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Is Essbase Making a Comeback? What You Need to Know.

Mon, 2026-08-10 11:22
At the recent Kscope26 conference, Oracle showed the next generation of Essbase in several different sessions. I came away from the conference more excited about the future of Essbase than I have been in a long time.

For several years, I have heard people suggest that Essbase is dead or that Oracle is no longer investing in it. Based on what Oracle showed at Kscope26, that does not appear to be the case.

Essbase remains very important under the covers of a number of Oracle applications. Oracle Cloud EPM is the most obvious example, but Essbase technology is also used throughout Oracle’s application portfolio. Many of the improvements Oracle has made to Essbase over the past several years have probably been driven by the requirements of those applications, even though standalone Essbase customers may not have seen much promotion or discussion of that work.

Now, Oracle appears to be preparing to make Essbase much more visible.

The most significant development is the increasingly close relationship between Essbase and Oracle Autonomous AI Lakehouse. When that relationship is combined with federated cubes, AI-assisted cube creation, natural-language queries, the Essbase MCP server, and Oracle’s growing multicloud strategy, I think it could represent a major new chapter for Essbase.

Let’s look at what Oracle is doing and why it matters.

Oracle Autonomous AI Lakehouse
Over the past year, Oracle has been positioning Autonomous AI Lakehouse as a foundation for enterprise data, analytics, and artificial intelligence.

This is not simply another place to store data.  It serves:
  • Relational data
  • Data lake storage based on Apache Iceberg
  • Vector data for retrieval-augmented generation
  • AI inference
  • SQL analytics
  • Machine learning
  • Governance and security
  • Integration across OCI, Microsoft Azure, AWS, and Google Cloud
The key idea is that AI and analytics should operate where the enterprise data already lives.

Instead of continually moving information into separate AI platforms, Oracle wants organizations to apply AI, analytics, machine learning, vector search, and natural-language access directly to governed enterprise data.

That can include relational information stored in Oracle, data stored in Apache Iceberg tables, and information managed across other cloud and lakehouse platforms, including Snowflake and Databricks.

This is important because companies do not want to create another uncontrolled copy of their most sensitive financial and operational data simply so an AI assistant can use it. They want AI to work with current information while continuing to respect enterprise security, access controls, governance, and data-management policies.

Essbase is clearly becoming part of this broader vision.

Essbase can provide a dimensional business model over information managed in Autonomous AI Lakehouse. Essbase provides the dimensions, hierarchies, calculations, and familiar analytical interfaces, while the detailed data can remain in the lakehouse.

That represents a significant architectural shift.

To be clear, Oracle has not formally announced that Essbase itself will become a native database engine inside Autonomous AI Lakehouse. However, the direction is becoming much clearer, and Oracle has been talking about this concept for over a year. Further, they showed some very impressive demos at Kscope26. My expectation is that this innovation will be announced very soon.

Making Essbase More Available
For quite some time, Oracle has made Essbase available through the Oracle Cloud Infrastructure Marketplace, or OCI Marketplace. Although the Marketplace deployment works well, Oracle has not promoted standalone Essbase very heavily.

Unless you were already an Essbase customer or specifically looking for the product, you might not even know it was available.

Oracle’s work to connect Essbase more closely with Autonomous AI Lakehouse could change that.

Traditionally, customers have thought of Essbase as a separate analytical server. An Essbase environment includes applications, outlines, calculation scripts, rule files, data files, security definitions, and other supporting components. The server must be provisioned, configured, patched, monitored, and maintained separately from the source database.

The architecture Oracle demonstrated appears to bring Essbase much closer to the Oracle Database and lakehouse platform.

As we understand it, the Essbase application components will all be stored in the database, while Essbase uses Autonomous AI Lakehouse as its storage and processing platform. This could simplify the architecture considerably and make Essbase easier for Oracle Database customers and developers to discover.

Oracle has followed an interesting accessibility strategy for years with Oracle Application Express, or APEX. APEX is a web-based application development environment that runs on top of Oracle Database. It allows developers to build browser-based applications directly against information stored in the database without requiring an entirely separate application-development stack.

APEX has become extremely popular. In fact, a significant portion of the Kscope community and conference content is now dedicated to APEX.

Could making Essbase more readily accessible with Oracle Database create a similar opportunity?

Oracle Database has an enormous installed base. An estimated 400,000 companies may use Oracle Database worldwide. If even a small percentage of those companies are introduced to Essbase through Autonomous AI Lakehouse, it could bring multidimensional analysis to a very large group of database developers, data engineers, and business analysts who may never have considered deploying a traditional Essbase server.

I think that could be a very significant development.

Federated Cubes Change the Architecture
The clearest example of the relationship between Essbase and Autonomous AI Lakehouse is the federated cube.

Most traditional Essbase applications require a data-loading process.

Typically, data is extracted from a relational database or another source system, transformed into the format needed by Essbase, and then loaded into the cube. Depending on the application, that process may run monthly, nightly, hourly, or perhaps every few minutes.

No matter how frequently it runs, there is usually some delay between the time a transaction is recorded in the source system and the time it becomes available in Essbase.

There is also a substantial amount of infrastructure associated with the process. Someone must create and maintain the extracts, transformation rules, load rules, scheduling, error handling, reconciliation, and operational monitoring.

Federated cubes provide a different approach.

With a federated cube, Essbase can query detailed data stored in Autonomous AI Lakehouse directly. The data does not have to be copied into the traditional Essbase storage engine before users can query or aggregate it.

The Essbase outline continues to contain the dimensional metadata, including dimensions, member names, hierarchies, and formulas. The corresponding detailed information remains in a fact table within Autonomous AI Lakehouse.

In other words, Essbase provides the business-oriented multidimensional model while Autonomous AI Lakehouse stores and processes the underlying data.

This is a significant architectural shift because, historically, data generally had to be loaded into an Essbase cube.

With a federated cube, the detailed data can remain in the lakehouse.

Essbase Calculations Still Matter
One of the important points about federated cubes is that customers do not have to give up the analytical features that make Essbase valuable.

Organizations can still define dimensions, hierarchies, member formulas, calculations, and business logic in Essbase.

When calculations and queries are executed, Essbase can analyze the request and, whenever possible, translate the required work into SQL. That processing can then be pushed into Autonomous AI Lakehouse so the computation occurs close to the data.

Not every Essbase operation will necessarily be pushed into the database. Some calculations may still be processed by Essbase.

However, the general principle is important: perform the work where it makes the most sense.

Essbase continues to manage the multidimensional model and business logic. Autonomous AI Lakehouse manages the detailed data and performs SQL-based processing whenever possible.

This can improve scalability and reduce data latency because detailed information does not have to be copied into Essbase before it can be analyzed.

Live Data Without Traditional ETL
Think about what this means operationally.

If someone adds or updates information in the underlying lakehouse fact table, the updated information can become available to the Essbase application without waiting for the next traditional export-and-load cycle.

Organizations could potentially build real-time or near-real-time analytical applications without developing and maintaining the same type of data pipeline required by a conventional Essbase cube.

There is no need to create another complete copy of the detailed data merely so Essbase can query it.

This can reduce the cost and complexity surrounding ETL. It may also eliminate some of the reconciliation problems that occur when the source system and the analytical cube contain different versions of the data.

Writeback can also be supported. Information submitted through an Essbase interface can be written to stored intersections in the lakehouse fact table, while Essbase calculations and data-loading operations can generate updates to the underlying table.

That makes the relationship bidirectional rather than limiting it to read-only reporting.

Of course, real-time access does not eliminate the need for good design. Organizations still need appropriate dimensional models, governance, security, calculation logic, performance planning, and controls over writeback.

However, reducing or eliminating the traditional data-loading process could remove a substantial amount of technical complexity.

Essbase Gets a Broader Reach
The broader Autonomous AI Lakehouse strategy makes this even more interesting.

Autonomous AI Lakehouse supports Apache Iceberg and is designed to work with information stored across other data platforms and cloud environments. Oracle can connect to data and metadata associated with platforms such as Databricks, Snowflake, AWS, and other lakehouse environments.

Oracle can then apply capabilities such as SQL analytics, machine learning, AI Vector Search, graph analytics, spatial analytics, and natural-language querying to that information.

Now combine those capabilities with Essbase.

