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Research Paper | Data & Knowledge Engineering | United States of America | Volume 12 Issue 10, October 2023
Evolving Paradigms of Data Engineering in the Modern Era: Challenges, Innovations, and Strategies
Alekhya Achanta [6] | Roja Boina [5]
Abstract: The exponential data volume, velocity, and variety growth in the digital era have profoundly impacted data engineering. Traditional paradigms centered on batch processing in on-premise data warehouses must be revised for emerging real-time, large-scale use cases. This paper examines modern data engineering challenges, including complex distributed architectures, diverse data types, speed and agility demands, skills shortages, governance needs, and accessibility requirements. Current innovations in cloud computing, data lakes, streaming architectures, metadata management, machine learning automation, and self-service platforms are highlighted as strategies to address these challenges. However, more than technology is required. The paper emphasizes the critical importance of developing new data-driven cultures, processes, and organizational structures. Success requires a holistic approach encompassing technological capabilities, data literacy programs, collaborative workflows, and leadership commitment to an analytics-first mindset. Though daunting obstacles remain, the purposeful evolution of paradigms can unlock tremendous latent value in ever-growing data assets.
Keywords: Agile data delivery, Analytics modernization, Data-driven culture, Data engineering transformation, Data platform innovation
Edition: Volume 12 Issue 10, October 2023,
Pages: 606 - 610