Today at Google Cloud Next we are announcing innovations that will enable data teams to simplify how they work with data and derive value from it faster. These new solutions will help organizations build modern data architectures with real-time analytics to power innovative, mission-critical, data-driven applications.
Too often, even the best minds in data are constrained by ineffective systems and technologies. A recent study showed that only 32% of companies surveyed gained value from their data investments. Previous approaches have resulted in difficult to access, slow, unreliable, complex, and fragmented systems.
At Google Cloud, we are committed to changing this reality by helping customers simplify their approach to data to build their data clouds. Google Cloud’s data platform is simply unmatched for speed, scale, security, and reliability for any size organization with built-in, industry-leading machine learning (ML) and artificial intelligence (AI), and an open standards-based approach.
Vertex AI and data platform services unlock rapid ML modeling
With the launch of Vertex AI in May 2021, we empowered data scientists and engineers to build reliable, standardized AI pipelines that take advantage of the power of Google Cloud’s data pipelines. Today, we are taking this a step further with the launch of Vertex AI Workbench, a unified user experience to build and deploy ML models faster, accelerating time-to-value for data scientists and their organizations. We’ve integrated data engineering capabilities directly into the data science environment, which lets you ingest and analyze data, and deploy and manage ML models, all from a single interface.
Data scientists can now build and train models 5X faster on Vertex AI than on traditional notebooks. This is primarily enabled by integrations across data services (like Dataproc, BigQuery, Dataplex, and Looker), which significantly reduce context switching. The unified experience of Vertex AI let’s data scientists coordinate, transform, secure and monitor Machine Learning Operations (MLOps) from within a single interface, for their long-running, self-improving, and safely-managed AI services.
“As per IDC’s AI StrategiesView 2021, model development duration, scalable deployment, and model management are three of the top five challenges in scaling AI initiatives,” said Ritu Jyoti, Group Vice President, AI and Automation Research Practice at IDC. “Vertex AI Workbench provides a collaborative development environment for the entire ML workflow – connecting data services such as BigQuery and Spark on Google Cloud, to Vertex AI and MLOps services. As such, data scientists and engineers will be able to deploy and manage more models, more easily and quickly, from within one interface.”
Ecommerce company, Wayfair, has transformed its merchandising capabilities with data and AI services. “At Wayfair, data is at the center of our business. With more than 22 million products from more than 16,000 suppliers, the process of helping customers find the exact right item for their needs across our vast ecosystem presents exciting challenges,” said Matt Ferrari, Head of Ad Tech, Customer Intelligence, and Machine Learning; Engineering and Product at Wayfair. “From managing our online catalog and inventory, to building a strong logistics network, to making it easier to share product data with suppliers, we rely on services including BigQuery to ensure that we are able to access high-performance, low-maintenance data at scale. Vertex AI Workbench and Vertex AI Training accelerate our adoption of highly scalable model development and training capabilities.”
BigQuery Omni: Breaking data silos with cross-cloud analytics and governance
Businesses across a variety of industries are choosing Google Cloud to develop their data cloud strategies and better predict business outcomes — BigQuery is a key part of that solution portfolio. To address complex data management across hybrid and multicloud environments, this month we are announcing the general availability of BigQuery Omni, which allows customers to analyze data across Google Cloud, AWS, and Azure. Healthcare provider, Johnson and Johnson was able to combine data in Google Cloud and AWS S3 with BigQuery Omni without needing data to migrate.
This flexible, fully-managed, cross-cloud analytics solution allows you to cost-effectively and securely answer questions and share results from a single pane of glass across your datasets, wherever you are. In addition to these multicloud capabilities, Dataplex will be generally available this quarter to provide an intelligent data fabric that enables you to keep your data distributed while making it securely accessible to all your analytics tools.
Spark on Google Cloud simplifies data engineering
To help make data engineering even easier, we are announcing the general availability of Spark on Google Cloud, the world’s first autoscaling and serverless Spark service for the Google Cloud data platform. This allows data engineers, data scientists, and data analysts to use Spark from their preferred interfaces without data replication or custom integrations. Using this capability, developers can write applications and pipelines that autoscale without any manual infrastructure provisioning or tuning. This new service makes Spark a first class citizen on Google Cloud, and enables customers to get started in seconds and scale infinitely, regardless if you start in BigQuery, Dataproc, Dataplex, or Vertex AI.
