– Data teams spend time on repetitive tasks such as ingesting and wrangling data, wishing to focus on higher-value analysis
– Gemini in BigQuery provides AI-powered experiences for data preparation and analysis, enhancing user productivity and optimizing costs
– Features of Gemini in BigQuery include AI augmented data preparation, visual data pipelines, and AI assistance in maintaining data pipelines
The process of turning data into insights can be time-consuming and complex for data teams, who often spend a lot of time on repetitive tasks such as data ingestion, wrangling, and pipeline optimization. At Next ’23 and Next ’24, Google introduced Duet AI in BigQuery, which has now evolved into Gemini in BigQuery. Gemini provides AI-powered experiences for data preparation, analysis, and engineering, as well as intelligent recommendations to boost productivity and reduce costs.
With the new AI-powered features in Gemini in BigQuery, teams can extract valuable insights from data more efficiently. Natural language-based experiences, low-code data preparation tools, and automatic code generation streamline analytics workflows, allowing data practitioners to focus on high-impact projects. This also enables users with varying skill levels, including business users, to access data insights more easily, fostering a data-driven culture within organizations.
Gemini in BigQuery accelerates data preparation with AI, helping users cleanse and wrangle data from various sources with inconsistent formats and errors. Users can build low-code visual data pipelines or rebuild legacy pipelines in BigQuery, with AI assisting in identifying and resolving issues such as schema or data drift. This reduces the maintenance burden associated with data pipelines, while also providing integrated metadata management, data lineage, and capacity management within BigQuery.