It may become possible to provide an Essbase dimensional model over a much broader collection of enterprise information.

For example, financial information may be stored in Oracle while operational data resides in Databricks, Snowflake, or an Iceberg-based data lake. Essbase could provide a common analytical model over that information.

Users would continue to think in terms of accounts, entities, products, customers, scenarios, periods, and organizational hierarchies. They would not need to think about physical files, database tables, object-storage locations, joins, or cloud-provider boundaries.

That is one of the things Essbase has always done extremely well. Essbase organizes information in the way business users think.

Autonomous AI Lakehouse can provide access to distributed enterprise data. Essbase can provide the dimensional business model, calculations, and interactive analytical experience over that information.

I think that combination could be extremely powerful.

AI Where the Data Lives
There is also an important AI implication.

A central part of Oracle’s positioning is that AI should operate where governed enterprise data already resides.

Autonomous AI Lakehouse includes capabilities for vector search, retrieval-augmented generation, in-database machine learning, natural-language SQL, and agentic workflows. These capabilities can operate against enterprise data while remaining subject to security, access controls, and governance.

This helps address one of the biggest challenges companies face with enterprise AI.

Companies do not want to move all of their sensitive financial and operational information into a separate environment simply so an AI system can use it. They also do not want an AI assistant to bypass the organization’s existing security and governance policies.

Running AI close to the data can reduce unnecessary data movement, preserve existing controls, and give the AI access to current enterprise information.

Essbase adds another important layer to that architecture.

The detailed data may reside in relational tables or Iceberg storage, but the business meaning of that data is often expressed through the Essbase model.

The Essbase outline tells us which members belong to which dimensions, how entities roll up into regions, how accounts are categorized, which calculations define important business metrics, and how users are permitted to access the information.

In that sense, Essbase does more than provide another query engine.

It provides a business-oriented semantic model.

That may become one of the most important roles Essbase plays in Oracle’s enterprise AI strategy.

Using AI to Build Essbase Cubes
Oracle demonstrated AI-assisted Essbase cube creation at Kscope26.

Building a good Essbase application has always required a combination of technical skill and business knowledge. The developer must understand the source data, identify the fact tables and measures, determine the dimensions, build the hierarchies, and decide how the application should aggregate and calculate information.

Oracle showed AI technology that can analyze database tables and recommend a potential Essbase cube structure.

For example, the AI can identify a table that appears to contain measures such as revenue, units, cost, or margin. It can then identify the keys that relate those measures to customers, products, entities, periods, or other potential dimensions.

Based on those relationships, the AI can recommend dimensions and hierarchies and then help create the Essbase application.

The AI is essentially looking at a relational star schema and recommending how it could be represented as a multidimensional Essbase model.

I do not think this eliminates the need for an experienced Essbase developer. The technology may be able to identify relationships in the data, but it does not necessarily understand every business rule or know how management wants to analyze the company.

There are often several technically valid ways to organize the same information. Determining the best design still requires someone who understands the business.

However, AI-assisted cube creation could give developers a very good starting point. Instead of creating every dimension and hierarchy manually, a developer could begin with the AI-generated model and refine it.

It could also make Essbase more approachable to Oracle Database developers who have never built a multidimensional application.

From Relational Tables to a Working Cube
The AI cube builder becomes even more interesting when paired with federated cubes.

In a traditional project, AI might identify the fact table and dimensions, but someone would still need to build a process to export the data and load it into Essbase.

With a federated architecture, the source data is already available to the Essbase application.

The AI can identify the tables, recommend a cube, create the dimensional structure, and connect that structure to data that remains in Autonomous AI Lakehouse.

The entire process could become dramatically faster.

A developer could potentially move from a collection of relational tables to a working Essbase analytical application without building a traditional ETL process and without maintaining a second copy of all the detailed data.

That could change the economics of creating Essbase applications.

Applications that once required a major implementation project may become practical for smaller departments, shorter-term projects, or specialized analytical requirements.

Natural-Language Queries
Another interesting capability is the ability to ask Essbase questions using natural language.

Instead of requiring the user to know MDX or manually construct a report, the user can describe the information they want in ordinary language.

For example, a user might ask:

“Show revenue and gross margin by region for the current quarter compared with last year.”

Essbase can interpret the request, determine the relevant dimensions and members, generate the appropriate query, and return an Essbase grid.

The user can then work with the result just as they would any other Essbase report. The grid can be pivoted, drilled into, recalculated, or opened in another interface for additional analysis.

Depending on the interface, the question could be typed or spoken.

The fact that Essbase returns a grid is important.

Many AI tools return a paragraph describing what they believe the answer to be. That can be useful, but financial and operational users often need to see the actual numbers arranged in a structured report. An Essbase grid gives the user something that can be reviewed, drilled into, recalculated, and validated.

It is not simply an AI-generated narrative. It is an interactive analytical result.

The Essbase MCP Server
The Essbase MCP server may be one of the most interesting new capabilities of all.

MCP stands for Model Context Protocol. You can think of MCP as a standardized middle tier between an AI assistant and an enterprise system.

The AI assistant does not need unrestricted access to the underlying database. Instead, the MCP server exposes specific tools and operations that the assistant is permitted to use.

In the case of Essbase, those tools might include listing applications, examining an outline, finding members, running queries, retrieving data, executing calculations, or performing other approved operations.  

The MCP server also respects security and limits what a particular user or AI assistant is allowed to do. What makes this especially interesting is that an MCP server enables companies use their chosen AI platform. A company standardized on Microsoft Copilot might connect Copilot to the Essbase MCP server. Another company might use ChatGPT or a different MCP-compatible AI platform.

The intelligence does not necessarily have to come from an Oracle-specific AI interface. The company can use the AI technology it has already selected while using Essbase as the governed analytical source.

Consider an example.

Suppose a regional cost-of-goods-sold number is significantly over budget. A financial executive might ask:

“Which entities contributed the most to the unfavorable cost-of-goods-sold variance in the Southeast region?”

The AI assistant could use the Essbase MCP server to examine the outline, identify the appropriate region, find the entities beneath it, retrieve actual and budget data, calculate the variances, and return the entities that contributed most to the result.

The user does not need to know the exact member names or how to write the query.

The AI assistant can formulate the query, but the numbers still come from Essbase. The existing dimensions, hierarchies, calculations, and security rules continue to apply.

That is a much better model than simply asking a general-purpose AI tool to guess at the answer.

Why This Is Important for Dodeca
These developments also strengthen the position of Dodeca.

Oracle is making it easier for AI systems to discover, query, and analyze enterprise data. That is important, but discovering data is only part of the problem.

Organizations also need a trusted way to present the information, apply business context, enforce workflow, validate results, and deliver reports that people can confidently use to make decisions.

That is where Dodeca is different.

Dodeca provides curated and governed reports, business context, enterprise security, and trusted spreadsheet-based analysis. It gives organizations a controlled environment for delivering the reports and applications that finance and operational users depend on every day.

As AI becomes better at asking questions and finding information, companies will increasingly ask a different question:

“How do I know this result is correct?”

Dodeca is well positioned to answer that question. 

The opportunity is not for Dodeca to compete with Oracle’s AI capabilities. It is to complement them. Check out: Don’t Just add AI, Add Confidence 

An AI assistant may be able to identify an interesting variance or discover a pattern in the data. Dodeca can provide the governed report that places the result in context, shows the supporting detail, applies the organization’s calculations and presentation standards, and gives the user a trusted analytical experience.

Further, the upcoming Dodeca MCP server will an AI assistant to identify the appropriate Dodeca report, open it, retrieve the governed result, and use that report as evidence supporting the AI-generated answer.

Instead of the AI saying:

“I believe this is the answer.”

It could say:

“Here is the answer, and here is the governed Dodeca report that supports it.”

That is an important distinction.

Many organizations have invested heavily in enterprise systems intended to eliminate uncontrolled spreadsheets. Yet business users continue returning to spreadsheets because they are familiar, flexible, and effective for financial analysis.

The problem is not the spreadsheet interface itself. The problem is uncontrolled spreadsheets, inconsistent formulas, disconnected data, and a lack of governance. Dodeca preserves the familiar spreadsheet experience while placing it inside a controlled enterprise platform.

