Databricks Data Intelligence Platform vs. Oracle Fusion Cloud Enterprise Performance Management

Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.

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It is well suited in a geographically distributed environment where you have various key participants in multiple time zones providing input and necessary explanations on performance and measured success It is less appropriate where you have a single office environment with a single finance department working together. It is also less suited when the GL is NOT an Oracle product and the interface between the GL and the reporting software needs to be implemented with utmost care and handling.

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• Process raw data in One Lake (S3) env to relational tables and views
• Share notebooks with our business analysts so that they can use the queries and generate value out of the data
• Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
• Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers

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• Account Reconciliation CLoud Services provides tight integration with Oracle R12 and 11i . Provides drillback capability. It is flexible and provides rich functionality to users.
• We have implemented ARCS on cloud and migrated planning and budgeting from On premise to PBCS and EPBCS. Functionality is onpar with onpremise. Provides regular updates monthly
• We don't need to upgrades every year and saved a lot on Hardware.

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• Sometimes, when multiple jobs depend on each other in different environments, it is not always easy to see the full workflow in one place.
• It is sometimes difficult to determine which job or cluster contributes more to the overall cost.
• For beginners, cluster configuration may be a little difficult. So more recommendation in the platform can help.

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• The limitation of constrained out of the box dimensions that requires ripping the initial implementation to add the new dimensions is a major drawback.
• Smartview interface is cumbersome for many.
• The only financial adapter is available for integration for other modules we have to go with file-based uploads which is cumbersome and not real-time.
• It is very costly in terms of licensing unlike other Oracle products.

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We have a pretty good userbase who are at the high level who gained value from this tool. Discontinuing this tool is prone to impact their day to day operations. Also the data in this tool cannot alter the source, which maintains the integrity of this tool. Smartview is a cool addition that gained lot of traction as well.

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Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX

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It produces consolidated multi currency reports faster and more accurately whilst adding additional pivotal functionality. The Smartview reporting is a key for us and a flagstone of the application

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EPM has been so far available all the time

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The product performance is so far very good

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One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.

Support from our implementation partner is a 10/10, but support from Oracle is awful. Case in point: recently over a 2-day period, there were a series of intermittent outages. Oracle actually asked US for the outage time-stamps. I would have thought that, since we were paying them, they should probably be able to tell us!

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I suggest doubling the time to implement from the initial quote you get from the Sales team. That's always been the case for me for multiple EPMs now. The out-of-the-box functionality of Oracle Planning and Budgeting Cloud is pretty robust so I appreciate that and it really helped us get the ball rolling quickly.

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The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.

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Oracle Planning and Budgeting Cloud fared very well against Adaptive, Anaplan, and Host Analytics. We ultimately decided on Oracle due to its reputation and strength of existing customers. We are happy with our decision.

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The product is scalable

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• The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
• DB has the ability to terminate/time out instances which helps manage cost.
• The ability to quickly access typical hard to build data scenarios easily is a strength.

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• Significantly reduced the amount of money we spent for the maintenance of in-house hardware.
• We now need less employees to manage the configurations of Oracle EPM Cloud.
• There have been many time savings as a whole to apply to more productive causes.

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