Hydrograph vs. Oracle Data Integration Platform Cloud (DIPC)

hydrograph is very usefull when we need to analyze big data. in our scenario it helped a lot with rdms databases

Incentivized

For daily transactional information and customer data needs it has been an extremely fast and reliable system. Several thousand users are able to connect many third party tools and run a large number of extracts while still experiencing great performance. For smaller systems in use with limited user accounts this system is less appropriate due to maintenance resources required.

Incentivized

• Coupling between complex model and MapReduce framework without reducer procedure was simplified.
• The ability to reduce execution time and handle partial failure
• The framework adapts to higherned complex model

Incentivized

• ODI uses ELT architecture, which enhances the load performance and is very scalable.
• ODI is platform independent. It supports all platforms, hardware and operating systems.
• It comes with pre-built integration, allowing immediate data replication, transformation, and data governance while ensuring data quality.

Incentivized

• Microsoft azure is recently joined in 2020 that can be improved.

Incentivized

• Training and ramping up to learn all the Oracle specific intricacies.
• Security concerns with administering some of the tools to end users.

Incentivized

Snap logic fits good for small/medium whereas hydrograph suits even for Enterprise grade.

Incentivized

We are longtime ODI on-premise users. So, it was easy for us to migrate to the ODI cloud rather than implementing another solution.

Incentivized

• Their is no tangible ROI for us Management of bid data is easy.

Incentivized

• Ability to setup reliable disaster recovery.
• Upgrades and performance monitoring tools are readily available.

Incentivized