Data analytics and business intelligence (BI) are key for driving informed decisions, and today's best solutions make use of artificial intelligence (AI) to help users along the way. AI chatbots, powered by natural language and machine learning (ML), are one such intelligent tool that can quickly analyze large datasets, helping your business users find helpful answers to queries, get recommendations, and even uncover valuable insights.

Large Language Models (LLMs) will be at the core of many groundbreaking AI solutions for enterprise organizations. Here are just a few examples of the benefits of using LLMs in the enterprise for both internal and external use cases: Optimize Costs. LLMs deployed as customer-facing chatbots can respond to frequently asked questions and simple queries.

In the modern digital landscape, data grows at an astonishing rate. Mid-sized enterprises struggle to manage its volume and velocity, let alone its value. Today’s complex data environments must span hybrid and multicloud setups. And as transformative technologies like generative AI (GenAI) matter more to business operations, they must manage skyrocketing demand for sophisticated, flexible and scalable data solutions.

Learn how BigQuery and Document AI Layout Parser can help with document preprocessing when building retrieval-augmented generation (RAG) pipelines.

Learn about the challenges of natural language to SQL (NL2SQL), and how to implement it on Google Cloud with BigQuery and Gemini.

The way we interact with computers and digital systems has come a long way. Gone are the days of cryptic commands and rigid syntax; today, we can speak naturally to our devices, ask questions in complete sentences, and even have (somewhat) intelligent conversations with chatbots. This transformation is largely thanks to a fascinating field of artificial intelligence (AI) called natural language processing (NLP).

In the world of big data, configuration management is often the unsung hero of platform performance and cost-efficiency. Whether you’re working with Snowflake, Databricks, BigQuery, or any other modern data platform, effective configuration management can mean the difference between a sluggish, expensive system and a finely-tuned, cost-effective one.

Effective cost management becomes crucial as organizations increasingly rely on Google BigQuery for their data warehousing and analytics needs. This checklist delves into the intricacies of cost management and FinOps for BigQuery, exploring strategies to inform, govern, and optimize usage while taking a holistic approach that considers queries, datasets, infrastructure, and more.

In this tutorial, learn how to use Gretel to generate synthetic data with BigQuery DataFrames, a Python client for BigQuery.