Building AI-Powered Data Pipelines: Streamlining Elasticsearch to BigQuery Integration with Python

EasyChair Preprint 15162

Abstract

In the era of big data, organizations rely on efficient data pipelines to transfer, process, and analyze vast datasets. This article explores the creation of AI-powered data pipelines, specifically focusing on the integration between Elasticsearch and BigQuery. It highlights the key techniques to streamline the data flow between these platforms, enhancing the potential for AI-driven insights. The goal is to demonstrate how businesses can optimize data management and analytics by building robust, scalable, and intelligent data pipelines.

Keyphrases: AI-powered pipelines, Big Data, BigQuery, Data Analytics, ETL, Elasticsearch, data integration, machine learning

@booklet{EasyChair:15162,
  author    = {Toluwani Bolu},
  title     = {Building AI-Powered Data Pipelines: Streamlining Elasticsearch to BigQuery Integration with Python},
  howpublished = {EasyChair Preprint 15162},
  year      = {EasyChair, 2024}}