SAP HANA migration: challenges, methods, and best practices
Our data engineer talks about SAP HANA migration and the steps they follow for a successful migration, including post migration steps. That’s not it, all traditional challenges and way to encounter it has included as well.
Suresh
Oct 1, 2025 |
10 mins
SAP HANA migration is usually performed to move old SAP databases or third-party systems into the in-memory, high-performance SAP HANA platform. For data teams, this isn’t just an upgrade—it’s about enabling faster queries, real-time analytics, and integrating structured + unstructured data in one place.
Why it’s needed:
Performance – traditional systems struggle with large datasets; HANA’s in-memory processing slashes query times.
Consolidation – unify multiple data silos into a single source of truth.
Future readiness – SAP is phasing out legacy platforms, making HANA the foundation for S/4HANA and advanced analytics.
Better insights – empowers data teams with predictive analytics, AI integrations, and scalable reporting.
Step by step guide for SAP HANA migration
Pre migration – Planning, migration approach, data preparation
What happens here: you inventory the landscape, pick the migration path, size HANA correctly, shrink what you don’t need, and align on downtime and success criteria. Concretely: catalog every SAP system, database size, growth rate, integrations, batch windows, SLAs, and “can’t-break” reports. Run SAP’s Readiness/Maintenance planning (it flags add-ons, deprecated apps, and custom code hotspots), and agree on brownfield vs. greenfield and whether you’ll combine update + DB move using SUM’s Database Migration Option (DMO). Do early sizing for memory/CPU/IOPS, archive cold tables to cut volume, and pick your cutover strategy (classic downtime, near-zero with nZDM/ZDO, or dual-run with HANA System Replication). Lock a data-reconciliation plan (row counts, checksums, key financial checks) so nobody argues post-go-live.
Category | Examples to capture | Source | Why it matters |
|---|---|---|---|
System inventory | SID, modules, versions, add-ons | Solution Manager / system docs | Readiness & compatibility |
Data volumes & growth | Total GB/TB, top tables, daily delta | DB stats | Sizing & archiving scope |
Performance baselines | Peak CPU/mem/IOPS, batch duration | OS/DB monitors | Hardware & window planning |
Integrations | Up/ downstream systems, interfaces | PI/PO, CPI, interface lists | Cutover & test coverage |
Downtime constraints | Max outage, blackout dates | Business owners | Choose cutover method |
Compliance & HA/DR | RTO/RPO, audit needs | Security/Infra | Pick HSR, backup policy |
Technical migration — Execute, minimize downtime, test
What happens here: you run the move using SUM/DMO (often “one tool, one process, one downtime”), optionally pair it with DMO “without update,” or DMO with System Move, and choose a downtime-saving method: near-Zero Downtime Maintenance (nZDM) and/or Zero Downtime Option (ZDO) where supported. For very tight windows, stage a secondary HANA with HANA System Replication (HSR) and switch over at cutover time. If you have non-SAP feeds to HANA, line up Smart Data Integration (SDI)/Smart Data Access (SDA) or SLT for change capture. During runs, follow SAP’s pre/post consistency checks (FI/CO, master-data health), keep backups and log backups current, and track progress in SUM. Once the migration completes, reconcile data (row counts, aggregates), run smoke tests, interface pings, and performance sanity checks before you open the system.
Downtime-reduction playbook (pick what fits your SLA):
Standard SUM/DMO: simplest; one guided downtime window.
nZDM (near-Zero Downtime Maintenance): reduces technical downtime with SUM innovations.
ZDO (Zero Downtime Option for S/4HANA updates): apply updates/upgrade with no technical downtime; plan extra rehearsal.
HSR-assisted cutover: replicate to a secondary HANA, switch over quickly at go-live.
Post-migration — Optimize, govern, monitor & improve
What happens here: you tune for HANA’s columnar engine (compression, partitions, delta merges), analyze expensive SQL and adjust indexes/calculated views, and right-size memory/CPU after real workloads appear. Turn on governance: roles/privileges, encryption at rest/in transit, auditing, backup/restore cadence, and DR via HSR—including documented failover tests. Wire monitoring (HANA Cockpit/SolMan) with alerts for memory growth, long-running statements, log/backup space, replication lag. Close the loop with business: validate key reports, close reconciliation, and schedule a “stabilization” sprint to fix queries and loads that were fine pre-migration but now have different plans on HANA. Iterate—most teams realize 20–40% extra headroom after two or three tuning cycles.
