Wednesday, May 29, 2024
No menu items!
HomeCloud ComputingHow Gemini in BigQuery accelerates data and analytics workflows with AI

How Gemini in BigQuery accelerates data and analytics workflows with AI

The journey of going from data to insights can be fragmented, complex and time consuming. Data teams spend time on repetitive and routine tasks such as ingesting structured and unstructured data, wrangling data in preparation for analysis, and optimizing and maintaining pipelines. Obviously, they’d rather prefer doing higher-value analysis and insights-led decision making. 

At Next ‘23, we introduced Duet AI in BigQuery. This year at Next ‘24, Duet AI in BigQuery becomes Gemini in BigQuery which provides AI-powered experiences for data preparation, analysis and engineering as well as intelligent recommendations to enhance user productivity and optimize costs.

“With the new AI-powered assistive features in BigQuery and ease of integrating with other Google Workspace products, our teams can extract valuable insights from data. The natural language-based experiences, low-code data preparation tools, and automatic code generation features streamline high-priority analytics workflows, enhancing the productivity of data practitioners and providing the space to focus on high impact initiatives. Moreover, users with varying skill sets, including our business users, can leverage more accessible data insights to effect beneficial changes, fostering an inclusive data-driven culture within our organization.” said Tim Velasquez, Head of Analytics, Veo 

Let’s take a closer look at the new features of Gemini in BigQuery.

Accelerate data preparation with AI

Your business insights are only as good as your data. When you work with large datasets that come from a variety of sources, there are often inconsistent formats, errors, and  missing data. As such, cleaning, transforming, and structuring them can be a major hurdle.

To simplify data preparation, validation, and enrichment, BigQuery now includes AI augmented data preparation that helps users to cleanse and wrangle their data. Additionally we are enabling users to build low-code visual data pipelines, or rebuild legacy pipelines in BigQuery. 

Once the pipelines are running in production, AI assists with finding and resolving issues such as schema or data drift, significantly reducing the toil associated with maintaining a data pipeline. Because the resulting pipelines run in BigQuery, users also benefit from integrated metadata management, automatic end-to-end data lineage, and capacity management.

Gemini in BigQuery provides AI-driven assistance for users to clean and wrangle data

Kickstart the data-to-insights journey

Most data analysis starts with exploration — finding the right dataset, understanding the data’s structure, identifying key patterns, and identifying the most valuable insights you want to extract. This step can be cumbersome and time-consuming, especially if you are working with a new dataset or if you are new to the team. 

To address this problem, Gemini in BigQuery provides new semantic search capabilities to help you pinpoint the most relevant tables for your tasks. Leveraging the metadata and profiling information of these tables from Dataplex, Gemini in BigQuery surfaces relevant, executable queries that you can run with just one click. You can learn more about BigQuery data insights here.

Gemini in BigQuery suggests executable queries for tables that you can run in single click

Reimagine analytics workflows with natural language

To boost user productivity, we’re also rethinking the end-to-end user experience. The new BigQuery data canvas provides a reimagined natural language-based experience for data exploration, curation, wrangling, analysis, and visualization, allowing you to explore and scaffold your data journeys in a graphical workflow that mirrors your mental model. 

For example, to analyze a recent marketing campaign, you can use simple natural language prompts to discover campaign data sources, integrate with existing customer data, derive insights, and share visual reports with executives — all within a single experience. Watch this video for a quick overview of BigQuery data canvas.

BigQuery data canvas allows you to explore and analyze datasets, and create a customized visualization, all using natural language prompts within the same interface

Enhance productivity with SQL and Python code assistance 

Even advanced users sometimes struggle to remember all the details of SQL or Python syntax, and navigating through numerous tables, columns, and relationships can be daunting. 

Gemini in BigQuery helps you write and edit SQL or Python code using simple natural language prompts, referencing relevant schemas and metadata. You can also leverage BigQuery’s in-console chat interface to explore tutorials, documentation and best practices for specific tasks using simple prompts such as: “How can I use BigQuery materialized views?” “How do I ingest JSON data?” and “How can I improve query performance?”

Optimize analytics for performance and speed 

With growing data volumes, analytics practitioners including data administrators, find it increasingly challenging to effectively manage capacity and enhance query performance. We are introducing recommendations that can help continuously improve query performance, minimize errors and optimize your platform costs. 

With these recommendations, you can identify materialized views that can be created or deleted based on your query patterns and partition or cluster of your tables. Additionally, you can autotune Spark pipelines and troubleshoot failures and performance issues. 

Get started

To learn more about Gemini in BigQuery, watch this short overview video and refer to the documentation , and sign up to get early access to the preview features. If you’re at Next ‘24, join our data and analytics breakout sessions and stop by at the demo stations to explore further and see these capabilities in action. Pricing details for Gemini in BigQuery will be shared when generally available to all customers.

Cloud BlogRead More

RELATED ARTICLES

LEAVE A REPLY

Please enter your comment!
Please enter your name here

Most Popular

Recent Comments