My Organization Already Has Oracle Essbase or Cloud EPM. Why Would I Consider Casabase Cube?

If your organization already has Oracle Essbase or Oracle Cloud EPM, you may be asking an obvious question:

Why would we need another multidimensional analytics product?

It’s a fair question.

After all, Essbase has been solving complex multidimensional problems for decades. Organizations have built critical financial reporting, planning, forecasting, profitability, and operational applications around it.

The problem isn’t multidimensional modeling.

The problem is everything we’ve had to build and maintain around it.

Separate analytical infrastructure. Data extracts and integrations. Specialized administration. Proprietary interfaces and query languages. Processing and aggregation cycles. Moving data back and forth between systems. And, increasingly, the challenge of getting that data into the modern analytics ecosystem where the rest of the organization wants to use it.

Essbase was designed for a different era of enterprise computing. So was Microsoft Analysis Services.

They solved a difficult problem extremely well: how do you deliver fast, sophisticated multidimensional analytics when the underlying relational infrastructure isn’t capable of doing it efficiently?

But the underlying infrastructure has changed.

Cloud data platforms such as Snowflake have fundamentally changed what is possible.

So perhaps the better question isn’t:

“Why would I replace Essbase?”

It’s:

“If all of my enterprise data is moving to Snowflake, why do I still need a separate OLAP platform?”

That’s the question Casabase Cube was built to answer.

Two Ways Casabase Cube Can Help

For organizations already invested in Oracle Essbase or Oracle Cloud EPM, there are two primary ways to use Casabase Cube.

The first is relatively non-disruptive: keep your existing Oracle applications, but use Casabase Cube to make their data dramatically easier to access, integrate, report on, and analyze.

The second goes further: migrate the multidimensional model itself to Casabase Cube and eliminate the need for the separate OLAP platform.

You don’t have to start with the second. In fact, the first use case may be the easiest way to discover why the second is possible.

1. Unlock Your Essbase and Cloud EPM Data

Anyone who has spent significant time working with Essbase or Cloud EPM knows that calculating data is only part of the problem.

Getting the data out and making it useful somewhere else can be just as challenging.

You may have a perfectly good Essbase application containing years of business logic, but then someone wants the data in Power BI. Or Tableau. Or Alteryx. Or another enterprise application. Or a data science environment. Or an AI application.

Now you have an integration project.

This is where Casabase Cube changes the equation.

Migrate the Essbase or Cloud EPM cube to Casabase Cube and the detailed level-0 data becomes relational data in Snowflake.

Dimensions, hierarchies, members, aliases, formulas, and other multidimensional structures are represented within the same Snowflake environment. Casabase Cube’s migration process uses the source outline and native level-0 data export to reconstruct supported multidimensional structures and load the underlying detailed data.

Suddenly, your multidimensional data isn’t trapped behind a specialized OLAP interface.

It’s in Snowflake.

Want the detailed data? Use SQL.

Level-0 data can be accessed relationally using standard SQL.

That makes it available to the enormous ecosystem of technologies that can already work with Snowflake, rather than requiring every downstream consumer to understand how to communicate with a proprietary multidimensional database.

Want the aggregated or calculated data? Query the cube.

Casabase Cube preserves the multidimensional layer on top of that relational data.

That means applications can retrieve dynamically aggregated and calculated results while Casabase Cube handles the multidimensional logic.

You’re not forced to choose between relational accessibility and multidimensional intelligence.

You get both.

And because Casabase Cube runs as a Snowflake Native Application, you aren’t solving the integration problem by introducing yet another external data platform.

The multidimensional engine runs where the data already lives: inside Snowflake.

From Essbase to Snowflake Without Rebuilding Everything

This is where things get particularly interesting for existing Oracle customers.

Moving away from a legacy analytical platform has historically implied a large migration project.

Rebuild the dimensions. Recreate the hierarchies. Translate the business logic. Reload the data. Reconcile the results. Then hope you haven’t lost years of knowledge embedded in the original application.

Casabase Cube was specifically designed to make migration from Oracle Essbase and Oracle Cloud EPM much simpler.

A typical migration starts with artifacts you already have: the Essbase outline and a native level-0 data export. Casabase Cube uses them to recreate supported dimensions, hierarchies, members, parent-child relationships, aliases, aggregation operators, shared members, member formulas, and relevant properties, and then loads the detailed data into Snowflake.

The migration can be performed interactively or automated using SQL stored procedures for repeatable production workflows.

See how an Oracle Essbase or Cloud EPM migration works

And that leads to the second use case.

2. What If You Didn’t Need Essbase at All?

Using Casabase Cube to make Oracle data accessible is valuable.