Topic | Default, SAP-recommended tools |
|---|---|
Combine update + DB move | SUM with DMO (in-place or System Move) (support.sap.com) |
Near/zero downtime | nZDM in SUM, ZDO for S/4HANA updates (SAP Help) |
HA/DR & fast cutover | HANA System Replication (HSR) (SAP Help) |
Ongoing ingestion | SDI/SDA for integration/federation; SLT for CDC (SAP Help) |
Readiness | Readiness Check / Maintenance Planner (SAP Help) |
SAP HANA migration checklist and timelines
We have built a one-page project plan for SAP HANA migration, designed to give project managers, SAP Basis teams, data engineers, and business owners a shared view of what needs to happen at each stage. It takes the migration journey — from planning to execution to post-migration optimization — and breaks it down into timelines, owners, and checkpoints.
Phase | Timeline | Owners | Checkpoints |
|---|---|---|---|
Pre-Migration (Planning & Preparation) | 4–6 weeks | Project Manager, SAP Basis, Data Team, Business Owners | - Run SAP Readiness Check - Size HANA infrastructure - Archive cold data - Decide cutover approach (DMO, nZDM, ZDO, HSR) - Define reconciliation plan |
Technical Migration (Execution & Testing) | 2–3 weeks | SAP Basis, Infrastructure Team, Data Engineers, Functional Leads | - Rehearse SUM/DMO end-to-end - Backups & log backups ready - Run consistency checks (FI/CO, Master Data) - Test integrations/interfaces - Perform data reconciliation |
Post-Migration (Optimization & Governance) | 2–4 weeks (stabilization) | Basis Team, Security, Data Engineers, Business SMEs | - Tune SQL/partitions - Validate HA/DR (HSR failover test) - Implement monitoring & alerts - Validate business-critical reports - Document runbook & handover to Ops |
Talking about SAP HANA migration timelines, it vary from one company to another.
For Small companies (single SAP system, low data volume, few integrations), the typical timeline is 8 to 12 weeks.
For mid-sized companies with multiple SAP modules, moderate data volumes, 3rd-party integrations, the time taken might be 3 to 6 months.
For large enterprises with global, high data volume, multiple regions, 100s of interfaces, SAP HANA migration can take 6 to 12 months and even longer.
For highly regulated industries (banking, pharma, healthcare), SAP HANA migration can take 12 to 18 months.
How is SAP HANA different from traditional databases?
Traditional databases are like filing cabinets: data is stored on disk, and every time you need it, you go fetch it. SAP HANA is like a high-speed digital whiteboard: all the data is already on the board (in memory), ready to be queried instantly, and optimized for both recording new info (transactions) and analyzing it (analytics).
Aspect | Traditional Databases | SAP HANA |
|---|---|---|
Architecture | Disk-based, row-oriented storage. Data is read from disk into memory when queried. | In-memory, column-oriented storage. Data stays in RAM, optimized for fast reads/writes and analytics. |
Performance | Slower query times (I/O bottleneck). Good for transactional workloads but limited for analytics at scale. | Real-time or near real-time analytics; massively reduces query times from hours to seconds. |
Data Processing | Typically separates OLTP (transactions) and OLAP (analytics), requiring ETL and data warehouses. | Unified platform supporting both OLTP + OLAP on the same system (“hybrid transactions + analytics processing”). |
Scalability | Scaling usually requires complex sharding, indexing, and tuning. | Scales horizontally and vertically with simpler scaling logic, leveraging in-memory compression and partitioning. |
Use Cases | Banking systems, ERP, CRM — mostly transactional with nightly/weekly reporting. | Predictive analytics, AI/ML integration, real-time dashboards, IoT data ingestion, advanced ERP (S/4HANA). |
Cost Implications | Lower hardware cost (disk-based) but higher overhead for ETL, warehousing, and maintenance. | Higher upfront infrastructure cost (RAM-heavy), but reduced need for ETL, separate warehouses, and faster ROI from real-time insights. |
Final thoughts
Migrating to SAP HANA isn’t just a technical upgrade; it’s a business transformation. Traditional databases limit how fast and how deeply companies can analyze data. With SAP HANA, you unlock:
Real-time insights instead of waiting hours or days for reports.
Simpler architecture by combining transactions and analytics in one platform.
Scalability and future readiness as SAP phases out older systems and moves toward S/4HANA.
Competitive edge through predictive analytics, AI, and advanced reporting.
But migration is complex — downtime, data volumes, integrations, and compliance make it a high-stakes project. That’s where we come in. Our team blends deep SAP expertise with modern data engineering practices, helping you: plan migration with precision, execute with minimal precision, and optimize post-migration for performance, governance, and monitoring.
by Suresh
Suresh, the data architect at datakulture, is our senior solution architect and data engineering lead, who brings over 9 years of deep expertise in designing and delivering data warehouse and engineering solutions. He is also a Certified Fabric Analytics Engineer Associate, who plays a major role in making us one of the early adopters of Fabric. He writes in words whatever he delivers with precision to his clients, consistently voicing out trends and recent happenings in the data engineering sector.