In that sense, the Dodeca opportunity becomes even more interesting when we return to the APEX comparison. APEX provides a web-based application development interface on top of Oracle Database. It helps developers turn database information into governed browser-based applications.

Over the years, numerous people inside Oracle have referred to Dodeca as “APEX for Essbase.” I think that comparison is increasingly appropriate.

Dodeca provides an application-building and delivery platform on top of Essbase and other enterprise data sources. It allows organizations to create sophisticated spreadsheet applications, guided analytical workflows, reports, planning solutions, and operational processes without handing users an uncontrolled collection of Excel files.

Just as APEX helps developers build applications on top of Oracle Database, Dodeca helps organizations build governed business applications on top of Essbase.

However, Dodeca goes beyond simply displaying data.

It brings together Essbase, relational data, calculations, security, commentary, auditability, and familiar spreadsheet presentation in a single application environment.

As Oracle expands Essbase through Autonomous AI Lakehouse and introduces it to a broader population of database and AI developers, Dodeca can provide the application layer that turns those multidimensional models into trusted, business-ready solutions.

Oracle can make the data and analytical engine more accessible.

Dodeca can make the resulting information usable, governed, and actionable.

That is a highly complementary message, and I believe it creates a significant opportunity for both Essbase and Dodeca.

Oracle’s Multicloud Strategy
There is one more important piece to this story.

Oracle Autonomous AI Lakehouse is part of a broader multicloud strategy.

Oracle is making its database services and related technologies available in conjunction with OCI, Microsoft Azure, AWS, and Google Cloud.

This is important because most large companies already operate in more than one cloud.

A company may use Azure for application services, AWS for a data lake, Google Cloud for AI, and Oracle for financial and operational databases. Requiring the company to move everything to a single cloud is usually not realistic.

Oracle’s multicloud strategy recognizes that reality.

Autonomous AI Lakehouse is intended to bring analytics and AI to the data, regardless of whether the information is associated with OCI, Azure, AWS, Google Cloud, an on-premises environment, or another lakehouse platform.

This makes the opportunity for Essbase even larger.

Essbase would not be limited to customers building exclusively on OCI. It could participate in modern analytical architectures that span multiple clouds, catalogs, storage systems, and data platforms.

Is Essbase Back?
I am not sure Essbase ever really went away.

It continued to serve as an important analytical engine inside Oracle applications, and thousands of companies continued to rely on standalone Essbase for mission-critical reporting, analysis, and planning.

What changed was its visibility.

For several years, Oracle did not talk very much about standalone Essbase. There was not a large marketing campaign, and it was sometimes difficult to tell where the product fit into Oracle’s broader database, cloud, and AI strategy.

Based on what Oracle showed at Kscope26, that picture is becoming much clearer.

Oracle is positioning Autonomous AI Lakehouse as an open and governed foundation for enterprise analytics and artificial intelligence.

Essbase can provide a multidimensional business and semantic model over the information in that platform.

Federated cubes allow detailed data to remain in Autonomous AI Lakehouse rather than requiring traditional Essbase data loads. Calculations and queries can be translated into SQL and pushed into the lakehouse whenever possible. AI can help design the cube. Natural-language interfaces can make the information easier to query. MCP can make governed Essbase capabilities available to enterprise AI assistants.

Dodeca can then provide the governed application and reporting layer that turns those capabilities into trusted business solutions.

Taken together, these developments place Essbase in a much broader data and AI architecture than it has occupied in the past.

I think Oracle is positioning Essbase to become an important multidimensional analytical and semantic layer for its modern data and AI platform.

That is a very exciting direction.

Of course, some of the functionality demonstrated at conferences may still be under development. Product names, features, licensing, architecture, and release dates may change before everything becomes generally available.

Oracle has not announced that Essbase itself is becoming a native engine inside Autonomous AI Lakehouse.

However, the current federated-cube architecture and the capabilities Oracle demonstrated at Kscope26 show that Essbase is becoming increasingly connected to Oracle’s lakehouse and enterprise AI strategy.

The direction seems clear.

Essbase is not dead.

In fact, I think we may be seeing the beginning of an Essbase renaissance.

As someone who has worked with Essbase for many years, I am absolutely thrilled to see it.

 

Categories: BI & Warehousing

So Long 2025 and Welcome 2026

Mon, 2026-01-05 10:42

As we wrap up 2025 and head into 2026, it’s worth taking a quick look back and taking a sneak peek ahead.

We were proud to show our leadership in the Oracle community by once again being the top-level Titanium sponsor of the ODTUG Kscope25 conference in Grapevine, Texas. Kscope is the top technical event for people working with Oracle EPM, Oracle APEX, Oracle Cloud Infrastructure, Oracle Database, and other related technologies. It was a lot of fun seeing customers, partners, and friends in person.

This past year was also a great one for Dodeca. We added some initial AI capabilities, delivered significant enhancements for EPM Cloud, and introduced direct support for several new data sources such as Oracle Cloud EDM and Parquet. We also strengthened security by enhancing support for both SAML and OAuth authentication.

We had some fantastic customer presentations as well including a presentation where one customer shared how Dodeca helped their organization save more than 1,600 man-days per quarter. That is just one of many similar stories we hear from customers.

To add to our acclaimed technical support, we introduced free, online, hands-on training for Dodeca. In this 3-hour, instructor-guided class, you can try Dodeca for yourself and see what all the hype is about. As a self-funded, founder-led company, our measure of success isn’t profitability. Instead, it is the success of our customers who continue using our products in mission-critical applications year after year.

We did have some sad news this year.  In late October we lost our Chief Operating Officer, Michael McCarty, after a short illness. Michael was a friend for 30 years and helped lead our company for the past 25 years. His experience and humor are deeply missed, and we’re grateful for everything he contributed to our team and our work.

Looking ahead to 2026, we’re excited to keep pushing forward. We’ll continue adding capabilities to help customers eliminate spreadsheet chaos and use standardization, automation, and enterprise integration to bring confidence to the accuracy of corporate spreadsheets. And if you’re not yet a Dodeca customer, hopefully this will be the year you discover what Dodeca can do for you.

Here’s to a great 2026.

 

Categories: BI & Warehousing

Happy 40th Birthday to You, Excel!

Mon, 2025-09-29 14:36

I thought about calling this post, “The Meaning of Life: Excel Edition”, as Excel has played such an important part of my life.

My lifelong journey with spreadsheets began in college. Back then, I assigned portions of assignments to different members of my group and asked them to give me their work in SuperCalc format. I then assembled and integrated everything into a single project. That was my first real glimpse of how powerful spreadsheets could be, a skill I expanded in my first job at Deloitte.

It was the late 1980s, and I quickly discovered that working with spreadsheets was more fun than doing normal accounting work. I drafted audit schedules and even learned to program using macros in Lotus 1-2-3. That curiosity led me to my first true spreadsheet job in Huntsville, Alabama, at Intergraph, where I handled international consolidations in SuperCalc 5. In that role, I both leveraged and expanded my spreadsheet skills. Within a year, I had automated all spreadsheet reporting and took on additional responsibilities for automating some reports for our corporate reporting department.

At Intergraph, I saw my first graphical spreadsheet—Wingz from Informix—and I thought it was terrible. Keyboard support was non-existent, which slowed everything down compared to the “slash” commands used in the DOS spreadsheet products.

That perception completely changed the first time I used Excel. I led the effort to standardize on Excel at Intergraph, where I managed international consolidations for 45 subsidiaries. In those days, I had the very first true PC in the company. It was on a cart that was supposed to be shared by the entire Finance Department—but it lived in my office. That’s where I got my first real hands-on experience with Excel, starting with version 4.0. From then on, Excel was central to my career.

Initially, I learned macros in Excel using the old XLM language, which felt natural after my experience with SuperCalc. But when Excel 5 introduced VBA in 1994, I dove in headfirst and started doing things that had never been done before. I was probably the first person to hook up Excel to Oracle Financials in a pivot table during the Excel 5 beta program! That also gave me a glimpse of the possibilities spreadsheets opened up. It also exposed some of the complexities, as the SQL query for that pivot table required a nine-table join.

My passion for Excel only grew stronger when I attended Microsoft TechEd in 1993 and met some of my Excel heroes. Around the same time, I started answering questions in the CompuServe Excel forum, which became my first real experience with community support. I’d read questions in the evening, experiment at work the next day, and then return with answers.

