#dataagents

A big thank you to Amit Chandak for delivering a fantastic session on Data Agents and Copilot in Microsoft Fabric for our Azerbaijan Data & AI Community Meetup Group!

It was an insightful session where attendees explored how Microsoft Fabric and Microsoft Copilot can accelerate report creation, simplify analytics workflows, generate DAX, and enable natural language interaction with data through Data Agents.

🎥 Watch the recording here: https://www.youtube.com/live/i6lIUtGIbvQ?si=2MLPlfbbJ7qmhmyf

To stay connected and join our future meetups on Data, AI, Power BI, Microsoft Fabric, Excel, and Copilot, subscribe to our meetup group: https://www.meetup.com/azdata-aicommunity/

Data accessibility can hinder AI adoption since so much data is unstructured and unmanaged. Data should be accessible, actionable, and revolutionary for businesses. A data cloud based on open standards, that connects data to AI in real-time, and conversational data agents that stretch the limits of conventional AI are available today to help you do this.

An open real-time data ecosystem

Google Cloud announced intentions to combine BigQuery into a single data and AI use case platform earlier this year, including all data formats, numerous engines, governance, ML, and business intelligence. It also announces a managed Apache Iceberg experience for open-format customers. It adds document, audio, image, and video data processing to simplify multimodal data preparation.

Volkswagen bases AI models on car owner’s manuals, customer FAQs, help center articles, and official Volkswagen YouTube videos using BigQuery.

New managed services for Flink and Kafka enable customers to ingest, set up, tune, scale, monitor, and upgrade real-time applications. Data engineers can construct and execute data pipelines manually, via API, or on a schedule using BigQuery workflow previews.

Customers may now activate insights in real time using BigQuery continuous queries, another major addition. In the past, “real-time” meant examining minutes or hours old data. However, data ingestion and analysis are changing rapidly. Data, consumer engagement, decision-making, and AI-driven automation have substantially lowered the acceptable latency for decision-making. The demand for insights to activation must be smooth and take seconds, not minutes or hours. It has added real-time data sharing to the Analytics Hub data marketplace in preview.

Google Cloud launches BigQuery pipe syntax to enable customers manage, analyze, and gain value from log data. Data teams can simplify data conversions with SQL intended for semi-structured log data.

Connect all data to AI

BigQuery clients may produce and search embeddings at scale for semantic nearest-neighbor search, entity resolution, semantic search, similarity detection, RAG, and recommendations. Vertex AI integration makes integrating text, photos, video, multimodal data, and structured data easy. BigQuery integration with LangChain simplifies data pre-processing, embedding creation and storage, and vector search, now generally available.

It previews ScaNN searches for large queries to improve vector search. Google Search and YouTube use this technology. The ScaNN index supports over one billion vectors and provides top-notch query performance, enabling high-scale workloads for every enterprise.

It is also simplifying Python API data processing with BigQuery DataFrames. Synthetic data can replace ML model training and system testing. It teams with Gretel AI to generate synthetic data in BigQuery to expedite AI experiments. This data will closely resemble your actual data but won’t contain critical information.

Finer governance and data integration

Tens of thousands of companies fuel their data clouds with BigQuery and AI. However, in the data-driven AI era, enterprises must manage more data kinds and more tasks.

BigQuery’s serverless design helps Box process hundreds of thousands of events per second and manage petabyte-scale storage for billions of files and millions of users. Finer access control in BigQuery helps them locate, classify, and secure sensitive data fields.

Data management and governance become important with greater data-access and AI use cases. It unveils BigQuery’s unified catalog, which automatically harvests, ingests, and indexes information from data sources, AI models, and BI assets to help you discover your data and AI assets. BigQuery catalog semantic search in preview lets you find and query all those data assets, regardless of kind or location. Users may now ask natural language questions and BigQuery understands their purpose to retrieve the most relevant results and make it easier to locate what they need.

It enables more third-party data sources for your use cases and workflows. Equifax recently expanded its cooperation with Google Cloud to securely offer anonymized, differentiated loan, credit, and commercial marketing data using BigQuery.

Equifax believes more data leads to smarter decisions. By providing distinctive data on Google Cloud, it enables its clients to make predictive and informed decisions faster and more agilely by meeting them on their preferred channel.

Its new BigQuery metastore makes data available to many execution engines. Multiple engines can execute on a single copy of data across structured and unstructured object tables next month in preview, offering a unified view for policy, performance, and workload orchestration.

Looker lets you use BigQuery’s new governance capabilities for BI. You can leverage catalog metadata from Looker instances to collect Looker dashboards, exploration, and dimensions without setting up, maintaining, or operating your own connector.

Finally, BigQuery has catastrophe recovery for business continuity. This provides failover and redundant compute resources with a SLA for business-critical workloads. Besides your data, it enables BigQuery analytics workload failover.

Gemini conversational data agents

Global organizations demand LLM-powered data agents to conduct internal and customer-facing tasks, drive data access, deliver unique insights, and motivate action. It is developing new conversational APIs to enable developers to create data agents for self-service data access and monetize their data to differentiate their offerings.

Conversational analytics

It used these APIs to create Looker’s Gemini conversational analytics experience. Combine with Looker’s enterprise-scale semantic layer business logic models. You can root AI with a single source of truth and uniform metrics across the enterprise. You may then use natural language to explore your data like Google Search.

LookML semantic data models let you build regulated metrics and semantic relationships between data models for your data agents. LookML models don’t only describe your data; you can query them to obtain it.

Data agents run on a dynamic data knowledge graph. BigQuery powers the dynamic knowledge graph, which connects data, actions, and relationships using usage patterns, metadata, historical trends, and more.

Last but not least, Gemini in BigQuery is now broadly accessible, assisting data teams with data migration, preparation, code assist, and insights. Your business and analyst teams can now talk with your data and get insights in seconds, fostering a data-driven culture. Ready-to-run queries and AI-assisted data preparation in BigQuery Studio allow natural language pipeline building and decrease guesswork.

Connect all your data to AI by migrating it to BigQuery with the data migration application. This product roadmap webcast covers BigQuery platform updates.