AI-Powered Data Engineering: Automating Pipelines for Real-Time Analytics and Quality

@article{1702759,
      author = {Allen R Chan},
      title = {AI-Powered Data Engineering: Automating Pipelines for Real-Time Analytics and Quality},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {4},
      number = {7},
      pages = {145-155},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702759.pdf},
      abstract = {Organizations are producing massive amounts of data at warp speed, yet classic data engineering approaches seldom keep pace. Slow updates, manual pipeline creation, and error prone data transformations limit the organization from getting real-time insights. On the other hand, AIpowered data engineering eliminates manual processes like pipeline configuration, real-time monitoring, and data quality checks. This automation allows data teams to focus their efforts away from these manual maintenance tasks and on high-level innovation instead. Studies suggest that by implementing AI, enterprises are able to convert crude data into actionable intelligence much quicker and more reliably. In this article, we look into how AI optimizes data pipelines, enhances real-time analytics, and improves data quality. It also looks to the future and discusses the changing landscape of AI-powered data engineering and offers up notable advantages, issues, and considerations.},
      keywords = {AI-Powered Data Engineering, Automated Data Pipelines, Real-Time Analytics, Data Quality, Machine Learning, Schema Evolution, Data Automation, Advanced Analytics, Artificial Intelligence, Data Processing.},
      month = {January},
  }