azure and Databricks are also discussed in detail and focus on the proper setting to have scalable and cost-optimal pipelines. In this paper, we consider an end-to-end process of constructing the scalable pipeline for realtime data analytics in the financial sector and demonstrate the approach and the results. A comparison and contrast of current batch processing pipelines and new realtime streaming pipelines is also presented. The paper ends with the prospective directions of development of the scalable data engineering concept and the ways organizations can expand the efficiency of the pipeline with the help of new tendencies such as serverless computing and artificial intelligence.
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Data Engineering, Azure, Databricks, Scalable Pipelines, Cloud Computing, Apache Spark, Data Ingestion, Fault Tolerance.