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Salesforce has closed its acquisition of Griddable. The team is now a part of Salesforce! 

Accelerate cloud migration
and modernization

Policy-based SAAS platform migrates and modernizes data across heterogeneous databases and clouds

A smart grid for enterprise data is a resilient scale-out grid platform that synchronizes data across topologies, databases, and clouds. A policy engine controls flexible definition of data filtering, masking, and transformation in transit. The grid guarantees transactional data is always consistent and recoverable from failures.

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Multi-node topologies

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Many use cases require modernizing data to multiple targets simultaneously. Whether replacing a legacy Oracle database or re-architecting for microservices, Griddable migrates data to multiple different destinations at once, even on different clouds, while customizing data uniquely for each destination.

Filtering & transformation

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Griddable masks, renames, and replaces any number of data elements. It also filters data and selectively removes entire rows or columns with an easily-defined policy created with a text editor or the built-in graphical schema browser.

Fully-elastic infrastructure

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The Griddable data pipeline runs on a portable and elastic Kubernetes infrastructure. Kubernetes clusters automatically grow on demand when additional capacity is required, and the Kubernetes graphical UI provides complete visibility to the operational metrics of the cluster.

Hybrid cloud data integration technology has the potential to save you millions of dollars in operational costs, and 10 times that amount in strategic savings.

David Linthicum, Chief Cloud Strategy Office at Deloitte Consulting

Griddable improves the cloud

Hybrid cloud operations with data portability

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Migrate and re-factor databases for cloud-first architecture, or synchronize data with cloud innovation projects. Never be locked-in to a cloud platform, saving as much as 75% of your cloud costs.

Real time analytics in the cloud with consistent synchronized clones

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Select and mask data from systems of record to feed big data platforms while transforming data in transit. Safely automate actions for fraud detection or customer engagement while eliminating sprawl of dozens of redundant copies of regulated data.

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