Modernizing SAP IQ Analytics on Azure Linux

A global financial services organization needed to vacate their data center and move a mission-critical SAP IQ analytics environment from aging Solaris/SPARC infrastructure to Linux on Microsoft Azure. The project required more than a basic platform move. The estate included production, disaster recovery, and non-production environments; multi-terabyte datasets; thousands of database objects; DBA jobs; application connectivity dependencies; and a constrained production cutover window. mLogica executed a controlled migration approach that preserved database structures, moved and validated data, configured production readiness, and supported application testing and post-go-live knowledge transfer.

The organization relied on SAP IQ to support analytical workloads in a regulated financial services environment. The existing Solaris/SPARC platform created infrastructure constraints and limited future flexibility, while Azure Linux offered a more scalable and supportable target. The modernization objective was direct: move the IQ estate to Azure Linux, maintain functional continuity, implement production-grade configuration, and reduce execution risk during cutover.

The migration had to balance technical accuracy, operational continuity, and cloud readiness. Solaris/SPARC and Azure x86-64 are not one-for-one infrastructure equivalents, making compute, memory, storage, and network design critical. Large-table extraction and compression created elapsed-time pressure. Data loads exposed constraints around NOT NULL columns, temporary space, access to system views, large VARCHAR handling, and disk capacity. Network throughput also became a practical bottleneck. The team had to migrate the database platform while avoiding unnecessary changes to application behavior, data model semantics, and production operating procedures.

mLogica used a phased migration model aligned to environment criticality. The team reviewed the current IQ estate, finalized the implementation plan, set up Azure Linux targets, extracted schemas and DDL, created target database objects, developed migration scripts, moved and validated data, tested DBA jobs, supported application testing, and prepared production cutover and rollback steps. Production included simplex setup, failover and failback testing, application connectivity checks, DBA and ETL job validation, and a go/no-go control point before release to users. Disaster recovery migration included setup, data movement, validation, log shipping, and application testing support.

One notable point in the engagement was the customer's request for Azure platform recommendations. This was outside the original migration scope, but the mLogica team created practical guidance anyway. The recommendations covered Azure VM classes, vCPU and memory sizing, production and DR alignment, Premium SSD and Premium SSD v2 options, LVM striping for data volumes, host caching considerations, accelerated networking, and the placement of /tmp and /swap on separate devices. The guidance helped the customer make better infrastructure decisions and improved confidence in the target Azure operating model.

Several technical adjustments helped stabilize migration execution. Large static and historical tables were identified ahead of cutover so they could be extracted and loaded earlier where appropriate. Large tables were split into smaller parallel segments using agreed criteria, such as date ranges or application-informed partitions. Indexes were dropped before high-volume loads and recreated afterward. Affected columns were adjusted where load behavior conflicted with source constraints. System-view permissions were granted where needed. Temporary and swap space were separated from the root OS disk, and data disks were designed for striped throughput to reduce I/O bottlenecks.

The project created a cleaner, cloud-ready analytics foundation without forcing a disruptive application rewrite. The organization gained a Linux-based Azure target, a more robust production architecture, database-level encryption planning, validated data migration routines, clearer cutover and rollback procedures, and practical guidance for compute, storage, and network sizing. The team also reduced ambiguity around Azure infrastructure decisions that were initially outside scope. The result was not only a database migration, but a stronger operational blueprint for running SAP IQ workloads on Azure with better visibility into performance, resilience, and supportability.

Enterprise database migrations fail when treated as simple lift-and-shift exercises. For analytical platforms such as SAP IQ, infrastructure design and data movement strategy are inseparable. Plan for architecture translation, not server substitution. Validate with production-like workloads before committing to final sizing. Separate static, historical, and transactional data movement wherever feasible. Document rollback, failover, and job restart procedures before cutover. Finally, be prepared to provide recommendations beyond the written scope if the target platform itself becomes a source of delivery risk.