2021 has been a year punctuated with new realities. As enterprises now interact mainly online, data and analytics teams need to better understand their data by collaborating across organizational boundaries. Industry research shows 90% of organizations have a multicloud strategy which adds complexity to data integration, orchestration and governance. While building and running enterprise solutions in the cloud, our customers constantly manage analytics across cloud providers. These providers unintentionally create data silos that cause friction for data analysts. This month we announced the availability of BigQuery Omni, a multicloud analytics service that lets data teams break down data silos by using BigQuery to securely and cost effectively analyze data across clouds.
For the first time, customers will be able to perform cross-cloud analytics from a single pane of glass, across Google Cloud, Amazon Web Services (AWS) and Microsoft Azure. BigQuery Omni will be available to all customers on AWS and for select customers on Microsoft Azure during Q4. BigQuery Omni enables secure connections to your S3 data in AWS or Azure Blob Storage data in Azure. Data analysts can query that data directly through the familiar BigQuery user interface, bringing the power of BigQuery to where the data resides.
Here are a few ways BigQuery Omni addresses the new reality customers face with multi cloud environments:
Multicloud is here to stay: Enterprises are not consolidating, they are expanding and proliferating their data stack across clouds. For financial, strategic, and policy reasons customers need data residing in multiple clouds. Data platforms support for multicloud has become table stakes functionality.
Multicloud data platforms provide value across clouds: Almost unanimously, our preview customers echoed that the key to providing game-changing analytics was through providing more functionality and integration across clouds. For instance, customers wanted to join player and ad engagement data to better understand campaign effectiveness. They wanted to join online purchases data with in-store checkouts to understand how to optimize the supply chain. Other scenarios included joining inventory and ad analytics data to drive marketing campaigns, and service and subscription data to understand enterprise efficiency. Data analysts require the ability to join data across clouds, simply and cost-effectively.
Multicloud should work seamlessly: Providing a single-pane-of-glass over all data stores empowers a data analyst to extend their ability to drive business impact without learning new skills and shouldn’t need to worry about where the data is stored. Because BigQuery Omni is built using the same APIs as BigQuery, where data is stored (AWS, Azure, or Google Cloud) becomes an implementation detail.
Consistent security patterns are crucial for enterprises to scale: As more data assets are created, providing the correct level of access can be challenging. Security teams need control over all data access with as much granularity as possible to ensure trust and data synchronization.
Data quality unlocks innovation: Building a full cross-cloud stack is only valuable if the end user has the right data they need to make a decision. Multiple copies, inconsistent, or out-of-date data all drive poor decisions for analysts. In addition, not every organization has the resources to build and maintain expensive pipelines.
BigQuery customer Johnson & Johnson was an early adopter of BigQuery Omni on AWS; “We found that BigQuery Omni was significantly faster than other similar applications. We could write back the query results to other cloud storages easily and multi-user and parallel queries had no performance issues in Omni. How we see Omni is that it can be a single pane of glass using which we can connect to various clouds and access the data using, SQL like queries,” said Nitin Doeger, Data Engineering and Enablement manager at Johnson and Johnson.
Another early adopter from the media and entertainment industry had data hosted in multiple cloud environments. Using BigQuery Omni they built cross cloud analytics to correlate advertising with in game purchases. Needing to optimize campaign spend and improve targeted ad personalization while lowering the cost per click for ads, their challenge was that campaign data was siloed across cloud environments with AWS, Microsoft Azure, and Google Cloud. In addition to this the data wasn’t synchronized across all environments and moving data introduced complexity, risk and cost. Using BigQuery they were able to analyze CRM data in S3 while keeping the data synchronized. This resulted in a marketing attribution solution to optimize campaign spend and ultimately helped improve campaign efficiency while reducing cost and improving data accessibility across teams.
In 2022, new capabilities will include cross cloud transfer’ and authorized external tables to help data analysts drive governed, cross-cloud scenarios and workflows all from the BigQuery interface. Cross cloud transfer helps move the data you need to finish your analysis in Google Cloud and find insights leveraging unique capabilities of BigQuery ML, Looker and Dataflow. Authorized external tables will provide consistent and fine grained governance with row-level and column-level security for your data. Together these capabilities will unlock simplified and secure access across clouds for all your analytics needs. Below is a quick demo of those features relevant to multicloud data analysts and scientists.
To get started with BigQuery Omni, simply create a connection to your data stores, and start running queries against your existing data, wherever it resides. Watch the multicloud session at Next 21 for more details.
BigQuery Omni makes cross cloud analytics possible! We are excited with what the future holds and look forward to hearing about your cross cloud data analytics scenarios. Share your questions with us on the Google Cloud Community, we look forward to hearing from you.
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