Discover how DataLark complements and extends SAP data integration solutions with Python-native integration, hybrid deployment, and predictable pricing.

Cutting Costs and Complexity: A Smarter Path for SAP Data Integration

For decades, SAP has been a trusted leader in enterprise software, providing organizations with powerful platforms to manage data and integration at scale. Platforms such as SAP Data Intelligence (DI), SAP Datasphere (DSP), and SAP Integration Suite have empowered organizations to orchestrate complex data landscapes, govern information flows, and enable business-ready insights.

Yet, enterprise data integration is no longer about moving information from one ERP into a data warehouse. Today, organizations operate in hybrid environments with SAP and non-SAP systems across cloud, on-premise, and edge computing, while also facing pressure to handle real-time data streaming, AI-driven workloads, and cost control.

While SAP solutions remain the foundation of enterprise landscapes, many organizations and partners are looking for complementary approaches — ones that are developer-friendly, cost-predictable, and flexible in deployment.

This is where DataLark comes in: a Python-native integration platform designed to work alongside SAP’s portfolio, reducing complexity for data integration teams and accelerating project delivery without replacing the strategic role of SAP solutions.

The State of SAP Data Integration Today

SAP Data Intelligence (DI) and end-of-life

SAP Data Intelligence provided a strong foundation for connecting structured and unstructured data across systems. It offered Docker-based customization, Python extensibility, and advanced ML integration. However, with DI approaching end-of-life (EOL), many enterprises are exploring complementary tools or transitioning workloads to other SAP solutions.

SAP Datasphere on the rise

SAP Datasphere is increasingly the strategic platform of choice for business-ready data access. Its built-in data federation capabilities allow users to query and combine data from multiple sources in real time without physically moving it, thus reducing ETL complexity, lowering costs, and accelerating time-to-insight for analytics teams. Yet, it is more business-user oriented and less open to deep scripting or custom extensions.

SAP Integration Suite

With strong support for enterprise process automation and secure cloud-to-cloud integration, SAP Integration Suite enables organizations to connect applications and workflows across diverse environments. It is especially useful for event-driven architectures and workflow orchestration. However, it is not intended as a developer-centric scripting environment.

The market challenge

According to IDC's 2024 Global SAP Implementation Services Buyer Perception Survey, over 59% of SAP implementation projects involve integrating SAP products with non‑SAP platforms or solutions. Hybrid IT is the norm, and enterprises need tools that:

• Seamlessly connect SAP protocols with external APIs
• Handle real-time streaming alongside batch integration
• Offer cost predictability in high-volume environments
• Empower developers with scripting flexibility

SAP’s platforms are strong in governance, federation, and strategic alignment. But many organizations find value in specialized tools that complement SAP, especially when developer agility and predictable economics are top priorities.

Where DataLark Adds Value

Designed as a Python-native, SAP-centric data integration solution, DataLark complements SAP’s ecosystem by helping teams accelerate delivery, simplify complex integrations, and extend capabilities across hybrid IT environments.

Developer-centric, with accessibility for business users

At its core, DataLark is designed for developers. The platform provides unrestricted Python scripting and plugin-based extensibility, enabling technical teams to build custom operators, connectors, and transformations without limitations. This flexibility makes DataLark particularly well-suited for complex, large-scale, and highly specific integration scenarios that go beyond what pre-built tools can achieve.

At the same time, DataLark includes an intuitive drag-and-drop interface that allows business users to design simple SAP data pipelines or monitor integrations. While this interface is valuable for quick adoption, the true strength of the platform lies in the advanced customization options available to developers who can take integrations much further.

Case Study: Logistics Company

A logistics provider needed to process shipment documents with barcodes embedded in PDFs. The business team prototyped the pipeline visually, but it was the developers who extended it with a custom Python operator using OpenCV to handle complex barcode recognition.

Impact: By combining ease of use with deep developer flexibility, the team delivered a production-ready solution in two weeks — cutting delivery time in half and eliminating the need for costly third-party services.

Built for SAP connectivity

DataLark is designed with deep SAP protocol support. Out of the box, it supports RFC, BAPI, OData, and SOAP, making it simple to integrate SAP with external systems.

Case Study: Global Manufacturer

A global manufacturer running SAP S/4HANA for operations and Salesforce for CRM needed real-time order synchronization. Standard connectors required additional licensing and offered limited customization.

With DataLark, developers built a Python-based connector to Salesforce in days. Orders were extracted from S/4HANA using BAPI, transformed with Python scripts, and loaded into Salesforce APIs.

Impact: Delivery time reduced by 60%, project costs fell significantly, and the integration team gained flexibility to extend the data pipeline as business requirements evolved.

Hybrid deployment options

Today’s IT landscapes rarely reside in one place. DataLark supports deployment on SAP BTP, AWS, Azure, or on-prem Kubernetes clusters, ensuring organizations can align integration strategy with IT policy.

Case Study: European Retailer

A retailer needed to process sensitive financial data on-premise (due to regulatory requirements) while also streaming marketing campaign data into Azure. With DataLark, pipelines ran seamlessly across both environments.

