Bigtable has been a core piece of application infrastructure for Google and companies such as Snap, Spotify, and many other massive platforms for over 20 years. In this session, we’ll discuss the fundamental changes to Bigtable processing capabilities made available via SQL that will let you bring more data transformations directly into Bigtable – enabling extract, load, and transform (ELT) capabilities taking advantage of Bigtable’s flexible schema to achieve increased data freshness – and that will reduce the time and costs of running other data processing services to prepare data for your real-time application.

Applications of the future require a database that transcends historical paradigms. They require advanced in-database capabilities like Graph RAG, vector and full-text search without compromising on critical database properties of compliance, scale, and availability. In this talk, you'll learn how Spanner's native search and interoperable multi-model capabilities enable your developers to build intelligent, global applications on a single, zero-ETL (extract, transform, and load) data platform.

NVIDIA GPUs accelerate batch ETL workloads at significant cost savings and performance. In this session, we will delve into optimizing Apache Spark on GCP Dataproc using the G2 accelerator-optimized series with L4 GPUs via RAPIDS Accelerator For Apache Spark, showcasing up to 14x speedups and 80% cost reductions for Spark applications. We will demonstrate this acceleration through a reference AI architecture on financial transaction fraud detection, and go through performance measurements.

Unstructured data makes up the majority of all new data; a trend that's been growing exponentially since 2018. At these volumes, vector embeddings require indexes to be trained so that nearest neighbors can be efficiently approximated, avoiding the need for exhaustive lookups. However, training these indexes puts intense demand on vector databases to maintain a high ingest throughput. In this session, we will explain how the NVIDIA cuVS library is turbo charging vector database ingest with GPUs, providing speedups from 5-20x and improving data readiness.

This Session is hosted by a Google Cloud Next Sponsor.
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This talk will demonstrate how the SAP user community can use Looker/Explore Assistant Chatbot to explore data insights into SAP ERP data stored on Google Cloud's BigQuery using natural language prompts. We will discuss the challenge of accessing and analyzing SAP data - ETL, Complex Data Model, introduction to Generative AI and Large Language Models (LLMs), and Looker Explore Assistant and Chatbot This presentation will illustrate how SAP users can leverage Looker and Explore Assistant Chatbot to gain insights into their SAP ERP data residing on Google Cloud's BigQuery, using natural language prompts. We will address common challenges in accessing and analyzing SAP data, such as ETL processes and complex data models. Additionally, we will provide an introduction to Generative AI and Large Language Models (LLMs), as well as an overview of Looker Explore Assistant and Chatbot's capabilities.

Join us to learn how you can build on Google’s intelligent, open, and unified Data Cloud to accelerate your AI transformation. This session covers deep integrations between BigQuery and Google’s operational databases, such as Spanner, AlloyDB, Bigtable, Cloud SQL. Mercado Libre will share how Spanner and Bigtable Data Boost enable near-zero impact analytics on their operational data. Plus, discover how Datastream and change streams simplify data movement to BigQuery, and how reverse ETL (extract, transform, and load) from BigQuery powers operational analytics.

Build robust ETL pipelines on Google Cloud! This hands-on lab teaches you to use Dataflow (Python) and BigQuery to ingest and transform public datasets. Learn design considerations and implementation details to create effective data pipelines for your needs.

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BigQuery is unifying data management, analytics, governance, and AI. Join this session to learn about the latest innovations in BigQuery to help you get actionable insights from your multimodal data and accelerate AI innovation with a secure data foundation and new-gen AI-powered experiences. Hear how Mattel utilized BigQuery to create a no-code, shareable template for data processing, analytics, and AI modeling, leveraging their existing data and streamlining the entire workflow from ETL to AI implementation within a single platform.

Redpanda, a leading Kafka API-compatible streaming platform, now supports storing topics in Apache Iceberg, seamlessly fusing low-latency streaming with data lakehouses using BigQuery and BigLake in GCP. Iceberg Topics eliminate complex & inefficient ETL between streams and tables, making real-time data instantly accessible for analysis in BigQuery This push-button integration eliminates the need for costly connectors or custom pipelines, enabling both simple and sophisticated SQL queries across streams and other datasets. By combining Redpanda and Iceberg, GCP customers gain a secure, scalable, and cost-effective solution that transforms their agility while reducing infrastructure and human capital costs.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.