SAP Case Study

How we integrated with SAP in only 2 hours

SAP is one of the most widely used enterprise applications, yet connectivity and integration challenges with other applications can take weeks, if not months, to overcome.

Our German client experienced such challenges when they sought to analyze data from SAP and combine it with other, non-SAP data sources. We connected to SAP, started pulling data within two hours, and delivered a working product in three months.


2 hours
Time to connect with SAP and load data to a data lake
3 months
Time to a working product
Automated data refreshing frequency

About our client

This European manufacturing company generates over $1B in sales, with their products used throughout the world in automotive plants, machine tools, and many other industrial applications. Of our client’s more than 40 subsidiaries, over half use the SAP ecosystem, each independently configured.

A single-point-of-access to data for the sprawling company was an elusive challenge.  Updates were done manually and shared sporadically, and there was no access to a consolidated view of their data or the stories the data wanted to tell.

The client’s goal

The client’s Finance team approached us with these objectives:

  1. Consolidate data from SAP-using entities

  2. Combine that data with data coming from other non-SAP sources in a single, easily-accessible location (a data lake)

  3. Ensure that (interim) results are delivered at speed

Our Solution

Develop a custom data connector that connects with SAP to extract data.

Load extracted data into a data lake as a single point of access and work foundation for data analysts.

Provide a reporting tool with dashboards and detailed consolidated data views.

Our Approach

Agile Methodology
We follow the agile methodology, which allows us to progress quickly and test extensively, as well as nimbly respond to new requirements while ensuring transparency in our actions.
Minimum Viable Product
The approach ensures that we always portion a project into a Minimum Viable Product (MVP). The ensuing releases build off the learnings acquired in the MVP phase.
Delivering in Three Months
We committed to delivering an MVP to the client in three months. The MVP included several data extracts, a data lake implementation, data modeling, and visualization.
Data Connector
The foundation for each deliverable was developing a data connector – a process that makes it possible to extract data from SAP (or any other data source) and write it to its destination (such as a data lake).
Why do I need a data connector?

Data connectors enable the combining of various sources of data into one integrated space. A holistic overview will replace a segmented approach to prevent data silos and poor communication, and to improve quality of insights.

5 benefits of data connectors:
  • Improved decision-making process

  • Increased productivity

  • Streamlined operations

  • Improved customer experience

  • Ability to predict a future

Why should I build my own data connector instead of buying one?

The primary benefits to building your own data infrastructure are the flexibility and ability to retain control over it.

How did we go about writing the connector?

Why connecting to SAP can be so challenging

SAP does not follow existing industry standards. Instead they’ve developed their own, which are not well adapted to third-party systems and are difficult to integrate.

  • Complexity and cost increase with inconsistent configuration: SAP’s greatest benefit is in meeting each business’s specific needs. However, the high level of customization creates complexity in terms of integration, increasing the time and cost of connecting to new systems. Multinational corporations typically have individually customized SAP instances for each region (e.g. North America, Europe, and Asia), and  significant integration challenges ensue. As companies acquire competitors with their SAP instances (or another ERP entirely), the challenges mount.

  • Slow delivery: Developing and delivering software is complicated with SAP’s comprehensive solutions and multiple integration methods, warranting large teams of developers. SAP projects can take months to complete because of their complexity and business-critical nature. In the meantime, companies have to prioritize and carefully allocate resources and must back-up line-of-business requests awaiting prioritization and resourcing, frustrating end-users who need to innovate in real-time.

What are the principles we follow when developing a data connector
  • Utmost flexibility for a deliverable that’s scalable and adaptable. We avoid integrating UI-based tools since they are difficult to automate, extend and maintain long-term.
  • Efficiency. We avoid solutions that require purchasing additional tools due to lengthy adoption processes that could involve multiple stakeholders.
  • Complete transparency. We keep clients informed along every step of our process and share the complete work product. Our clients may use our solutions without us as needed and receive full source code.

Given the lack of industry standards and the client’s extensive SAP customization, our next step was to develop a proprietary data connector which we performed in two stages: The prototype stage and iteration stage.

The prototype stage was completed in just two hours. We built a straightforward product that connects to SAP, extracting and loading data to a data lake daily. It’s built on top of our existing data product repository and follows our nimble development recipes.

We spent the iteration phase adding requested features, completing data veracity checks and ensuring the reliability of the product. This happened while concurrently performing data analytics. 

The final MVP was delivered in three months. The robust and feature-rich analytics solution included infrastructure, code, data models, dashboards and documentation.

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