Modern Data Engineering for Enterprises: Harnessing Python, PySpark, Cloud Platforms, and Real-Time Analytics

  • Authors

    • Karthik Allam Big Data Infrastructure Engineer at JP Morgan & Chase, USA. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2024PI5R7X

    Published 02-14-2024

  • Data Engineering, Python, PySpark, Cloud Computing, Real-Time Analytics, Big Data, Data Pipelines, Distributed Computing, Enterprise Data Architecture, Data Processing

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

  • Abstract

    Today's enterprises constantly generate large amounts of data from applications, IoT devices, consumer interactions, and digital platforms that are both structured and unstructured. As such, data engineering must be very efficiently implemented so that businesses can make informed decisions. This paper discusses how companies may develop, not only scalable and trustworthy, but also smart, data ecosystems through the exploitation of Python, PySpark, cloud platforms, and real-time analytics. Our main goal is to determine the contribution of each of these technologies towards the design of contemporary data pipelines capable of offering high-performance data processing, smooth integration, and quicker business insights. The research will be conducted from an action-oriented and system architectural perspective by dissecting complete data engineering processes from the point of generating to consuming data including ingestion, transformation, storage, orchestration, and visualization, within cloud-native environments. Python is going to be considered due to its capability to manipulate and automate data, on the other hand PySpark is going to be explored because of its excellent capacity to process very large and highly distributed datasets. Besides, with regards to elastic infrastructure provision, cost reduction, and the fact that data services are already managed by them, cloud platforms can help the enterprises to scale operations without heavy hardware investments. On top of all that, this study also talks about real-time analytics methods to show the capability of the continuous data processing in the detection of data patterns, prompt responses to incidents and the improvement of overall efficiencies. The conclusions reveal that the combination of these technologies largely improve the level of data integrity, the speed of data processing, and the system resilience while minimizing the operational complexity and the cost of running the processes. Besides, it emphasizes through a concrete example that the successful enterprise architectures should include such things as automation, governance, security, and control monitoring.

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