Real-Time Analytics in the Cloud
Synchronized consistent database clones without copy sprawl
Today’s Real-Time Analytics Challenge
Digital businesses are rapidly adopting real time analytics platforms in the cloud to automate decisions for fraud detection, create digital customer experiences, and improve global collaboration. Competitive advantage depends on integrating fast moving unstructured data with regulated data from on-premise system of record. Griddable.io synchronizes filtered and transformed enterprise data into analytics platforms to complement high velocity data from streaming systems.
of enterprises rely on hybrid cloud or public cloud for big data analytics
(EMA, State of Cloud Analytics)
Real-time analytics is growing 3x faster than other analytics
of all data storage is a copy of some kind, creating a 50 billion dollar problem
Real-time analytics can allow data science teams to perform modelling, simulations and optimizations based on a complete set of transaction data and not just samples. End users can harness increasingly sophisticated analytic capabilities through packaged real-time analytics without prohibitive processing wait times or the need for developers to intervene.
Roy Schulte, VP & Distinguished Analyst, Gartner
Synchronized Consistent Clones of Regulated Data
Griddable.io’s smart grid guarantees continuous delivery of synchronized consistent clones of transactional data sources into analytics platforms. Policies flexibly define data filters and transformations at both the source and destination.
Eliminate lag times from copy sprawl
The transaction grid guarantees high performance and automated recovery of synchronized database clones over varying distances & flexible topologies. This eliminates the lag time typical of redundant copies in distributed data pipelines using ETL and storage snapshots. And it gives you the freedom to put real time analytics applications in any cloud or line of business innovation centers.
Optimizes network utilization with distributed policies
Policies define each database clone to include only relevant data, quickly boot strap the destination, and automatically bring the clone up to date or recover from failures. After that, only incremental changes at the source are continuously synchronized minimizing network utilzation into cloud platforms.
Flexibly transform and mask regulated data
Data masking policies can be applied at the source to mask private or secure data before it goes into the cloud. Apply grid policies at each target in the cloud to further filter or transform data to efficiently ingest into any relational or non-relational data platform.