![]() ![]() “ActionIQ is a composable CDP technology that helps enterprises tap into customer data to deliver personalized customer experiences. ![]() Other customers, like ActionIQ, see Cross-Cloud Transfer potential to complement differentiated offerings in their customer-centric platforms. We found the usability and performance useful during the POC,” said Dinesh Anchan, Manager of Engineering at Kargo. ![]() “We tested Cross-Cloud Transfer to assist with a proof of concept on BigQuery earlier this year. Kargo used cross-cloud transfer to accelerate a performance test of BigQuery. Getting started with a cross-cloud architecture can be daunting, but cross-cloud transfer has been used to help customers jumpstart proof of concepts because it enables the migration of subsets of data without committing to a full migration. Each job comes with statistics to help admins manage budgets.ĭuring our preview period, we saw good proof points on how cross-cloud transfer can be used to accelerate time to insight and deliver value to data teams. We’ve improved job latency significantly for the most common load jobs and are seeing performance improvements with each passing day.Ĭost auditability: From one invoice, you can see all your compute and transfer costs for LOADs across clouds. Latency: With data movement managed by BigQuery Write API, users can effortlessly move just the relevant data without having to wait for complex pipes. We also now support CMEK support for the destination table to help secure data as it’s written in BigQuery and VPC-SC boundaries to mitigate data exfiltration risks. Security: With a federated identity model, users don’t have to share or store credentials between cloud providers to access and copy their data. We’ve also added SQL support for data lake standards like Hive partitioning and JSON data type. With semantics for both appending and truncating tables, LOAD supports both periodic syncs and refreshing the complete table semantics. LOAD SQL supports data formats like JSON, CSV, AVRO, ORC and PARQUET. Usability: The LOAD SQL experience allows for data filtering and loading within the same editor across clouds. This feedback is why we’ve spent much of this year improving our cross-cloud transfer product to optimize releases around these core tenants: Allowing administrators to cap costs for budgeting is a must. Providing transparency into single operations and invoices in a consolidated way is critical to driving success for cross-cloud operations. Data transfer costs can get costly, and we hear frequently this is the number 1 concern for multi-cloud data organizations. By default, data admins and security teams are increasingly looking for solutions that don’t persist user credentials between cloud boundaries.Ĭost control comes with cost transparency. In order for data admins to empower data analysts and engineers, they need to be assured there isn’t additional risk in doing so. BigQuery users expect high performance for a single operation, even if those operations are managed across multiple data centers.ĭemocratizing data shouldn’t come at the cost of security. The longer an analyst needs to wait for an operation to complete, the less likely a complete workflow is to be completed end-to-end. Networking is an implementation detail, latency should be too. The more of the workflow that can be managed by SQL, the better. Same SQL can be used to periodically copy data using BigQuery scheduled queries. As soon as analysts are asked to leave their SQL workspaces to copy data, set up permissions, or grant permission, workflows break down and insights are lost. In order for analysts to work with distributed data, workspaces should not be siloed. A few learnings stand out:Ĭross-cloud operations need to meet analysts where they are. We’ve learned a lot in this preview period. In April 2022, we previewed a SQL supported LOAD statement that allowed AWS/Azure blob data to be brought into BigQuery as a managed table for advanced analysis. Cross-cloud analytics tools help analysts and data scientists easily, securely, and cost effectively distribute data between clouds to leverage the analytics tools they need. Now, we are excited to launch the next big evolution for multi cloud analytics: cross-cloud analytics. Organizations globally are using BigQuery Omni to analyze data across cloud environments. To help customers break down data silos, we launched BigQuery Omni in 2021. ![]()
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