The enterprise AI market will reach $204 billion by 2030. Ninety-two percent of organizations plan to increase their AI investments over the next three years. Yet MIT research shows 90% of AI projects ...

With Apache Spark Declarative Pipelines, engineers describe what their pipeline should do using SQL or Python, and Apache Spark handles the execution.

As digital transformation accelerates, now is the time to revisit your integration strategy and unlock the full potential of your enterprise data.

GCP Uber Data Pipeline Project This project demonstrates an end-to-end Google Cloud Platform (GCP) data pipeline for analyzing Uber data and visualizing it in a Power BI dashboard.

Google has added new agents to its BigQuery data warehouse and Looker business intelligence platform to help data practitioners automate and simplify analytics tasks.

This project demonstrates a fully functional ETL pipeline that extracts, transforms (including sanitization of data using the Pandas library), loads, and analyzes data. The pipeline leverages Google ...

The AI-driven ETL pipeline dynamically adjusts data extraction, transformation, and loading processes, resulting in significant improvements in data integration performance.

Flexible deployment of ETL flows in hybrid-cloud environments. Scalability of ETL flows. Microservices-like agile and quick deployment of ETL flows. Support for streaming ETL operations.

Google brings Cloud Monitoring metrics in BigQuery as a new capability in preview to combine billing data with resource utilization metrics. The combination allows users to perform detailed cost ...