Trends in the data space such as generative AI, distributed storage systems, unstructured data formats, MLOps, and the sheer size of datasets are making it necessary to expand beyond the SQL language to truly analyze and understand your data.
To provide users with more flexibility of coding languages, we announced BigQuery DataFrames at Next ‘23. Currently in preview, this new open source library gives customers the productivity of Python while allowing the BigQuery engine to handle the core processing. Offloading the Python processing to the cloud enables large scale data analysis and provides seamless production deployments along the data to AI journey.
BigQuery DataFrames is a unified Python API on top of BigQuery’s managed storage and BigLake tables. It lets developers discover, describe, and understand BigQuery data by providing a Python compatible interface that can automatically scale to BigQuery sized datasets. BigQuery DataFrames also makes it easy to move into a full production application by automatically creating SQL objects like BigQuery ML inference models and Remote Functions.
This is all done from the new BigQuery DataFrames package which is unified with BigQuery’s user permission model, letting Python developers use their skills and knowledge directly inside BigQuery. A bigframes.DataFrame programming object can be handed off to the Vertex AI SDK and the BigQuery DataFrames Python package is integrated with Google Cloud notebook environments such as BigQuery Studio and Colab Enterprise, as well as partner solutions like Hex, and Deepnote. It can also be installed into any Python environment with a simple ‘pip install BigQuery DataFrames’ command.
Since the large-scale processing happens on the Google Cloud side, a small laptop is enough to get started. BigQuery DataFrames contains two APIs for working with BigQuery — bigframes.pandas and bigframes.ml. In this blog post, we will look at what can be done with these two APIs.
bigframes.pandas
Loosely based on the open source pandas API, the bigframes.pandas API is primarily designed for exploratory data analysis, advanced data manipulation, and data preparation.
The BigQuery DataFrames version of the pandas API provides programming abstractions such as DataFrames and Series that pandas users are familiar with. Additionally, it comes with some distinctions that makes it easier when working with large datasets. The core capabilities of bigframes.pandas today are:
Unified data Input/Output (IO): One of the primary challenges data scientists face is the fragmentation of data across various sources. BigQuery DataFrames addresses this challenge head-on with robust IO methods. Irrespective of whether the data is stored in local files, S3, GCS, or others, it can be seamlessly accessed and incorporated into BigQuery DataFrames. This interoperability not only facilitates ease of access but also effectively breaks down data silos, enabling cohesive data analysis by making disparate data sources interactable within a unified platform.
