{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"<iframe src=\"https://embed.acast.com/$/670d2bd3883ece12585b0859/670e4887d21580773f93ce89?\" frameBorder=\"0\" width=\"700\" height=\"250\"></iframe>","title":"Next-Gen Data Lakehouses with BigQuery and Iceberg","thumbnail_width":200,"thumbnail_height":200,"thumbnail_url":"https://open-images.acast.com/shows/670d2bd3883ece12585b0859/1728989006703-6efc70b9-b124-4fec-b69e-c1ab5d24a416.jpeg?height=200","description":"<p><strong>BigQuery Iceberg Tables</strong>&nbsp;are an open-format data lakehouse solution on Google Cloud. These tables combine the flexibility of Cloud Storage with BigQuery's managed analytics, allowing users to work with data in Parquet format using the open-source Apache Iceberg table format. Key features of BigQuery Iceberg Tables include schema evolution, unified batch and streaming data handling, automatic storage optimization, and enhanced security features. The article explains how to create and use Iceberg tables in BigQuery, including data ingestion methods and a real-world case study of an e-commerce company using BigQuery Iceberg Tables to improve their data management and analytics capabilities.</p>","author_name":"RAVISH GARG"}