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At this year’s Google Cloud Next conference, Google has treated enterprises with some notable product developments, starting with the launch of new AI agents and software delivery shields to new cloud regions.
On the data side, the company’s focus has largely been on creating an open, extensible data cloud, one that could allow enterprises to access and work with all kinds of data, no matter its storage format or environment, in a trusted and governed manner. To this end, it unveiled multiple exciting capabilities for its data cloud.
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Below is a rundown of everything.
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Unstructured data support on BigQuery
Firstly, Google said it is bringing support for unstructured data in BigQuery, allowing enterprises to work with and query more types of data, like video from television archives, audio from call centers and documents, in the popular cloud data warehouse. The move will enable enterprises to cover most of their information sources, going beyond the days of only being able to analyze structured data from operational databases and software-as-a-service (SaaS) applications or semi-structured information like JSON log files.
Support for major data formats
Next, Google announced that its storage engine, BigLake, will support popular open-source table formats such as Apache Iceberg, Delta Lake and Apache Hudi. The move will help organizations derive full value from their data, although, as of now, only Apache Iceberg has launched in preview. The other two will follow suit in the coming weeks.
New Apache Spark integration
Along with support for new open-source table formats, Google Cloud Next also saw the launch of a new integrated experience in BigQuery for Apache Spark, an open-source data analytics engine. With this integration, Google said, data practitioners will be able to create procedures in BigQuery, using Apache Spark, that integrate with their SQL pipelines.
Google also announced the rollout of a new Datastream integration that will enable organizations to replicate data from all kinds of sources, including real-time data in AlloyDB, PostgreSQL, MySQL and third-party databases like Oracle, directly into BigQuery. This will give teams more data to quickly analyze and gain insights from.
To help organizations maintain high-quality datasets, Google also announced improvements to Dataplex. As part of this, the intelligent data fabric solution will begin to automate common processes associated with data quality. For instance, users can now more easily understand data lineage — where data originates and how it has transformed and moved over time — reducing the need for manual, time-consuming processes.
Vertex AI Vision
Extending the capabilities of Vertex AI, which enables model orchestration and deployment, Google announced Vertex AI Vision. This end-to-end application development environment enables data practitioners to ingest, analyze and store visual data for wide-ranging computer vision applications. It provides an easy-to-use, drag-and-drop interface and a library of pre-trained ML models for common tasks such as occupancy counting, product recognition and object detection, and can reduce the time to create computer vision apps from weeks to hours at one-tenth the cost of current offerings.
Finally, the company also used the Cloud Next stage to rebrand Google Data Studio, which enables self-service analytics, as Looker Studio and offer the solution with Looker under a common umbrella to create a deep integration of Looker, Data Studio and core technologies like AI and ML. The unified offering will serve as a complete business intelligence suite, helping enterprises go beyond dashboards and infuse their workflows and applications with the intelligence needed to help make data-driven decisions. As part of this shift, Looker Studio will also evolve to include a complete user interface for working with data modeled in Looker.