Impact: The retailer achieved compliance, without duplicating infrastructure, and gained the agility to scale workloads across cloud and on-prem environments.

Predictable economics

Many data integration platforms follow consumption-based pricing, which can become unpredictable as data volumes scale. DataLark uses tiered, fixed annual pricing. This means enterprises know exactly what they will pay, regardless of the number of data pipelines, volume of messages, or throughput handled. The model removes the fear of “bill shock,” allowing organizations to scale projects freely without worrying about hidden costs.

Case Study: Consumer Goods Company

A CPG company piloted an IoT initiative where thousands of connected devices sent usage telemetry back into SAP. Initial integrations using a per-message tool quickly exceeded budget forecasts. By switching to DataLark, the company adopted a fixed-cost model, ensuring predictable integration spend, even as device counts scaled.

Impact: Project costs stabilized, enabling leadership to greenlight full IoT rollout with confidence.

Streaming & real-time data support

DataLark natively supports streaming protocols such as Kafka, MQTT, and WebSockets, enabling organizations to build event-driven pipelines that capture, process, and deliver data continuously. That makes it a perfect fit for enterprises relying on event-driven architectures to respond to business activities the moment they happen.

Case Study: Banking Sector

A bank needed to improve fraud detection by analyzing transactions in near real-time. Using DataLark, they streamed payments via Kafka into SAP BW, where detection models ran continuously.

Impact: Detection latency dropped by 40%, significantly reducing risk exposure.

Customer Benefits: Beyond Technology

While technical features define what a platform can do, the true measure of value lies in the business outcomes it enables. DataLark not only simplifies integration tasks; it also helps organizations achieve strategic goals more effectively.

Faster time-to-value

Integration projects often struggle with delays caused by rigid tools, limited extensibility, or long development cycles. By combining a drag-and-drop interface for simple use cases with unrestricted Python scripting for complex ones, DataLark allows teams to quickly move from design to deployment.

Cost predictability and control

Unpredictable integration costs can derail even well-planned IT initiatives. With consumption-based tools, every increase in message volume, transaction count, or system load may translate into higher bills. DataLark’s tiered, fixed annual pricing model brings financial stability.

Empowering developers and IT teams

Developers are often forced to work around platform limitations, relying on external services or waiting for new connectors. DataLark shifts that dynamic by putting flexibility and control back in the hands of developers. With full Python support, custom operators, and extensibility through plugins, technical teams can handle even niche use cases internally.

Reduced risk and future-proofing

Enterprises face constant change — whether migrating to the cloud, adopting new SaaS tools, or responding to regulatory requirements. Locking in to a single deployment model or rigid licensing structure can introduce risk.

With DataLark, organizations retain flexibility: data pipelines can run on SAP BTP, public cloud providers, or on-premise Kubernetes clusters. This ensures that integrations remain viable, even as IT strategies evolve.

Improved business agility

Ultimately, integration is not just a technical necessity — it’s a business enabler. By reducing complexity, speeding up delivery, and ensuring predictable costs, DataLark allows organizations to respond faster to market opportunities.

• New digital channels can be integrated quickly.
• Partnership ecosystems can be expanded without months of integration overhead.
• Analytics and AI initiatives can consume fresh data streams in near real time.

In short, DataLark helps enterprises become more agile, resilient, and innovation-ready.

How DataLark Fits into the SAP Ecosystem

SAP’s platforms excel in areas that are strategic at the enterprise level — capabilities that DataLark strives to extend. For example:

• SAP Datasphere delivers powerful data federation and semantic modeling, ensuring business users can access harmonized data across the organization.
• SAP Integration Suite is designed for end-to-end process orchestration, connecting applications across the enterprise in line with governance and compliance requirements.
• SAP Business Technology Platform (BTP) provides the broader cloud foundation for innovation, security, and scalability.

DataLark focuses on the technical integration layer — helping developers implement SAP data pipelines, custom connectors, and real-time scenarios more efficiently. When used together, SAP’s governance, federation, and orchestration strengths combine with DataLark’s developer flexibility and cost predictability, giving organizations both strategic control and operational agility.

Conclusion

In today’s fast-paced, hybrid IT landscape, enterprises need more than just powerful platforms — they need solutions that balance strategic governance with hands-on flexibility. SAP’s platforms continue to provide the foundation for enterprise-wide data management, federation, and process orchestration.

DataLark builds on this foundation by extending SAP data integration capabilities where developers and technical teams need them most. With unrestricted Python scripting, real-time streaming, hybrid deployment options, and predictable economics, DataLark gives organizations the agility to innovate quickly while maintaining cost control and alignment with enterprise standards.

The result is a stronger, more resilient integration strategy: one that combines the trust and governance of SAP platforms with the speed and adaptability of DataLark. Together, they enable enterprises to respond faster to market opportunities, unlock the full value of their data, and drive innovation without unnecessary complexity.

Learn more about how DataLark can extend the value of your SAP integration strategy.