Conversational Analytics is transforming the way organizations interact with their data. Imagine simply chatting with your data, getting immediate, actionable insights. This is a game-changer for analysts, liberating them from the endless cycle of report creation and empowering business users with true self-service.
This approach goes beyond basic question-and-chart interactions. Advanced language models are leveraged to guide users through their data, offer summaries, and even surface automated insights – ensuring crucial information doesn’t slip through the cracks.
We are committed to integrating Conversational Analytics across the entire portfolio. Looker customers will be able to start a standalone chat client and for more flexibility transition to Looker Studio for fine-tuned dashboarding and reporting.
Simplicity with an AI-ready, unified data platform
BigQuery helps you get all your data ready for AI. At Google Cloud Next ’24 we announced that BigQuery would be the single integrated platform for your data-to-AI journey, designed to be multimodal, multi-engine, and multicloud.
To help, we’ve built a first-party connection from BigQuery to Vertex AI to provide direct access to AI models and fine-tune LLMs using your enterprise data, helping to ensure greater consistency and accuracy for your models. We also introduced new query capabilities using vector indexing directly in BigQuery to leverage AI with your data where it is stored. And recently, we added support for the latest Gemini models to BigQuery, as well as safety enhancements and grounding support.
“Switching to BigQuery has transformed our ability to access, understand, and use data at Veo. With its direct integration into other Google Cloud solutions, we get greater accessibility and faster insights, unlocking significant impact and enablement across our organization. Even non-technical team members feel comfortable using BigQuery to run ad-hoc analyses themselves, freeing up time for our analytics team to work on high-value projects.” – Max Schuman, Senior Data Scientist, Veo
Macquarie Bank is co-innovating with Google Cloud on the next wave of digital banking services for their customers in Australia. By unifying all of their data, they have made it easier to connect with the latest AI technologies — including gen AI — to enable scale and build new ways for customers to interact with its financial services.
More and more customers rely on BigQuery to manage unstructured data including images, audio, and video formats via object tables, usage of which has grown over 600% YoY. BigLake, the open storage engine for BigQuery, provides customers the ability to analyze open structured and multimodal data on one platform, along with a fully managed Apache Iceberg experience, to build a fully managed, streaming and AI-optimized open lakehouse. At Next ‘24 we announced foundational capabilities for BigLake to further enable an open multiformat and multimodal platform, including a single runtime metastore that interoperates across multiple engines and open-format table types such as Parquet and Apache Iceberg. In addition, we are adding support for more open formats like Delta Lake, which is now GA. Finally, workflow and scheduling enhancements that are now generally available in BigQuery provide data teams with more automation over their data pipelines.
“Deutsche Telekom built a horizontally scalable data platform in an innovative way that was designed to meet our current and future business needs. With BigQuery at the center of our enterprise’s One Data Ecosystem, we created a unified approach to maintain a single source of truth while fostering de-centralized usage of data across all of our data teams.” – Ashutosh Mishra, VP of Data Architecture, Deutsche Telekom
Data processing and streaming made easy
Apache Spark has become a popular data-processing runtime, especially for data engineering tasks. In fact, customers’ use of serverless Apache Spark in Google Cloud increased by over 500% in the past year. BigQuery’s newly integrated Spark engine lets you process data using PySpark as you do with SQL. Like the rest of BigQuery, the Spark engine is completely serverless — no need to manage compute infrastructure.
For many companies, running Apache Kafka meant managing many clusters across multiple cloud vendors and on-premises distributions. We’ve heard from many customers that they would like a simpler way to run an Apache Kafka cluster on Google Cloud. Today, you can turn one up in any of your projects with Google Cloud Managed Service for Apache Kafka directly in the Google Cloud console. This managed service helps automate Kafka operations and security while providing customers with the ability to run streaming analytics at scale and integrate them with user-facing operational systems.
Streaming data is also important across multiple industries that need to share real-time data with their partners and customers. For example, a retailer may want to share inventory levels in real-time with Consumer Packaged Goods (CPG) enterprises to provide real-time fulfillment visibility. To help organizations easily share and monetize their data from BigQuery in real-time, we are announcing the preview of Pub/Sub topics sharing in Analytics Hub. Pub/Sub is a globally used messaging service for reliable, large-scale data streaming. Analytics Hub, BigQuery’s data exchange platform, is used by thousands of companies to securely share hundreds of petabytes across organizational boundaries in a given week with zero-copying at scale. This new integration enables curated sharing of streaming data, centralized management of data access, and the discovery of valuable data from other organizations, all in real-time.
Accelerate your data journey to the cloud for AI readiness
Moving your data to the cloud is perhaps the number one way to prepare for the AI era. To help organizations accelerate time-to-value with data and AI, we are excited to introduce a data platform migration incentive program targeted at data warehouses and data lakes, plus enhanced BigQuery migration services tooling. Now, it’s easier than ever to migrate all types of data and workloads — from multimodal data to SQL, Spark, and Python — to Google Cloud. Get started on your Data Cloud journey today — take the data strategy assessment and connect with our team to accelerate your path to innovation using your enterprise data for AI.
