Read about how BigQuery now allows you to use manifest files for querying open table formats.

The data landscape is constantly evolving, and with it come new challenges and opportunities for data teams. While generative AI and large language models (LLMs) seem to be all everyone is talking about, they are just the latest manifestation of a trend that has been evolving over the past several years: organizations tapping into petabyte-scale data volumes and running increasingly massive data pipelines to deliver ever more data analytics projects and AI/ML models.

Google recently introduced significant changes to its existing BigQuery pricing models, affecting both compute and storage. They announced the end of sale for flat-rate and flex slots for all BigQuery customers not currently in a contract. Google announced an increase to the price of on-demand analysis by 25% across all regions, starting on July 5, 2023.

Data is a powerful force that can generate business value with immense potential for businesses and organizations across industries. Leveraging data and analytics has become a critical factor for successful digital transformation that can accelerate revenue growth and AI innovation.

What sets successful organizations apart from others today? They extract value from their data, turning it into an asset that helps drive the business forward. To get to that point, an organization — of any size — needs real-time, consistent, connected, and trusted data to support critical business operations and insights. However, many stumbling blocks can cause organizations to fail when executing their data strategy.

Artificial intelligence (AI) has become a driving force in the digital transformation of businesses across various industries. As Chief Information Officers (CIOs) strive to stay ahead of the AI hype cycle in today’s competitive landscape, harnessing generative AI in particular can help them achieve their enterprise AI goals – by transforming processes, boosting productivity, and enhancing decision-making.