Before long, Microsoft recognized a few of us CompuServe forum contributors as MVPs, or “Most Valuable Professionals.” At the time, none of us really knew what that meant. All we knew was that we were getting T-shirts and trinkets for doing what we loved: helping people use Excel better. Still, it was quite an honor, as there were only five of us “Charter MVPs” in the entire world.

Excel opened doors I couldn’t have imagined. In late 1994, I left Intergraph to do Excel consulting, building Excel-based applications for companies, including Microsoft itself. Some of those applications were used personally by Bill Gates.

It was during this time that I encountered Essbase, which used Excel as its original interface. I wrote the first demoware for Essbase, the “Open Budget Pak,” a project funded by Microsoft to demonstrate Excel as a user interface for a database. After that product was complete, Arbor Software, the inventors of Essbase, sent me around the country working on the user interface side of high-profile Essbase projects. That experience was the spark that eventually led me to start Applied OLAP.

The first customer of Applied OLAP was a company in the telecommunications industry that used Essbase for reporting, but had an Excel-based budgeting system comprised of 1,200 linked Excel workbooks. That system was impossible to maintain, so I engineered and built a custom budgeting system for them using Visual Basic. The maze of linked workbooks I encountered there inspired me to create a product. Once I finished my work, I began conceptualizing our first product, ActiveOLAP, which was later adopted by numerous companies in the early 2000s.

Our flagship product, Dodeca, was born out of this journey. The idea came from decades of working with companies that used spreadsheets not just as tools, but as full-fledged, mission-critical systems. With Dodeca, we set out to standardize, automate, and streamline spreadsheet processes—reducing errors, saving time, and turning Excel into a true enterprise platform.

Forty years after its release, Excel has made a profound impact on my life. It gave me a career, introduced me to a global community, and led me to create products that continue to shape how businesses use data today.

If not for Excel, I honestly don’t know where I’d be. What I do know is that my life wouldn’t have been nearly as rich as it has been so far. I’m grateful that Microsoft invented Excel, and I’m proud of the path it set me on.

Let’s go back to the start of this post and my comment on Excel and the meaning of life. Look again at the formula and say, in English, what this formula is calculating.

That’s right—it reads, ‘To be, or not to be’! Ironically, no matter what numeric or Boolean value you place into cell $B$2, it always evaluates to TRUE. So, according to Excel, the ‘meaning of life’ is simply… truth.

Happy birthday, Excel—here’s to the next 40 years.

Categories: BI & Warehousing

Time to Resurrect This Blog!

Mon, 2025-09-29 13:55

It’s been more than seven years since I last posted here, and I think it’s time to get back to writing.

There are a number of reasons I’ve been away from the blog. The biggest was a significant healthcare challenge within my family. My wife was diagnosed with early-onset Alzheimer’s disease, and for several years I was her full-time caregiver. Alzheimer’s is a terrible disease, and sadly, my wife passed away a couple of years ago. My experience is something I hope nobody reading this will ever have to endure.

During this time, I continued to run my company, but I had no energy left for writing. Those years were exhausting, and much of my creative energy went into the business itself. Now, though, I am recovering from the experience and have once again found myself with things I want to share. That’s why I’m bringing this blog back.

You may notice some visual updates here over time, such as a refreshed look and updated links, but more importantly, I hope to post new content on a more regular basis. I already have some ideas lined up, and my next post should be coming within the next day or two.

Thank you for returning to the blog. Over time, I plan to share not only my thoughts and opinions but also content related to Dodeca, Excel, Oracle Cloud EPM, Essbase, and Oracle ADW. I hope you continue to find useful insights here.

Categories: BI & Warehousing

Happy 20th Anniversary to Applied OLAP!

Sun, 2018-03-18 13:08

Today, March 18, 2018, is the 20 year anniversary of our incorporation! I has been a long journey since that time; here are some of the highlights:
  • 1998 - We were a one-man shop and wrote a reporting and budgeting application for a customer in New York. I spent about 150 nights that year on the road.
  • 1999 - ActiveOLAP for Essbase was released and we earned the trust of two of our long-term customers. It was during the next couple of years that I traveled to the West Coast about 35 times in one year.
  • 2003 - Portions of our web-service technology was acquired by Hyperion Solutions and we wrote the Hyperion Objects product based on that technology.
  • 2007 - The Dodeca Spreadsheet Management System was released.
  • 2014 - We hired our first resource to focus solely on sales. Prior to that, we marketed our software via 'word of mouth'.
  • 2016 - The Dodeca Excel Add-In for Essbase was released and we acquired the DrillBridge product.
During this time, we have grown the company organically without outside investment. While this strategy meant we had slower growth, the benefit is that it has allowed us to focus solely on the needs of our customers and not the needs of 'investors'. It also meant that we 'ate a lot of beans' in the early days. Those were great lessons in the value of a dollar that we carry with us today in the value of the software we provide to our customers.

Thank you to all of our customers. We feel lucky to work with each and every one of you and we continue to learn from each of you. We pledge to continue working hard to make your companies successful.

Tim Tow
Founder and President
Applied OLAP, Inc
Categories: BI & Warehousing

Using MDX for Generated Members in Essbase Reports

Thu, 2018-01-11 18:30

There are times when Essbase users may need to see an ad-hoc collection of members aggregated together in Essbase, and that isn’t always an easy task.  If it were an aggregation that is needed on a recurring basis, the Essbase administrator may add an alternate hierarchy to assist.  Other times, users might just create a spreadsheet with the desired members in different rows or columns and use Excel formulas to add them together.  In this blog post, I will cover a third option, the use of MDX to create dynamically-generated members, how to run them in Smart View, and how to make them much easier to use in Dodeca.

In order to illustrate how dynamically-generated members can be used, let’s consider an example using the Sample Basic database.  Here is a simple quarterly income statement query that I will use as the basis for this blog post:

SELECT
    {[Year].Children, Year} on COLUMNS,
    Hierarchize(Descendants([Profit]), POST) ON ROWS
FROM 
    Sample.Basic
WHERE 
    ([Market].[New York], [Product].[Colas], Actual)

The results from this simple query look like this:



This MDX is pretty straightforward, but what if you wanted to see how New York and Connecticut would look if they were combined?  This is the question that a generated member can return for you.

Generated members in MDX are created using the WITH MEMBER clause.  Moreover, the generated member can then be used anywhere a normal member can be used, even in a slicer dimension (or what we would call a ‘page field’ in the classic Essbase add-in or a point-of-view in Smart View).  Here is the query modified to use the new generated member:

WITH MEMBER
    [Market].[SelectedMarkets] AS 'SUM({[New York], [Connecticut]})'
SELECT
    {[Year].Children, Year} on COLUMNS,
    Hierarchize(Descendants([Profit]), POST) ON ROWS
FROM
    Sample.Basic
WHERE
    ([Market].[SelectedMarkets], Colas, Actual)

The results from this query look like this:


So far, so good, but there are a couple of things to note.  First, the member displayed in the POV is not a real member; that is to be expected.  This leads to the second thing in that you cannot refresh the query as an ad-hoc analysis; the dynamically generated member name will be replaced with the dimension member name in its place.

To go even further, what if you want to have multiple generated members?  In that case, the syntax is easy as you just continue with another MEMBER clause:

WITH MEMBER
    [Market].[SelectedMarkets] AS 'SUM({[New York], [Connecticut]})'
MEMBER
  [Product].[SelectedProducts] AS 'SUM({[Colas], [Grape]})'
SELECT
    {[Year].Children, Year} on COLUMNS,
    Hierarchize(Descendants([Profit]), POST) ON ROWS
FROM
    Sample.Basic
WHERE
    ([Market].[SelectedMarkets], [Product].[SelectedProducts], Actual)

The results of this query look like this:



The syntax for creating and using generated members is not that difficult, but there are a couple of things that make it a bit more difficult than it should be for end users to use this approach.

First, any time end users start having to deal with scripts of any kind, the level of complexity goes up exponentially.  As one of my mentors used to say, “The difference between zero lines of code and one line of code is much greater than the difference between one line of code and a hundred lines of code”.  In other words, it is hard to get users to deal with code of any kind.

Second, once an end user has to ‘write a line of code’, or script in this case, then they assume the responsibility for it being correct.  As there are differing levels of comfort and skill among users, the risk of error goes up.