But once the cube is running in Snowflake, another question becomes difficult to ignore:

What is the separate OLAP platform still doing for you?

This is where Casabase Cube becomes more than an integration solution.

It can become the multidimensional calculation and aggregation engine itself.

Casabase Cube supports the concepts that make multidimensional models valuable in the first place: multiple dimensions, multiple hierarchies, ragged and balanced structures, dynamic member formulas, calculated members, shared members, solve order, Dynamic Time Series, time-balance calculations, and query-time multidimensional aggregation.

The business doesn’t have to give up multidimensional modeling.

You can give up the infrastructure that used to be required to provide it.

The Cube Wasn’t the Problem. The Architecture Was.

This distinction matters.

It would be easy to look at the rise of cloud data platforms and conclude that multidimensional databases are simply an old technology that should be replaced with relational tables.

We don’t believe that.

Dimensions, hierarchies, calculations, alternate rollups, time intelligence, and multidimensional business models remain incredibly useful ways to represent complex businesses.

Oracle’s own Essbase documentation continues to describe multidimensional analysis across finance, forecasting, profitability, operations, manufacturing, and other enterprise use cases. Microsoft likewise continues to document multidimensional Analysis Services models built around cubes, dimensions, hierarchies, query engines, calculations, and OLAP storage.

The multidimensional model survived for a reason.

What deserves another look is the architecture underneath it.

Historically, organizations needed a specialized OLAP database because conventional databases couldn’t efficiently perform these calculations at interactive speeds.

So you built separate OLAP servers. You copied data into them. You processed cubes. You built aggregations. You learned specialized query languages. You created integrations to get the results back out again. You accepted the operational complexity because there wasn’t a practical alternative.

There is now.

What About Microsoft Analysis Services?

The same argument isn’t limited to Oracle.

Microsoft Analysis Services was also designed around a specialized analytical engine. Microsoft’s current documentation describes a common multidimensional workflow that includes installing an Analysis Services instance, creating a model, deploying it to a server, processing the database to load data, and assigning permissions. Multidimensional models use cube structures, dimensions, OLAP query and calculation engines, and storage modes such as MOLAP, ROLAP, and HOLAP.

That architecture has delivered tremendous value.

But if Snowflake has become the center of your enterprise data architecture, it’s reasonable to ask whether another analytical server needs to sit beside it.

Casabase Cube takes a different approach:

Bring the multidimensional engine to Snowflake rather than moving Snowflake data to a multidimensional engine.

Stop Moving Data to the Cube. Bring the Cube to the Data.

That’s really the architectural idea behind Casabase Cube.

Your detailed data can remain relational and accessible. Your multidimensional model can remain multidimensional. Your calculations can remain sophisticated. Your hierarchies can remain hierarchical.

But the specialized infrastructure in the middle can disappear.

No separate OLAP server simply because you need a cube. No additional analytical database simply because your users need hierarchical aggregations. No proprietary island of enterprise data that has to be integrated back into the rest of your data architecture.

The cube becomes another capability of Snowflake.

And that changes much more than where the software runs.

You Don’t Have to Rip and Replace

There’s another important point for existing Oracle customers.

This doesn’t need to begin as a rip-and-replace project. Start with an existing Essbase or Cloud EPM application. Migrate it to Casabase Cube. Put the detailed data in Snowflake. Expose that data to Power BI, Tableau, Alteryx, Python, AI applications, or whatever else your organization needs.

Let existing Oracle processes continue running while you validate the Casabase Cube model alongside them.

Compare the results. Compare the performance. Compare the integration effort. Compare the operational overhead. Then decide what still needs to live in the legacy environment.

That creates a very different modernization path from the traditional multimillion-dollar “replace the platform” project.

You can start by solving the data-access problem. You may end by discovering that you no longer need the old OLAP platform.

OLAP Isn’t Legacy. Legacy OLAP Architecture Is.

We don’t think organizations should abandon the multidimensional models they’ve spent decades building. Quite the opposite. Those models encode some of the most important business logic in the enterprise.

But preserving that business logic shouldn’t require preserving every architectural decision that came with the software that originally implemented it.

Essbase helped define enterprise multidimensional analytics. Analysis Services helped bring it to another generation of organizations. They demonstrated the value of the model.

Casabase Cube asks what that model should look like when Snowflake is the data platform underneath it.

No separate OLAP infrastructure.

No unnecessary duplication of the analytical data estate.

Standard SQL access to detailed data.

Dynamic multidimensional aggregation and calculations when you need them.

Direct integration with the modern Snowflake ecosystem.

And a migration path that lets you bring existing Oracle models with you rather than starting over.

The future of OLAP isn’t another OLAP server.

It’s an OLAP engine that lives where your data already does.

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