83% Enterprise Workloads will be in the Cloud by 20201 59% Need data synchronization between cloud & on-premise2 77% Data Integration is Critical for Cloud2 Top 3 cloud concerns Data Security & Regulations Data Portability Cloud Lock-in3 1 – Logic Monitor Cloud Vision 2020 Future of Cloud Survey 2 – Oracle IOUG Data Integration Survey 3 – Bain & Company Changing Face of the Cloud CIO Survey
• Modify topologies during runtime without service interruption and add new sources or destinations dynamically • High-availability and scalability options are available to easily customize out-of-box with advanced features Choice of topologies Public Cloud On-Premise Public Cloud On-Premise Public Cloud On-Premise Public Cloud On-Premise Migration Analytics Innovation Operational Integration
for the use case • Automatically create initial database copy and then synchronize each subsequent transaction without manual intervention or user tracking of transaction numbers or logs • Dynamically add sources and destinations without service interruption Continuous synchronization between different database types Public Cloud On-Premise Aurora, Oracle, or MySQL
schema, table, row, or column level without impacting the source data • Enhance privacy by selectively encrypting or masking private data • Facilitate data re- engineering by selectively transforming data • Customize data at the source or destination according to the needs of the use case, such as reducing network traffic or preserving privacy Policy-based custom data synchronization
agents near the data Transaction Grid Resilient & secure scale-out architecture that guarantees transactional consistency Policy Engine Declarative language to configure topology & over-the-wire services Relational, Big Data, Cloud Services Pre-Built, SDK, message bus connectors Source DB is always source of truth with same commit timeline for all destinations 1:1, 1:N, N:1, and N:M topologies Selective data filters by row, column, schema Transform, mask/obfuscate, object mapping Griddable.io creates a smart grid for enterprise data
Policy Policy Fetcher Policy (limit data read) Publisher Policy (limit data stored) Consumer Request Policy (limit data served) Consumer Policy (configure application) Relay Fetcher Plugin Publisher Consumer Plugin Consumer Plugin Source Destination Pulls Changes Events From DB Logs with Minimal Overhead Each Consumer Pulls Data from Publisher Independently for Scale and HA Timeline Consistency & Neutral Format
and expressive to include everything needed to configure the grid • Optionally nested to enable the creation of hierarchical policies • Dynamically updated without service interruption • Define and tag groups of similar data, like personal data, and use tags in policy definitions Policy components Example Declarative policy language