Finally, when users use a script like the one used in this example, they have to type in the correct member names or, again, risk error. Here is the new MDX dialog in Smart View 11.1.2.5.720 showing where users type in the MDX including the member names.



To make it much easier for end users, Dodeca does a couple of things.  First, Dodeca developers can configure reports to use MDX without the end user ever having to know that MDX is powering the report ‘under-the-covers’.  Further, Dodeca has flexible Point-of-View selectors that allow the end user to simply pick which members they want to use in the query.

Dodeca report developers use tokens as a sort of substitution variables in the script.  The tokens are replaced in the script at run-time by the members selected by end users.  Here is the same script with tokens in place of the hard-coded values:

WITH MEMBER
  [Market].[SelectedMarkets] AS 'SUM({[T.Market]})'
MEMBER
    [Product].[SelectedProducts] AS 'SUM({[T.Product]})' 
SELECT
    {[Year].Children, Year} on COLUMNS,
    Hierarchize(Descendants([Profit]), POST) ON ROWS
FROM
    Sample.Basic
WHERE
    ([Market].[SelectedMarkets], [Product].[SelectedProducts], Actual)

The Dodeca Essbase Scripts editor has tools to help the report developer create and test MDX scripts.  Here are the Test Tokens available in the editor that allow developers to simulate the values plugged in by the Point-of-View selectors:


And the script itself in the scripts editor which has built-in testing facilities:



Finally, here is a Dodeca view that utilizes the tokenized MDX query and allows users to easily select the members they want dynamically aggregated and the report is produced without the risk of error.



Let me know if you would like to learn more about Dodeca and how it could help your company.


Categories: BI & Warehousing

Windows 10 Update Killed Essbase On My Laptop!

Thu, 2018-01-04 00:32

Like many Essbase consultants and developers, I run Essbase server on my Windows 10 laptop. It was a lengthy ‘Creator’s Update’ Windows update and, once it was complete, Essbase was dead on my machine. So, what do I do? First, I didn’t panic; us pilots have a way of not panicking when things don’t go as planned. We have several people internally who had this happen to them over the past several months and we fixed it each time, so there was nothing to worry about.

The root cause was that my OPMN service, which runs Essbase, was gone. This happened on the other machines we have that experienced that in the past, so I went to talk with one of our resident infrastructure gurus, Jay Zuercher. I remembered there was a command that I could run to recreate the service; Jay had the command filed away somewhere and within a couple of minutes, he sent it to me:

SC CREATE "OracleProcessManager_epmsystem1" binPath="C:\oracle\middleware\epmsystem11r1\opmn\bin\opmn.exe -S -I c:\oracle\middleware\user_projects\epmsystem1”
I ran this command – as an administrator – and then went into services to set the service to start automatically and start the service running. That did not, however, result in Essbase coming back to life. Next, I looked at the Essbase logs and noted several issues having to do with security. Initially, I thought there may have been due to an issue with Shared Services, but then I remembered about the fairly common Essbase issue regarding a corrupted essbase.sec file. I don’t know if the corruption was related to the Windows Update, but the timing sure was suspect. I replaced the essbase.sec file with a backup copy and I was back in business.

Hopefully this doesn’t happen to you when you update Windows but, if it does, perhaps this blog post will make your recovery quick and painless.
Categories: BI & Warehousing

Possible solution for TLS 1.2 issues between Windows 10 and Oracle EPM Weblogic

Thu, 2017-07-13 15:17

We have recently seen some users of both Dodeca and Hyperion products where Windows 10 machines have issues connecting to the Weblogic servers shipped with Oracle EPM due to the absence of the TLS 1.2 protocol.  The underlying issue is that Windows 10 is an evolution of technology whereas Oracle EPM Weblogic, and more specifically the Java version tested and shipped with it, are stuck in the stone age.  Java 1.6 started its journey to "end of life" in late 2013 and, though it continues to be covered under Extended Support, the EPM team has not delivered an update for their server.  Ironically, there is even a Java 1.6 version, Update 121, that now supports TLS 1.2; EPM is on Update 35.

So, what do you do?  I would be very hesitant to upgrade the Java version delivered with the EPM System.  After all, Oracle spent a lot of time working to certify on that version of Java.  One of our Senior Support Engineers, Jay Zuercher, did find something that appears to work - it hasn't yet been widely tested but may be worth a try.  Here are the steps he followed:

  1. Login to the Weblogic console.
  2. Navigate to Environment->Servers->AnalyticProviderServices0 (or to the server in which you are attempting to connect).
  3. Click on the SSL tab and expand the Advanced section at the bottom.
  4. Enable the “Use JSSE SSL” checkbox.
  5. Save changes.
  6. Navigate to the Server Start tab.
  7. Add the following string to the “Arguments” box:
    1. -Dweblogic.security.SSL.protocolVersion=TLS1
  8. Save changes.
  9. Activate all changes.
  10. Restart the applicable service. 
These steps are furnished with no guarantees, but hopefully you will find them helpful.


Categories: BI & Warehousing

ACE Alumni

Tue, 2017-05-23 23:08
Today, I asked Oracle to move me from Oracle ACE Director status to Oracle ACE Alumni status.  There are a number of reasons why I decided to change status.  When I started answering questions on internet forums years ago, I did it to share what I had learned in order to help others.  The same goes for this blog which I originally started so that I could give better and more complete answers to questions on the forums.

After the Hyperion acquisition by Oracle, I was contacted by Oracle who asked if I would be interested in becoming an "Oracle ACE".  It was an honor.  But over time, things have changed.  As more people found out about the ACE program, more people wanted to become an ACE.  If you have ever monitored the OTN Essbase and Smart View forums, they have become cluttered with copy and paste posts from people obviously trying to increase their points.  As the ACE program grew, it also become harder for the OTN team to manage and now require a formal activity reporting - a time report if you will - to track contributions to the community.  As I am already extremely pressed for time, I decided that tracking my contributions to the community - in exchange for a free pass to Open World, just didn't make sense.

All of that being said, just because I have moved to Oracle ACE Alumni status doesn't mean that I will stop contributing to the community.  My company will continue to provide free downloads and support for the Next Generation (Essbase) Outline Extractor and the Outline Viewer along with free downloads of Drillbridge Community Edition.  And maybe, just maybe, I will finally have time to write some new blog posts (maybe even some posts on some new Dodeca features inspired by our work with Oracle Analytics Cloud / Essbase Cloud!)

Categories: BI & Warehousing

Breaking News! Dodeca Spreadsheet Management System Certified on Oracle Analytics Cloud!

Thu, 2017-04-20 23:37
Now that the Oracle Analytics Cloud, or "OAC", has been released, we had to get serious about our work with one of the Oracle Analytics Cloud components, the Essbase Cloud Service, or "EssCS" for short.  You would think that we should have been working hard on EssCS for quite some time, but we had been assured by Oracle product management that the Essbase Java API would be available in EssCS.  Of course, Dodeca was built using the Essbase Java API and thus we expected that support for EssCS would be very easy.

We got access to a production version of the EssCS last week and started our work.  As promised by product management, the Essbase Java API is available in EssCS and, believe it or not, we did not need to change a single line of source code in order to support the Essbase Cloud.  We did, however, have to update our build processes to use Java 8 instead of the decrepit Java 6 used in Essbase 11.x.

As far as configuration inside Dodeca itself, the only change we made was that we configured the APSUrl in the Essbase Connection object to point to the Essbase Cloud APS instance.  Note that the URL format has changed in the cloud.  The Java API was accessible in Essbase 9.3.1 through Essbase 11.1.2.4 using the format:

http://<server>:<port>/aps/JAPI

In the cloud, this has changed to:

http://<server>:<port>/essbase/japi

The Essbase Connection configuration looks pretty much the same as the configuration for an on premise connection configuration:








Of course, the Dodeca views look identical when run against an on premise or a cloud server:



In summary, it was trivial to test Dodeca using EssCS.  Every single Essbase functionality that we use in the product, from data grid operations to metadata operations and even report scripts, worked exactly the same as it does against an on premise Essbase cube.  Based on our testing, we are certifying the Dodeca Spreadsheet Management System to work on the Oracle Analytics Cloud.

