The 2027 deadline for SAP ECC support isn’t just a technical milestone; it’s forcing a fundamental rethinking of how organizations manage, integrate, and extract value from their SAP data. With 59% of organizations now fully or partially live on S/4HANA, the conversation has shifted from “should we migrate?” to “how do we maximize the value of our modernized SAP landscape?”
The answer increasingly lies not in the ERP system itself, but in what organizations do with the data it generates. Traditional approaches such as expensive custom data warehouses, complex ETL pipelines, and endless data replication are giving way to a new architecture that’s reshaping enterprise data strategy: the Lakehouse.
The Pressure Points Driving SAP Modernization
Organizations migrating to S/4HANA face pressures that go far beyond technical upgrades. The survey highlights three major hurdles: Business process change, customizations, and organizational resistance. Yet beneath these migration challenges lies a deeper issue: SAP systems generate enormous volumes of structured, business-critical data that historically sits locked in silos, isolated from the 90% of enterprise data that’s unstructured or semi-structured.
Finance wants real-time visibility into cash flow. Supply chain teams need to combine ERP data with IoT sensor readings from manufacturing equipment. Marketing demands customer analytics that blend CRM records with social media sentiment. Each department has built its own data extracts, its own analytics tools, and its own version of the truth.
The result? Data teams spend 60-70% of their time on integration and pipeline maintenance rather than generating insights. Costs balloon as organizations maintain multiple analytics platforms, each with its own licensing, infrastructure, and operational overhead. Meanwhile, the promise of AI and machine learning remains frustratingly out of reach because the data required to train models is scattered across incompatible systems.
That’s the challenge most enterprises face as they modernize their SAP landscapes.
The Lakehouse Answer: Unifying SAP and Beyond
Enter the Lakehouse architecture, a data management approach that combines the reliability and performance of data warehouses with the flexibility and scale of data lakes. For SAP customers, this represents a fundamental shift in how enterprise data works.
The traditional model required organizations to extract SAP data, transform it through complex ETL processes, and load it into separate analytics platforms. Each step introduced latency, increased costs, and created opportunities for data quality issues. Operational data from IoT sensors, customer interactions, and external sources lived in entirely different systems, making comprehensive analysis nearly impossible.
The Lakehouse flips this model. Instead of moving data to where analytics happens, it brings analytics capabilities to where data lives, in cost-effective cloud storage, using open table formats like Delta Lake that support both batch and streaming workloads. SAP data and non-SAP data coexist in a unified platform where data engineers, analysts, data scientists, and AI practitioners can all work with the same datasets.
The SAP-Databricks Partnership: Making It Real
In February 2025, SAP announced a landmark partnership with Databricks: the industry’s first SAP-managed version of Databricks embedded within the SAP Business Data Cloud. This partnership addresses the core challenge that’s held back SAP analytics modernization, how to make SAP data accessible for advanced analytics and AI without the heavy lifting of data extraction and integration.
What does this mean for organizations modernizing their SAP landscapes? SAP data becomes immediately available for advanced analytics, machine learning, and AI, without heavy data engineering or complex integrations
Through SAP Business Data Cloud, organizations can access curated SAP data products starting with S/4HANA via zero-copy delta sharing. Instead of extracting and replicating gigabytes or terabytes of data, analytics teams can query SAP data directly where it lives. This eliminates data replication, reduces integration overhead, and enables rapid AI/ML use case deployment on SAP data products.
Integration Patterns Shaping 2026
While the SAP-Databricks partnership provides a native path for SAP Business Data Cloud customers, many organizations need to integrate existing on-premise SAP systems or have specific requirements that demand alternative approaches. The integration landscape has matured significantly, giving organizations multiple paths to Lakehouse adoption.
The Hybrid Reality
Most large enterprises in 2026 are operating in hybrid environments. They have on-premise ECC systems still in production, partial S/4HANA deployments, SAP BW environments with decades of historical data, and cloud-based SaaS applications. The winning integration pattern isn’t an all-or-nothing approach; it’s a pragmatic strategy that meets each data source where it is while building toward a unified Lakehouse architecture.
From Batch to Real-Time
Historical SAP analytics meant nightly batch processes, yesterday’s numbers informing today’s decisions. If 2025 was the year of “batch meets real-time,” then 2026 is the year of streaming-first Lakehouses. Organizations are combining traditional batch ingestion for breadth, bringing in data from multiple SaaS tools, databases, and external sources with streaming pipelines for depth, capturing operational data as it happens.
Finance sees cash positions updated continuously. Supply chain dashboards reflect current inventory levels, not last night’s snapshot. Customer service representatives access complete interaction histories that update in real-time as new touchpoints occur. This shift from batch to streaming doesn’t just make analytics faster, it enables entirely new use cases where immediacy matters.
From Reports to Intelligence
Perhaps the most profound shift is in what organizations do with SAP data once it’s in the Lakehouse. Traditional BI focused on backward-looking reporting: what happened last quarter, how did we perform against budget, which products sold best.
Lakehouse architectures enable forward-looking intelligence. Machine learning models trained on years of SAP transactional data predict which customers are at risk of churn, which suppliers are likely to face delivery delays, which products will see demand spikes. Predictive analytics identify maintenance needs before equipment fails. Recommendation engines suggest optimal pricing strategies based on comprehensive analysis of market conditions, inventory levels, and competitive positioning.
The Syngentic Approach: Making Modernization Real
The platforms are powerful. The integrations are proven. Yet, delivering successful SAP modernization with Lakehouse architectures requires more than licensing software; it requires expertise in both SAP’s unique complexities and modern data architectures.
At Syngentic, our approach combines:
Our technology-agnostic partnerships, our SAP domain expertise, pragmatic implementation, integration excellence, and long-term support from Syngentic.
What 2026 Demands
If 2025 was the year of defining digital ambitions, 2026 will be the year of executing them with precision. Organizations that successfully modernize their SAP analytics architectures will achieve:
- Unified data landscapes where SAP and non-SAP data coexist without complex integration overhead
- Real-time intelligence that enables proactive decision-making rather than reactive reporting
- AI-ready foundations that support everything from predictive analytics to generative AI applications
- Dramatically reduced costs through Lakehouse economics and elimination of duplicative analytics platforms
- Accelerated innovation measured in hours and days rather than months and quarters
Your SAP modernization project is already underway. Your data strategy will either maximize its value or become its next bottleneck. Make the choice that positions your organization for the next decade of innovation.
Syngentic partners with Databricks, SAP, and Cumulocity to help organizations modernize their SAP analytics architectures. Our team brings deep SAP domain expertise and modern data engineering capabilities to deliver pragmatic, high-value implementations. Ready to unlock the full potential of your SAP data? Let’s discuss your modernization strategy.
