Moving SAP data into Snowflake is not easy. SAP systems hold some of the most critical business information, but they are also complex, rigid, and not designed for fast analytics. Legacy ETL jobs and fragile custom scripts only add to the problem, slowing projects down, driving up costs, and creating compliance headaches.
CData Sync reduces SAP to Snowflake migration time significantly. The platform provides no-code, secure, and continuous replication that streams SAP data into Snowflake in near real time. By using incremental replication, CData Sync delivers sub-second latency, pushes down processing, and automates incremental updates without forcing teams to build custom scripts or one-off tools.
In this blog, we will explore five practical best practices to help you accelerate your SAP data migration, minimize risk, and prepare your cloud architecture for long-term success.
Define your migration goals and data scope
Clear goals and a defined scope not only keep your migration on track, but they also prevent wasted effort, ballooning costs, and compliance surprises down the road. A little upfront planning will save you weeks of rework later.
Use this checklist to shape your plan:
Identify use cases: Target reporting, predictive analytics, AI/ML, or operational dashboards.
Prioritize tables: Focus on high-value domains like finance, sales, and inventory, and drop non-essential fields.
Set success criteria: Example: cut report latency to under five seconds or load one terabyte of history within 48 hours.
Document compliance: Capture GDPR, SOC 2, or industry mandates and align them with Snowflake governance features.
Defining scope early keeps your migration efficient and ensures it delivers measurable results.
Prepare SAP for external connectivity
Before CData Sync can replicate your data, SAP must be ready to share it securely. Preparing the environment up front avoids connection errors, performance bottlenecks, and compliance gaps once the pipelines go live.
Follow these steps to configure SAP safely:
Enable SAP ODP or RFC endpoints: In SAP NetWeaver, navigate to the Data Provisioning settings and activate Operational Data Provisioning (ODP) or the relevant RFC endpoints. ODP is SAP’s push-based data extraction framework.
Create a dedicated service user: Use a dedicated account with read-only, least-privilege access and enforce the password policies.
Configure network access: Open required ports (such as 3300 for ODP) and whitelist CData Sync IP ranges.
Document terminology: Document ODP as SAP’s native framework for efficient data delivery.
Keep in mind that misconfigured SAP endpoints can degrade system performance during peak business hours. Careful setup ensures stable data replication without putting production workloads at risk.
Select the right SAP‑to‑Snowflake integration approach
SAP-to-Snowflake migration is not one-size-fits-all. Some projects only need a one-time bulk transfer, while others require continuous, near-real-time updates. Most enterprises fall somewhere in between. The table below compares three common approaches and their trade-offs.
Approach | When to Use | Pros | Cons |
Batch ELT | Legacy reporting, low-frequency refresh | Simple setup, minimal SAP load | Stale data, no real-time insights |
Incremental only | Real-time dashboards, AI-driven ops | Near-zero latency, minimal storage | Requires timestamp or counter columns |
Hybrid | Full migration with ongoing analytics | Fast bulk load, continuous freshness | Slightly more complex configuration |
“In most enterprise projects, the hybrid model strikes the right balance between speed and stability.” — CData Solutions Architect
Hybrid pipelines not only keep historical and live data in sync but can also reduce overall migration runtimes by up to 91%. For most SAP-to-Snowflake journeys, it is the clear path forward.
Configure CData Sync for secure, continuous replication
With your SAP environment ready, the next step is to set up CData Sync to move data securely and continuously into Snowflake. These settings ensure your pipelines stay fast, reliable, and compliant.
Key setup steps:
Add a new connection: In CData Sync, create SAP as the source and Snowflake as the destination.
Authenticate securely: Use OAuth or SAML-based SSO through providers like Okta or Azure AD. This satisfies SOC 2 requirements and simplifies access management.
Select tables and columns: Apply the scope you defined earlier. Turn on “auto-detect schema changes” so your pipelines stay resilient as SAP evolves.