We have a number of innovations we plan to introduce in the near future aimed to improve the Essbase Cloud experience, so stay tuned.  If you are planning to come to Kscope17 in San Antonio, plan to attend the Dodeca Symposium and you may just be the first to see of these cool new things!


Categories: BI & Warehousing

Kudos to the Dodeca Support Team!

Thu, 2017-02-16 00:10
Here at Applied OLAP, we have a great support team that we have tasked with doing their best to make sure our customers are happy with their Dodeca software.  On most days, they are answering emails and firing up impromptu support webcasts to help our customers with any questions they have.

Today, we got some great feedback from a customer after a popup support webcast.  Here is an email I received today from Edgardo Rodriguez at Wall Street law firm Sullivan and Cromwell (email used with permission):

Hi Tim – just wanted to drop you a quick line on the tech support I received this morning.  Kevin & Rodney were helping me with a headcount report.  Have to tell you – they were great. They helped me leverage some of their code and walked me through the finer points of using it under our scripts – all under an hour!  They also turned me on to using the SQL retrieve as a possible solution (which I will be using shortly).

I appreciate you support more than you know. 

Just thought you should know..

Regards

Ed

So, here is a special call-out to our support team.  Way to go!
Categories: BI & Warehousing

Essbase Cloud Service Webcast Available Online

Mon, 2017-01-02 10:55
Essbase Cloud Service is coming soon and, if you haven't had a chance to learn about it, here is how you can learn about it at your leisure. Oracle had a public webcast about two weeks ago and have made both the webcast and the slides available; you must have an Oracle login to see it. Here is the link to the EssCS webcast:

EssCS Webcast

And here is a link to the slides:

EssCS Webcast Slides

Essbase Cloud Service has some exciting new functionality that you should check out, even if you plan to keep your Essbase installations on-premise.  Over time, I would expect that most, if not all, of the innovations you see will be released in the on-premise version of Essbase.  If I were a betting man, I would guess the timing to be likely near the end of 2017 with the lrelease of "EPM 2017", which is the code name for the next major on-premise release,

Enjoy!
Categories: BI & Warehousing

Another Thanksgiving Email

Sat, 2016-11-26 16:06
Here is another email we got Wednesday from a Dodeca Excel Add-In for Essbase customer.  This customer has a lot of VBA macros running some automation with Essbase and asked for some assistance.  We told it was as easy as replacing they Essbase function declarations file with our Dodeca Add-In  function declarations file, and then setting the variables that contain the location for the Dodeca-Essbase server.  In other words, replace this file:

With this file:





Easy, right? Here is the email:

I hadn't had a chance to test this massive file out with the latest version of the add-in yet, but was more than pleasantly surprised when I replaced the add-in code for the Dodeca wrapper and the only thing I had to do was change the connection to our new server and the file was live! It's truly drop and go! Thanks so much!!!!



Categories: BI & Warehousing

Happy Thanksgiving (Featuring an Email That I Am Thankful For)

Wed, 2016-11-23 11:12

I get tons of email every day and often have hundreds of emails that have to track on a daily basis which explains, in part, my absence from doing recent blog entries.   Most of the emails I track are sales and support relating to Dodeca.  After all, with Dodeca, the buck stops here, right?  I also get some spam in the mix.  Sometimes, there is an email that really makes my day.  Here is one of those types I received this morning:

From: (masked)
Sent: Wednesday, November 23, 2016 8:48 AM
To: Applied OLAP Support
Subject: Happy Thanksgiving!

Although we are a smaller Essbase shop at 105 users, our users are strong advocates for Essbase and use it extensively. So, it is great timing to say how thankful I am to you and your teams for developing the Dodeca Essbase Add-In. At the end of the day, it’s the user experience that drives the support for Essbase and your product will excite our user base! [And, it requires little or no support from my team!]

Happy Thanksgiving to all!


Wow!  This email really made my day!  To our new customer that sent this email (and who graciously granted me permission to post her words), Happy Thanksgiving to you as well.  We hope this is the first of many Thanksgivings that we work together!


Categories: BI & Warehousing

Thoughts/Info on Essbase/EssCS after Oracle Open World 2016

Sat, 2016-09-24 21:59
I was at Oracle Open World last week and have some notes to share on the upcoming EssCS (Essbase Cloud Service) product and on Essbase in general.

EssCS will be Infrastructure as a Service (IaaS) product and will be priced on the number of cores and memory.  I believe there will be a metered and an unmetered pricing as well (metered meaning pay per cycle or some other usage measure).  According to presentations at Open World, which did have safe harbor statements meaning "do not make decisions based on this info as it could change", there will be options for 1, 2, 4, or 16 physical cores ("OCPU's") and 7.5 to 15 Gb of RAM.  In addition, it will be an updated version of Essbase that is not the current on-prem version.  It will feature, among other things:


  • The new Java Agent running in Weblogic that moves security from the essbase.sec file to a relational database.
  • Simplified security
  • ASO / BSO / Hybrid
  • Sandboxing and scenario management - what if's without copying data with workflow for approval to merge into given scenarios
  • Cloud-based UI
  • Ability to specify/create an Essbase cube from Excel 
  • A cube template library ("app store" type of thing)
  • A web-based outline editor (though most editing should be done in Excel)
  • EssCLI (Essbase Command Line Interface) - a sort of EPMAutomate for Essbase
  • The Essbase Java API and a new REST API (which is currently being engineered)

I do not remember hearing any dollar amount for EssCS at Open World.  I expect availability in the next 3 to 6 months though it wouldn't surprise me if it were to slip further.

As far as on-prem Essbase updates, I would expect that the updates we see in EssCS will go on-prem as part of the EPM 2017 on-prem release which Oracle currently believes will be delivered late in 2017 (also subject to safe harbor, etc).

As far as how Oracle is selling Essbase, Essbase is now firmly in the BI organization and is being sold by the BI reps; EPM reps do not generally sell Essbase.  To the Essbase team, EPM is important as they are an internal customer, but EPM is not their only customer.  As such, I saw at least one presentation that promoted the idea of customers writing custom planning applications using Essbase.  While some people I talked with thought that approach muddled the EPM message for customers, I see it as a situation where if they don't compete in the custom market, then someone else will.  As someone who frequently is involved in complex applications where the EPM Planning product may not be a fit, I am thrilled to see that message from Oracle.




Categories: BI & Warehousing

Cool Essbase MDX Stuff – Dimension Properties Edition

Wed, 2016-08-17 22:12
I have always liked MDX as it exposes very powerful operations for creating member sets.  It has this amazing ability to take a set of members, union it with another set of members, intersect it with yet another set of members, and then exclude members from yet another set.  This is very powerful.

Recently, I was talking with a customer about some creative uses for MDX and they told me about some cool things they had done with MDX.  Kudos to George Cooper and Esam Jaber at Gap for showing me some creative MDX.  Though this is a really cool and new-to-me technique, after doing some online searches I found that the technique isn’t new to everyone.  Both Gary Crisci and Harry Gates have blogged on it already at http://garycris.blogspot.com/2014/03/mdx-queries-to-get-parentchild-and.html and http://www.cubesavvy.com/cubesavvy-utilities-updated-mdx-capabilities, respectively.

This technique, which gives developers access to outline information that was not previously available, also seems to be lightning fast.  I decided maybe I need to write on it as well and, of course, put my own spin on it by showing how we can use this in the Dodeca Spreadsheet Management System.

So, what is it?  They are called dimension properties and can utilize the PROPERTY_EXPR function.  This function provides the ability to query for, and return, members related to a given member.  The ability to return related members, on the same row, is something that is unique to the Essbase query languages.  The Grid API, or query-by-example and used to retrieve data from Essbase in the classic add-in, Smart View, and Dodeca, cannot do it out of the box.  The Essbase Report Script language, which is frequently used for exporting data, cannot do it either.  Regardless, we have customers who have asked to how have the parent member and/or the grandparent member on the same row as a member.   In the remainder of this blog post, I will explain dimension properties, and more specifically, the PROPERTY_EXPR function.

