Databricks and planning or simple inputs

Motivation

Databricks has become an important data platform for companies. This platform has been further strengthened by the announcement of SAP's collaboration with Databricks  : 

Thanks to the strong AI possibilities in Databricks, you can, for example, have larger data automatically suggested for planning purposes.  

But what do you do when you realize that some data is missing – data that you can’t pull from an existing source or that needs to be entered or corrected manually? What options does Databricks offer for this? 

In this blog series, we want to explore exactly these options for planning in Databricks and highlight what is possible. 

In this post, we will start with the question: what exactly is planning and what are the technical requirements for collecting and managing numerical data? Possible use cases range from basic data entries to sophisticated planning applications. 

Not every use case needs all of these requirements. That’s why this initial analysis can help assess the specific needs of your own use case. 

In the following blog posts, we'll share how we were able to implement some of these requirements in Databricks already 

What is planning?

If we want to investigate whether planning is possible with Databricks, we first need to define what planning actually means and what kind of requirements the tool must meet to support it. We’ll soon see that there isn’t a single, universal planning process, and that technical needs vary depending on the business context. We will then take a closer look at specific aspects and present our findings in the upcoming blog posts. 

Here are a few examples of features that may be required depending on the planning scenario.  

Simple data entry

Let's start with data entry, even if this is not technically planning, but rather resembles an “input screen”. Capturing texts and figures that do not come from an existing source already represents a first simple variante of planning. This can, for example, be used for basic control logic during data loading. 

Combining display and entry columns

The entry of planned figures alongside existing actual figures comes closer to the concept of planning, for example when sales figures for various products from the previous year are available and figures for the upcoming year need to be planned. In this case, existing data must be displayed in a table, and planned values must be entered in an additional column. It must be ensured that the planner enters values only in the planning column and cannot modify the others. In the example in Figure 1, the cells to be changed are visually highlighted and clearly distinguishable.

Figure 1: Example of a simple budget planning of products using SAC with the following functions 

1: Copy of actual data
2: Entry on a hierarchy note and splash down to single products
3: Adding of a new line not existing in the actual data

Distribution of plan figures when entering at an aggregated level

In large planning scenarios, values are often required at a very detailed level (e.g. product, month), but the actual data entry takes place at a much higher level (e.g. year and product group). An initial distribution should then be carried out according to certain rules — for example, based on existing actual data or, if that is unavailable, distributed evenly. This initial distribution can then be adjusted by a planner (see Figure 1 – point 1). 

In addition to basic distribution functionality, the question quickly arises as to what should be entered manually and what should be suggested by AI. However, even AI-generated suggestions should still be reviewed by a human, as the AI may not account for all external factors (e.g. planned product launches, new store openings, etc.). A distribution function like this is also required when revising AI-generated data. 

Master data in planning and new lines

If new combinations or rows are to be added during planning, it is essential for consistent planning that only valid master data is used. Therefore, it must be possible to validate entries against master data tables when adding new rows. These tables can also serve as input assistance, helping to avoid typos that could otherwise negatively impact the quality of the planning. 

These master data tables can also contain additional attributes that enable grouping or summarization in reporting and planning — for example, product groups as attributes of the products. 

In some cases, it may be necessary to create new master data during the planning process. In such cases, it must be ensured that these entries are created consistently and can later be correctly linked to actual data. 

Audit-characteristics and undo function

It is often required in planning to trace who changed which figures, when, and how. For this purpose, all changes must be stored along with the user ID and a timestamp. This requires that all changes are saved as deltas only (see Table 1). 

When data is stored this way, it becomes possible to implement a functionality for reverting data changes. 

Tabelle 1: Delta entry with timestamp 

Parallel planning / entries by several users 

In larger planning scenarios, multiple users will likely want to enter data at the same time. To prevent them from overwriting each other’s entries, you can apply the following basic strategies (see Figure 2): 

  • No check at all, save everything 
    If data is rarely changed or only by one user, no restrictions are usually necessary. However, the more frequently multiple users enter data, the greater the risk that they will overwrite each other's values. If all displayed data is always saved regardless of changes, User 1 may unintentionally overwrite values changed by User 2 with outdated values that they themselves haven't touched. The longer a user works on data entry, the higher this risk becomes. 
  • Only save changed data, otherwise no check 
    To reduce the risk of accidental overwriting, it helps to store only the modified data, optionally with a timestamp. This allows multiple users to make changes to different values simultaneously. It also enables change tracking (see Figure 2). In this case, the last person to make a change "wins". 
  • Warning if numbers are changed simultaneously 
    A change is considered simultaneous if it occurs between loading the values into a user's cache and saving them (see Figure 3). A decision must then be made on how to proceed. Otherwise, if only the delta is saved, the resulting value might no longer reflect the input from a single user but rather the sum of all concurrent changes. 
  • Locking 
    The most secure, but also the most complex approach is to lock values as soon as a user opens a specific area for planning. Other users then receive a warning that the planning data is currently locked by User XXX, and changes are not permitted. However, modern applications are increasingly moving away from this classic locking concept. Locks increase implementation effort and are sometimes not properly released when an application is closed incorrectly. This often leads to faulty lock messages that disrupt the planning process. In older SAP BW planning (SAP BPC), planning is based on such a locking concept. In newer SAC-based planning, SAP has abandoned the use of locks. Microsoft, too, no longer locks documents exclusively on a central drive when changes are made. 

Figure 2: Parallel planning - User 1 and User 2 can change and save different values at the same time without a lock. Both changes are taken into account. 

Figure 3: Parallel planning - User 1 and User 2 want to change the same value at the same time. Here it must be clarified how the conflict is resolved. 

Administration and Control of planning

Planning processes require oversight. For recurring planning cycles, such as forecasts, an administrator must define the starting month for the current planning round. All layouts should then automatically adjust accordingly. 

It must also be possible to open and close a planning phase. After the planning period ends, figures should no longer be editable — or, after a review, data may be locked for specific countries, while others might still require updates. 

Versioning

Different versions may be necessary during planning. For example, budget planning might begin with an initial draft created by the respective departments. The overall result is then reviewed, and certain areas may need to revise their input. Alternatively, both an optimistic and a pessimistic scenario can be created. All of this must be managed through separate versions. This requires support through a robust versioning concept.

Functions for initialization / planning preparation

Planning often does not start from scratch but is initially created based on actual data with a simple percentage-based adjustment. Various functions — such as copy functions — are required for this planning preparation (see Figure 1 – point 2). This process may also be further enhanced by using an AI tool that goes beyond basic rule-based adjustments.

Overview of functionalities

Here is an overview of all the functionalities presented: geben wir über alle vorgestellten Funktionalitäten einen Überblick: 

table 2: Overview of functionalities

Next steps

As described, a wide range of requirements is involved in planning — although not every planning use case needs all of them. 

We will therefore analyze the requirements individually and evaluate what can be implemented in Databricks and how much effort is involved. We will share our findings in the upcoming blog posts.  

We are just as curious as you are to see how far we can go with Databricks to meet these requirements. 

Created in July 2025

Contact

Frank Liebrand
Head of Sales
Dr. Ulrich Meseth
Senior Consultant
Burcin Ince
Consultant
Ahmet-Ömer Özgen
Consultant
biX Consulting
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