With a few clicks, you can visualize data as a dashboard in Sheets and securely share it with anyone in your organization.
“Connected sheets are helping us democratize data,” says Nikunj Shanti, Chief Product Officer at AirAsia. “Analysts and business users are able to create pivots or charts, leveraging their existing skills on massive datasets, without needing SQL. This direct access to the underlying data in BigQuery provides access to the most granular data available for analysis. It’s a game changer for AirAsia.”
Connected sheets and BigQuery BI Engine are complemented by our broad range of updates to BigQuery. These include a new, updated BigQuery interface, now in GA, as well as the general availability of BigQuery GIS, enabling seamless analysis of spatial data in BigQuery, the only cloud data warehouse to support rich GIS functionalities out-of-the-box.
Predictive insights are increasingly becoming an important way businesses can anticipate needs like estimating customer demand or scheduling routine maintenance. Data warehouses often store the most valuable data sets for the enterprise, but unlocking these insights has traditionally been the domain of machine learning experts–a skill not shared by most data analysts or business users. We’ve changed that with BigQuery ML.
BigQuery ML generally available (coming soon), with expanded machine learning models
Last year, we announced BigQuery ML, enabling data analysts to build and deploy machine learning models on massive datasets directly inside BigQuery using familiar SQL.
We’re also continuing to expand BigQuery ML functionality to address even more business needs. We’ve made new models available like k-means clustering (in beta) and matrix factorization (in alpha) to build customer segmentations and product recommendations. Customers can also now also build and directly import TensorFlow Deep Neural Network models (in alpha) through BigQuery ML.
“Geotab is providing new smart city solutions leveraging aggregate data from over 1 million connected vehicles. We’re able to use BigQuery GIS to understand traffic flow patterns and BigQuery ML helped us derive insight into predicting hazardous driving areas in cities based on inclement weather,” explains Neil Cawse, CEO of Geotab.
AutoML Tables: apply machine learning to tabular data without writing a single line of code
Not everyone who can benefit from machine learning insights is a SQL expert. To make it even easier to apply ML on structured data stored in BigQuery and Cloud Storage, we’re excited to announce AutoML Tables, in beta. AutoML Tables lets your entire team of data scientists, analysts and developers automatically build and deploy state-of-the-art machine learning models on structured data in just a few clicks, reducing the total time required from weeks to days–without writing a single line of code.
The variety, volume and velocity of data from disparate systems, business processes, and other sources has meant that many organizations increasingly grapple with data access, discovery, management, security and governance. Finding and validating datasets can often be a complex, manual process, and increasing regulatory and compliance requirements has made it all the more important.
Data Catalog: data discovery and governance, simplified
To help organizations to quickly discover, manage and understand their data assets, we’re introducing Data Catalog in beta, a fully managed and scalable metadata management service. Data Catalog offers a simple and easy-to-use search interface for data discovery, powered by the same Google search technology that supports Gmail and Drive, and offers a flexible and powerful cataloging system for capturing technical and business metadata. For security and data governance, it integrates with Cloud DLP, so you can discover and catalog sensitive data assets, and Cloud IAM, where we honor source access control lists (ACLs), simplifying access management.
After deploying Data Catalog with his team, David Parfett, Director of Data Architecture at Sky explains, “With the increasing amount of data assets in our organization, we are confident that Data Catalog will allow us to quickly and easily discover our data assets across GCP and scale in line with our growing business.”
We’re also working with strategic partners like Collibra, Informatica, Tableau, and Looker to build integrations with Data Catalog, allowing customers to have a unified data discovery experience for hybrid cloud scenarios, using their platform of choice.
“Our relationship with Google Cloud has accelerated in recent months, and this partnership is the next step in our shared commitment to providing a foundation for data governance that sets organizations up to succeed,” said Jim Cushman, Chief Product Officer for Collibra. “We’re excited to continue building this partnership, with a mutual goal of integrating our technologies and making it easier for enterprise organizations to understand and use the data that is vital to their business.”
To learn more, and request access to Data Catalog, fill out this form.
From Fortune 500 enterprises to start-ups, more and more businesses continue to look to the cloud to help them store, manage, and generate insights from their data. And we’ll continue to develop new, transformative tools to help them do just that. For more information about data analytics on Google Cloud, visit our website.