Set replication mode: Start with a batch load for historical data, then switch to incremental replication.
Enable encryption: Use TLS 1.2 for data in transit and Snowflake-managed keys for data at rest.
Configure error handling: Set retry attempts to 3, use exponential back-off, and enable email alerts so issues surface quickly.
Note: CData Sync charges by connection, not by row. This keeps costs predictable and removes compliance concerns tied to per-row billing models.
Automated CDC pipelines (or incremental loads) can replicate data up to 3.5× faster than traditional approaches. With the right configuration, you not only secure your data but also gain the performance edge needed for modern analytics.
Refer to our Knowledge Base article to learn more about SAP to Snowflake integration using CData Sync.
Run the initial load with enabled incremental replication
Once you’ve configured CData Sync, it’s time to execute the first end-to-end load. This stage seeds Snowflake with historical SAP data and then transitions seamlessly into incremental updates.
Step-by-step guidance:
Start the replication job: Run the job in CData Sync and monitor Snowflake’s COPY INTO statistics. Aim for a throughput of at least 200 GB per hour to keep the migration on schedule.
Validate row counts: Run COUNT(*) queries on both SAP source tables and Snowflake to compare table counts after the bulk load.
Switch to incremental replication: When the initial load finishes, check that the first incremental batch is captured and applied in Snowflake within five seconds.
Test latency: Insert a test row into an SAP table, then query the corresponding Snowflake table. Latency should align with the incremental schedule you configured.
Document results: Save screenshots of CData Sync logs and Snowflake query history for audit purposes.
Note: Schedule the initial load during off-peak SAP hours. This avoids unnecessary strain on ERP users while ensuring high throughput
Validate, monitor, and scale your pipelines
Keep your SAP-to-Snowflake pipelines reliable as data grows by validating, monitoring, and scaling them.
Validate data: Run nightly checksum (MD5) checks on sample rows.
Monitor health: Use CData Sync’s dashboard and Snowflake’s Query History and Warehouse Load
Set alerts: Flag latency over five seconds, error rates above 0.1%, or SAP CPU spikes.
Scale smartly: Increase Snowflake warehouse size for peaks or run multiple CData Sync instances for parallel streams.
Tune performance: Enable query push-down and parallel paging to ease SAP load.
AI-driven pipelines will soon adjust batch sizes automatically, making performance optimization even simpler.
Frequently asked questions
How do I handle schema changes in SAP tables after the initial load?
Enable CData Sync's "auto‑detect schema changes" option; the connector will add new columns to Snowflake automatically and flag any datatype mismatches for review.
What should I do if replication stops because of a network interruption?
Configure automatic retries with exponential back‑off in CData Sync and set up email alerts; once the network is restored, the connector resumes from the last successfully processed transaction.
Can I use CData Sync for both batch loads and real‑time change data capture (CDC)?
Yes. Configure the incremental replication to run a one‑time bulk import followed by continuous change capture without additional tooling.
How do I ensure GDPR and SOC 2 compliance when moving SAP data to Snowflake?
Use TLS 1.2 encryption for data in transit, Snowflake‑managed keys for at‑rest encryption, and enforce role‑based access controls on both SAP and Snowflake; CData Sync's audit logs provide the traceability needed for compliance audits.
What are the next steps after a successful migration (e.g., AI integration, self‑service analytics)?
Connect Snowflake to your BI tool or AI platform, leverage Snowflake's native Snowpark or external Model Context Protocol (MCP) servers to enable AI‑driven insights and empower business users with self‑service dashboards.
Accelerate your SAP to Snowflake pipeline with CData Sync
SAP-to-Snowflake migration is more critical than ever. CData Sync makes it simpler and safer: define clear goals, prepare SAP for secure connectivity, and run hybrid incremental pipelines that scale with your business. The result is faster insights, predictable costs, and a cloud architecture built for the future.
Sign up for a free trial to start building your SAP to Snowflake integration pipeline today!
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