Building the most open data cloud ecosystem: Unifying data across multiple sources and platforms

Google is launching several new capabilities in its Data Cloud ecosystem, including the ability to analyze unstructured and streaming data in BigQuery, and support for major data formats including Apache Iceberg.

A new integrated experience in BigQuery for Apache Spark, expansion of Dataplex for automated data quality and data lineage, unification of its business intelligence portfolio under the Looker umbrella, a new service called Vertex AI Vision for accessible computer vision and image recognition AI, and expanded integrations with popular enterprise data platforms to prevent data lock-in.

Unifying data, across source systems, with major formats

Google is adding several new capabilities to its Data Cloud ecosystem to enable users to work with all types of data, no matter its storage format or location.

These capabilities include support for unstructured data in BigQuery, support for major data formats including Apache Iceberg, a new integrated experience in BigQuery for Apache Spark, and updates to Dataplex for automated data quality processes and data lineage.

The company is also launching Datastream for BigQuery to replicate data in real-time from various sources into BigQuery, enabling users to get more insights from their data in real time. These updates are designed to help customers remove limits from their data and avoid data lock-in across clouds.

Supporting all styles of analysis and empowering analysts with AI

Google is unifying its business intelligence tools Looker and Google Data Studio under the Looker umbrella and integrating them with core Google technologies like AI and machine learning (ML).

The company is also enhancing Looker’s integration with Microsoft Power BI and releasing Vertex AI Vision, a new end-to-end application development environment for computer vision that helps users ingest, analyze, and store visual data. Vertex AI Vision can reduce the time and cost of creating computer vision applications and includes a drag-and-drop interface and a library of pre-trained ML models, as well as the option to import custom ML models.

Supporting an open data ecosystem

Google is partnering with major open data platforms, including Collibra, Elastic, MongoDB, Palantir, ServiceNow, Sisu Data, Reltio, and Striim, to give customers flexibility and prevent data lock-in.

Google is also supporting open-source database engines such as MongoDB, MySQL, PostgreSQL, and Redis, as well as Google Cloud databases like AlloyDB, Cloud Bigtable, Firestore, and Cloud Spanner.

These integrations aim to help customers move data between platforms, bring Google’s data cloud capabilities to partner platforms, and improve the customer experience.

Read more about the Open Data cloud ecosystem: Unifying data across multiple sources and platforms

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