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Introducing BigQuery DataFrames 2.0 for the era of multimodal data science
- BigQuery DataFrames 2.0 was introduced to cater to the needs of data scientists and ML engineers by enabling the processing of multimodal data and AI within Python workflows.
- The future of data science involves handling insights from various unstructured data formats like images, audio, and text, alongside structured data.
- BigQuery DataFrames 2.0 extends Pandas DataFrames for processing multimodal data, making it easier for data scientists to work with large datasets and new AI techniques.
- The latest version strengthens the core foundation for Python data science, introducing features to unlock the full potential of both structured and unstructured data.
- Features in BigQuery DataFrames 2.0 include working with multimodal data and generative AI techniques, Pythonic operators for BigQuery AI Query Engine, and Gemini Code Assist for DataFrames.
- BigQuery DataFrames adoption has significantly grown, with over 100 PB processed monthly by thousands of customers, including Deutsche Telekom and Trivago.
- The release of BigQuery DataFrames 2.0 focuses on streamlining AI and machine learning pipelines, with features like an experimental Python UDF, dbt Integration, and core enhancements like partial ordering.
- The integration of structured and unstructured data in BigQuery eliminates data silos and enables unified data analysis, LLM-powered insights, simplified workflows, scalability, and governance.
- BigQuery DataFrames 2.0 offers a Pandas-like API for analyzing all data types in one place and leveraging LLMs for deeper insights, simplifying data pipelines, and ensuring scalability and governance.
- Overall, BigQuery DataFrames 2.0 is a significant advancement for data and AI work, empowering users to unleash the full potential of their data, irrespective of its format.
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