For this post, I will use the Geography dimension of the ASOSamp.Sample database.  The Geography dimension features members from different regions of the United States including details of the Region, the State, the City, and the Postal Code.  Here is a screenshot of some members in the Geography dimension:

Geography dimension members
With this dimension, let’s suppose you would like to get outline information at the city level, or level 1 in Essbase-speak.  In addition, let’s say you would like to have the state and the region as well.   Finally, while we are at it, maybe you also want to know the generation number of the member.  With dimension properties and the PROPERTY_EXPR function, you can get all of that information in one trip to the server.  Here is the query:

Select {} on COLUMNS,
Hierarchize(Descendants(Geography, Geography.Levels(1), SELF_AND_BEFORE), POST)
DIMENSION PROPERTIES
 GEN_NUMBER,
  PROPERTY_EXPR(Geography, MEMBER_NAME, Ancestor(CurrentAxisMember(), Geography.Generations(2)),"Gen2"),
  PROPERTY_EXPR(Geography, MEMBER_NAME, Ancestor(CurrentAxisMember(), Geography.Generations(3)),"Gen3")
 ON ROWS
FROM ASOSamp.Sample

Let’s break down the query to examine the components.  First, the Column axis specification is simple:

Select {} on COLUMNS,

The Column axis specification contains an empty set, but why do you need to even specify a set at all?  The Essbase MDX specification states that a query cannot skip axes based on a set order.  It seems much more natural for me to get data back on the second axis, or the Row axis, and as the Column axis is the first specified axis, a query cannot skip the Column axis and specify a Row axis.  As we really aren’t looking for any data to be returned in this query, then we can just use an empty set.

Next, let’s look at the Row axis specification:

Hierarchize(Descendants(Geography, Geography.Levels(1), SELF_AND_BEFORE), POST)

Let’s split this into sections starting from the inside out.  The Descendants function returns, naturally, all of the descendants of a given member down to, and including, level 1 members in the outline.  For this database, this query will return the Geography dimension down to the City level, but will not return the bottom, or zip code, level.

The member set returned by the Descendants function is then sorted by the Hierarchize function.

Hierarchize(Descendants(Geography, Geography.Levels(1), SELF_AND_BEFORE), POST)

The POST argument specifies that child members are sorted before their parent as they are in the spreadsheet add-ins.

Next, the DIMENSION PROPERTIES modifier for the Row axis specification provides the ability to return additional outline information related to each member returned in the set.  The DIMENSION PROPERTIES specify that the generation number, the related generation 2 and generation 3 members are returned for each member in the set.

DIMENSION PROPERTIES
 GEN_NUMBER,
  PROPERTY_EXPR(Geography, MEMBER_NAME, Ancestor(CurrentAxisMember(), Geography.Generations(2)),"Gen2"),
  PROPERTY_EXPR(Geography, MEMBER_NAME, Ancestor(CurrentAxisMember(), Geography.Generations(3)),"Gen3")

The GEN_NUMBER argument is self-explanatory, so let’s look at the PROPERTY_EXPR function to see how it works.  Consider this portion of the statement:

PROPERTY_EXPR(Geography, MEMBER_NAME, Ancestor(CurrentAxisMember(), Geography.Generations(2)),"Gen2")

The first argument is the dimension name, so that is easy enough.

The second argument, called the property_name argument, has a number of valid values as documented by Oracle in the documentation.  Valid values are MEMBER_NAME, MEMBER_ALIAS, LEVEL_NUMBER, GEN_NUMBER, IS_EXPENSE, COMMENTS, RELATIONAL_DESCENDANTS, MEMBER_UNIQUE_NAME), an attribute dimension name, an alias-table name, or a UDA.  At the time of this writing, the current docs are located at http://docs.oracle.com/cd/E26232_01/doc.11122/esb_tech_ref/frameset.htm?mdx_property_expressions.html.

The third argument is called member_value_expression and this is where the magic happens.  In this argument, you can use a number of functions that return exactly one member that is related to the current member.  In my example above, the Ancestor function is being used to return, for each member in the set as specified by the CurrentAxisMember function, the ancestor of that member at a given generation.  Among other functions are functions that allow you to get the parent, first child, next sibling, or previous sibling of the current member.

The fourth and final argument is used to give a title to the column containing the extended information.

So, now that we know a bit about this syntax, what does it look like in EAS?  

MDX dimension properties in EASIn their blogs, both Gary and Harry talk about ways to actually consume this information.  Harry wrote a special interface to display dimension properties returned by MDX.  Gary talked about the idea of using an Excel macro to parse the output.  I had my own ideas on how to use dimension properties in Dodeca.  I setup a simple Dodeca report and used a only 3 lines of our automation language, workbook scripting, to build the view.  Here is screenshot of my simple Dodeca view.

Dynamic MDX View in DodecaWhile running this view in Dodeca, the user can filter based on Geography, Product, and Stores dimensions and the report is highly dynamic.  The automation in Dodeca performs these tasks:
  1. Returns members and properties as the descendants of a user-selected Geography member
  2. Retrieves and places the dimension properties and the members on the worksheet
  3. Places the selected Product and Stores dimension members in the proper location
  4. Retrieves Essbase data into the worksheet
  5. Creates Excel grouping based on the generation number
I will leave the step-by-step of creating this view in Dodeca to another blog post.

So, how could you use dimension properties?


Categories: BI & Warehousing

ODTUG Leadership Program / ODTUG Board Elections

Sun, 2016-08-07 09:36
As many of you know, I have spent many years helping the Hyperion community in many ways.  I have wrote this blog for quite some time, have taken on the OlapUnderground Utilities and provided free support through my company,  In other words, I have worked hard to give back to the community and, in return, have had many kind words from people we have helped.

As a result of this spirit of giving back, I was lucky enough to be elected to the ODTUG Board of Directors which brings me to the real point of this blog post.  There is an upcoming ODTUG Board Election coming up and, due to term limits, my time on the board is coming to a close.  The same is true of my good friend Cameron Lackpour.  With two board seats open, could *you* be the next person to step up?

Here are my thoughts on what it takes to be a board member.  Joining the ODTUG Board isn't just something you decide to do and it isn't an achievement that is 'a feather in someone's hat'.  It is a commitment to helping others in the community for two years.  But it is more than that.  To be a successful ODTUG Board member, the commitment you have should be a continuation of the long-term commitment you have made to helping others.  So, if you are interested in serving on the ODTUG Board at some time in the future, the thing you should do is #GetInvolved.  If you are not already involved, the easiest way to get involved is to volunteer on the ODTUG Volunteer page.

If you are ready to step up from that point, for a number of years, ODTUG has maintained a Leadership Program to help train the next generation of users.  The application process for this year's Leadership Program is open for another week.  For more information, or to submit an application for the Leadership Program, you can get more information on the ODTUG Leadership Program page.

Categories: BI & Warehousing

Announcing the Dodeca Spreadsheet Management System, Version 7 and the Dodeca Excel Add-In for Essbase

Tue, 2016-05-31 14:54
After 18 months of hard work, Applied OLAP is proud to announce the general availability of the Dodeca Spreadsheet Management System, version 7, and the all-new Dodeca Excel Add-In for Essbase.

The Dodeca Spreadsheet Management System provides customers the ability to automate spreadsheet functionality, reducing the risk of spreadsheet errors while increasing productivity.  It combines unprecedented ease-of-use for business users using spreadsheets for planning, budgeting, forecasting, reporting and analysis tasks.  It also provides a robust, programmable development environment enabling companies to create spreadsheet applications tailored to their specific needs.

The new Dodeca Excel Add-In for Essbase was created as a drop-in replacement for the classic Essbase add-in and is the world’s only Excel add-in focused exclusively on Essbase.  The new add-in supports the most common actions used by Essbase traditionalists, supports the corresponding VBA functions, and includes a built-in Excel ribbon.  Early adopters have also been impressed by the speed of retrieving data, commenting they found the Dodeca Excel Add-In as fast as, or even faster, than the classic Excel Add-In for Essbase.  It is supported for Excel 2010, 2013, and 2016 and for Essbase 9.3.1 and higher.




Dodeca 7 includes new features and functionality for security, selectors, logging, and enhanced workbook scripting.

The enhanced security features add the ability to more easily manage users, roles, and permissions to access a Dodeca application.  The new security features include:
  • User Management – You can now track, monitor, and control user access to a Dodeca application and more easily monitor your Dodeca user base.  This feature enables customers to control individual user access to a specific application, enable users for admin access, and manage user mapping to roles while also tracking metrics such as the first, last, and count of user logins.  This feature also logs metrics on the users system to enable Dodeca administrators to more easily support their users.



