Evolving Data Engineering Landscape: Integrating Modern Data Stacks With Scalable, Cost-efficient Data Lakes for Future Ai and Ml Needs
Year: 2024
Type: article
Abstract: The data engineering landscape is rapidly transforming to meet the growing demands of AI and machine learning (ML).Traditional monolithic data architectures are being replaced by modular, cloud-native data stacks that prioritize flexibility, scalability, and cost-efficiency.This paper explores the integration of modern data stack components-such as ELT pipelines, real-time data streaming, and cloud data warehouses-with scalable data lakes that serve as unified repositories for structured and unstructured data.We discuss best practices for designing data platforms that can seamlessly support AI/ML workflows, including metadata management, data versioning, governance, and interoperability across tools.Additionally, we analyze cost-performance tradeoffs and architectural patterns that enable organizations to future-proof their data infrastructure while optimizing for real-time analytics, model training, and data democratization.By bridging the gap between modern data stacks and next-generation data lakes, organizations can unlock the full potential of their data to drive innovation in AI and ML.
Author: Harshavardhan Chinthalapalli
Institution: Cognizant (United States)
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4410856214