Spanner meets PostgreSQL: global, relational scale with a popular interface
We’re continuing to make Cloud Spanner, our fully managed, globally scalable, relational database, available to more customers now with a PostgreSQL interface, now in preview. With this new PostgreSQL interface, enterprises can take advantage of Spanner’s unmatched global scale, 99.999% availability, and strong consistency using skills and tools from the popular PostgreSQL ecosystem.
This interface supports Spanner’s rich feature set that uses the most popular PostgreSQL data types and SQL features to reduce the barrier to entry for building transformational applications. Using the tools and skills they already have, developer teams gain flexibility and peace of mind because the schemas and queries they build against the PostgreSQL interface can be easily ported to another Postgres environment. Complete this form to request access to the preview.
Our commitment to the PostgreSQL ecosystem has been long standing. Customers choose Cloud SQL for the flexibility to run PostgreSQL, MySQL and SQL Server workloads. Cloud SQL provides a rich extension collection, configuration flags, and open ecosystem, without the hassle of database provisioning, storage capacity management, or other time-consuming tasks.
Auto Trader has migrated approximately 65% of their Oracle footprint to Cloud SQL, which remains a strategic priority for the company. Using Cloud SQL, BigQuery, and Looker to facilitate access to data for their users, and with Cloud SQL’s fully managed services, Auto Trader’s release cadence has improved by over 140% (year-over-year), enabling an impressive peak of 458 releases to production in a single day.
Looker integrations make augmented analytics a reality
We are announcing a new integration between Tableau and Looker that will allow customers to operationalize analytics and more effectively scale their deployments with trusted, real-time data, and less maintenance for developers and administrators. Tableau customers will soon be able to leverage Looker’s semantic model, enabling new levels of data governance while democratizing access to data. They will also be able to pair their enterprise semantic layer with Tableau’s leading analytics platform. The future might be uncertain, but together with our partners we can help you plan for it.
We remain committed to developing new ways to help organizations go beyond traditional business intelligence with Looker. In addition to innovating within Looker, we’re continuing to integrate within other parts of Google Cloud. Today, we are sharing new ways to help customers deliver trusted data experiences and leverage augmented analytics to take intelligent action.
First, we’re enabling you to democratize access to trusted data in tools where you are already familiar. Connected Sheets already allows you to interactively explore BigQuery data in a familiar spreadsheet interface and will soon be able to leverage the governed data and business metrics in Looker’s semantic model. It will be available in preview by the end of this year.
Another integration we’re announcing is Looker’s Solution for Contact Center AI, which helps you gain a deeper understanding and appreciation of your customers’ full journey by unlocking insights from all of your company’s first-party data, such as contextualizing support calls to make sure your most valuable customers receive the best service.
We’re also sharing the new Looker Block for Healthcare NLP API, which provides simplified access to intelligent insights from unstructured medical text. Compatible with Fast Healthcare Interoperability Resources (FHIR), healthcare providers, payers, and pharma companies can quickly understand the context and relationships of medical concepts within the text, and in turn, can begin to link this to other clinical data sources for additional AI and ML actions.
Bringing the best of Google together with Google Earth Engine and Google Cloud
We are thrilled to announce the preview of Google Earth Engine on Google Cloud. This launch makes Google Earth Engine’s 50+ petabyte catalog of satellite imagery and geospatial data sets available for planetary-scale analysis. Google Cloud customers will be able to integrate Earth Engine with BigQuery, Google Cloud’s ML technologies, and Google Maps Platform. This gives data teams a way to better understand how the world is changing and what actions they can take — from sustainable sourcing, to saving energy and materials costs, to understanding business risks, to serving new customer needs.
For over a decade, Earth Engine has supported the work of researchers and NGOs from around the world, and this new integration brings the best of Google and Google Cloud together to empower enterprises to create a sustainable future for our planet and for your business.
At Google Cloud, we are deeply grateful to work with companies of all sizes, and across industries, to build their data clouds. Join my keynote session to hear how organizations are leveraging the full power of data, from databases to analytics that support decision making to AI and ML that predict and automate the future. We’ll also highlight our latest product innovations for BigQuery, Spanner, Looker, and Vertex AI.
I can’t wait to hear how you will turn data into intelligence and look forward to connecting with you.
By: Gerrit Kazmaier (Vice President & General Manager, Databases, Analytics & Looker)
Source: Google Cloud Blog