    Here is an example of the metrics stored for each user record.

  • User Roles – In addition to provisioning roles via Essbase, Microsoft Active Directory, or LDAP groups using Dodeca authentication services, you can now create your own groups directly in Dodeca and map them to users.  In addition, these new roles can be configured to be additive to the roles provided by other authentication services.  
  • Application Session Timeout – You can now prevent users from keeping a Dodeca session open indefinitely by specifying an inactivity timeout or by specifying a designated shutdown time.

The new logging features provide the ability for customers to both easily track what their users are running in the system and assist our support team if and when support is needed.  The new logging features include:
  • View Usage Logging – View usage is now automatically logged in the server.  The logs include not only identifying information for the view and the user, but also include performance information, error information, and tokens used in the view generation.

  • Client-side Request and Response XML Logging – The XML traffic traveling between the client and the server may now be logged to a directory on the client machine.  This logging expands on the Server-side Request and Response XML Logging to make it easier to gather the XML traffic for a single user.  In turn, the XML captured can be used by our support team to easily replicate transactions for our developers if and when necessary.

The selector enhancements include improvements to both view selectors and member selectors.  The improvements include:
  • View Selector Relational Hierarchy Generation – You may now populate the entire View Hierarchy, or alternatively, populate one or more branches, based on the results of a relational query.

  • View Selector Hierarchy Item Access Filters – Previously, you could control the View Hierarchies available to a given user.  This new filter provides the ability to control which view hierarchy items are presented in the view selector based on the current user’s roles.

  • Relational Treeview Point-of-View Selector List– You may now configure a hierarchy of point-of-view items based on the results of a relational query.

  • Enhanced Essbase Treeview Point-of-View Selector List Expansion and Selection Filtering – You can now filter Essbase member content and selectable status based on one or more specified filters including generation number, level number, member name, alias, or shared status.
View editing enhancements make it even easier to create and modify Views in Dodeca:
  • Enhanced SQLPassthroughDataSet Query Editing – You can now define token values to use for testing tokenized SQL queries in the Test Data Set utility.
  • Improved Token Editor – You can now view and edit tokens and their values in an improved grid layout.

Workbook scripting functionality adds more flexibility to Dodeca via 108 events that provide the opportunity for customers to extend Dodeca, 116 configurable methods (actions that can be taken in response to the events), and 138 functions that provide extended information to the workbook script.

The new workbook script functionality includes:
  • New Events
    • BeforeBuildExecute - Allows a workbook script to cancel the build before the view is covered.
    • BeforeRefreshExecute - Allows a workbook script to cancel the refresh before the view is covered.
    • Shown - Raised when the view is initially shown.  The event is raised before the framework applies the view’s AutoBuildOnOpen property, which allows a workbook script to set the property dynamically.
  • New Methods
    • ExportToExcel – Provides the ability to export view and point-of-view information in an exported Excel file.  This information is used to regenerate the view upon Excel file import which enables off-line data entry in Excel with subsequent database updates in Dodeca.
    • SetSelectorConfiguration - Provides the ability to add or remove a point-of-view selector to/from a view dynamically.
  • Enhanced Methods
    • CallWebService – Added a new RESTRequest overload, which allows web service calls to made to RESTful web services.
    • SendEmail - Added a new ServletSMTP overload, which sends email from the Dodeca server rather than from the client.  This is useful in circumstances in which SMTP mail must come from an approved IP address.
    • SetEntry – Added a new Added FormulaArray overload which allows Excel array formulas to be entered programmatically.
    • SetFill - Added a new Clear overload, which clears the fill color and pattern from the specified range.
  • New Functions
    • ColumnWidth - Returns the column width based on the active cell or a given cell.
    • RowHeight - Returns the row height based on the active cell or a given cell.
    • DataPointHasCellNote - Returns a boolean indicating if the active or specified data cell has Essbase LRO cell notes associated with it.
    • IsInCharacterRange - Returns a boolean indicating whether the specified string is limited to the specified character range.  This function can be used to detect multi-byte characters in a string.
  • Enhanced Functions
    • SheetCount - Added an optional IncludeHiddenSheets argument, which controls whether the returned count includes both visible and hidden sheets or only visible sheets.
For more information, we recommend you download and read the latest Dodeca release notes from the registered section of our website.  As always, you may also reach out to us via email or call us at
256.885.4371.

Categories: BI & Warehousing

Next Generation Outline Extractor 2.0.5.1073 released

Wed, 2016-01-06 23:21


In the last week or so, we placed an updated version of the Next Generation Outline Extractor on our website. This version provides support for some updated Essbase versions, including 11.1.2.4.002, 11.1.2.4.003, and 11.1.2.4.005. More importantly, it addresses a bug where alias names were improperly associated with parent members when using the MaxL extraction source.. This bug was reported to us by a number of users and we are glad we were able to address it. Here is a list of the issues that were addressed:

2015.11.23 - Issue 1401 - Resolved an issue where only one alias table is exported when using MaxL as the extract source.

2015.11.23 - Issue 1402 - Resolved an issue where extracts using MaxL input and having members specified with Unicode may print incorrect characters in the output.

2015.11.23 - Issue 1403 - Resolved an issue where aliases and udas may have been improperly placed on parent members.

Please contact our support team if you have any issues.

Categories: BI & Warehousing

Fundamentals of SQL Writeback in Dodeca

Mon, 2015-10-05 22:00
One of the features of Dodeca is read-write functionality to SQL databases.  We often get questions as to how to write data back to a relational database, so I thought I would post a quick blog entry for our customers to reference.

This example will use a simple table structure in SQL Server though the concepts are the same when using Oracle, DB2, and most other relational databases.  The example will use a simple Dodeca connection to a JDBC database.  Here is the Dodeca SQL Connection object used for the connection.

The table I will use for this example was created with the following CREATE TABLE  statement.

CREATE TABLE [dbo].[Test](
[TestID] [int] IDENTITY(1,1) NOT NULL,
[TestCode] [nvarchar](50) NULL,
[TestName] [nvarchar](50) NULL,
  CONSTRAINT [PK_Test] PRIMARY KEY CLUSTERED 
  ([TestID] ASC)
)

First, I used the Dodeca SQL Excel View Wizard to create a simple view in Dodeca to retrieve the data into a spreadsheet.  The view, before setting up writeback capabilities, looks like this.

To make this view writeable, follow these steps.
  1. Add the appropriate SQL insert, update, or delete statements to the Dodeca SQL Passthrough Dataset object.  The values to be replaced in the SQL statement must be specified using the notation @ColumnName where ColumnName is the column name, or column alias, of the column containing the data.
  2. Add the column names of the primary key for the table to the PrimaryKey property of the SQL Passthrough DataSet object.
  3. Depending on the database used, define the column names and their respective JDBC datatypes in the Columns property of the SQL Passthrough Dataset.  This mapping is optional for SQL Server because Dodeca can obtain the required information from the Microsoft JDBC driver, however, the Oracle and DB2 JDBC drivers do not provide this information and it must be entered by the developer.
For insert, update, and delete operations, Dodeca parses the SQL statement to read the parameters that use the @ indicator and creates a JDBC prepared statement to execute the statements.  The prepared statement format is very efficient as it compiles the SQL statement once and then executes it multiple times.  Each inserted row is also passed to the server during the transaction.  The values from each row are then used in conjunction with the prepared statement to perform the operation.

Here is the completed Query definition.


Next, modify the DataSetRanges property of the Dodeca View object and, to enable insert operations, set the AllowAddRow property to True.  Note that if you added update and/or delete SQL to your SQL Passthrough Dataset object, be sure to enable those operations on the worksheet via the AllowDeleteRow and AllowModifyRow properties.

Once this step is complete, you can run the Dodeca View, add a row, and press the Save button to save the record to the relational database.



The insert, update, and delete functionalities using plain SQL statements is limited to operations on a single table.  If you need to do updates on multiple tables, you must use stored procedures to accomplish the functionality.  You can call a stored procedure in Dodeca using syntax similar to the following example:

{call sp_InsertTest(@TestCode, @TestName)}

Dodeca customers can contact support for further information at support@appliedolap.com.
Categories: BI & Warehousing

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