Syniti closes the data readiness gap with trusted, validated, and contextualized data for Databricks.
Amid the bold visions for intelligent enterprises at SAP Sapphire Orlando 2025, one truth stood out in every conversation: AI is only as powerful as the data that feeds it. What the most forward-looking leaders now recognize is this — we don’t have a technology gap. We have a data readiness gap. AI initiatives stall not because we lack models or processing power, but because our data still lacks trust, consistency, and context.
Now supporting Databricks as a target, Syniti is setting a new standard. It’s not just a technical milestone. It’s a blueprint for how to scale AI with confidence, powered by business-ready data.
Data lake architectures are central to AI and analytics ecosystems, but simply moving data faster isn't enough. Business leaders need assurance that the data arriving in their lakehouses is not just technically correct, but enriched with meaning — validated at the source, aligned to business rules, and trusted from the start.
By tapping into the SAP Advanced Data Migration and Management by Syniti (SAP ADMM) platform and our methodology, we’re able to support data movement into Databricks, ensuring data is trusted, enriched, and truly business-ready.
We’re not just enabling near real-time data movement between SAP and non-SAP systems; we prepare data for SAP’s Business Data Cloud by making it analytics-ready, enabling customers move to the cloud.
Our approach to harmonizing and cleansing data for AI and analytics initiatives is what accelerates migration and transformation programs and vice versa. Unlike traditional platforms that only clean or enrich data after ingestion — introducing latency, risk, and unnecessary complexity — Syniti’s Data First approach flips this model on its head:
Backed by streaming pipelines that deliver trusted, structured data with precision, businesses can build truly real-time, intelligent operations.
Unlock business-ready data: download the latest eBook and learn how.
Critically, we offer something most data platforms can’t: true business validation. Not just column-level accuracy — but business logic alignment. While other solutions only validate fields, SAP ADMM validates data against business processes so it’s truly analytics-ready. It's what transforms data from a technical resource into a strategic asset.
Combined with our proven methodology, Syniti’s platform fosters collaboration between business and technical users—driving higher data quality and ensuring data is ready for business operations. Tapping into the power of AI, our software-led services approach reinvents the way companies digitally transform.
Business-ready data is the key to unlocking the potential of SAP Business Data Cloud and AI-driven insights. As Juergen Mueller, SAP CTO, recently noted:
“The ability to bring together SAP and non-SAP data in an open, governed, and business-contextualized manner is key to unlocking the full value of AI.”
Our latest milestone closely aligns with SAP’s broader partnership with Databricks, purpose-built for AI, ongoing innovation, and business value.
Aligned with SAP’s open ecosystem, we’re leveraging SAP ADMM’s API framework and metadata exchange to deliver native compatibility with platforms like Joule and SAP Analytics Cloud. It’s the clean data foundation that today’s analytics and AI initiatives demand.
As an SAP platinum partner, we’re enabling:
This isn’t simply about syncing systems. It’s about aligning technology with purpose — creating a data ecosystem where trust is built-in, not bolted on.
At Syniti, we’ve seen firsthand how leaders are rethinking their architectures, their data strategies, and their approach to AI. We’re building a new blueprint for digital transformation: one rooted in real-time data movement, enterprise-grade validation, and intelligent scalability.
We’re not just enabling data movement; we’re enabling data intelligence — from source to insight — and establishing a trusted foundation for AI and business analytics.
Get in touch to learn more about how to build a trusted data foundation for AI-first transformation.
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