Syncsort
Data integration software company, now part of Precisely
Syncsort was a data integration and data quality software company known for high-performance sorting, ETL, and mainframe-to-cloud data movement tools, before merging with Pitney Bowes' Data business in 2020 to form Precisely. Its products…
Definition
Syncsort was a data integration and data quality software company known for high-performance sorting, ETL, and mainframe-to-cloud data movement tools, before merging with Pitney Bowes' Data business in 2020 to form Precisely. Its products focused on efficiently extracting, transforming, and moving large volumes of data — especially from legacy mainframe and enterprise systems — into modern analytics and cloud platforms, a niche that made it a common presence in large, established enterprises with long-running IBM z/OS and midrange systems.
Overview
Syncsort's history traces back to sort-utility software for mainframes These sorting utilities were among the first third-party tools proven to outperform IBM's own native sort routines on the same hardware, which is what first earned the company a foothold inside conservative mainframe shops., a workload that sounds mundane but was foundational to enterprise batch processing: reordering and merging enormous datasets efficiently was a bottleneck for nightly financial, insurance, and logistics processing jobs. Over decades the company expanded from that sorting core into full ETL and data integration tooling, carrying forward a reputation for engineering products that squeezed maximum throughput out of constrained mainframe compute and I/O. Mechanically, Syncsort's flagship integration products worked by generating optimized execution code for high-volume transformation jobs, often outperforming hand-written COBOL or generic ETL engines on the same mainframe hardware, and by providing connectors that could read mainframe data formats like VSAM and IMS directly rather than requiring an intermediate export step. This let Syncsort tools sit close to legacy systems of record and move data out to analytics platforms without disrupting the mainframe workloads those businesses depended on. Within the integration-tooling landscape, Syncsort occupied a specific niche: general-purpose ETL vendors like Informatica or Talend served broad enterprise integration needs, but Syncsort's differentiation was mainframe and legacy-system expertise, an area many newer, cloud-native tools do not cover well. That specialization made it valuable to industries — banking, insurance, government — that still run substantial mainframe infrastructure decades after the initial migration to open systems began. In practice, organizations used Syncsort tools to offload nightly batch ETL processing from expensive mainframe CPU cycles, to replicate mainframe data into cloud data warehouses for analytics, and to modernize data pipelines gradually without a disruptive full mainframe decommission. This gradual-modernization pattern — keeping the mainframe as system of record while streaming its data outward — was a common enough enterprise strategy that Syncsort built dedicated change-data-capture tooling around it. Data quality and data governance modules were often layered on top of these pipelines to keep the migrated data consistent with source-of-record definitions. The main trade-off, in hindsight, is that Syncsort as an independent brand no longer exists: the 2020 merger with Pitney Bowes' Data business created Precisely, and Syncsort's products were absorbed into Precisely's portfolio. Organizations evaluating this lineage today are effectively evaluating Precisely, and should expect product names and support channels to reflect that combined company rather than the standalone Syncsort brand from before 2020.
Key Features
- High-throughput sort and ETL engines optimized for mainframe workloads
- Native connectors for mainframe data formats like VSAM and IMS
- Data quality and profiling tools layered onto integration pipelines
- Tools for offloading batch processing from costly mainframe CPU cycles
- Support for replicating legacy data into cloud data warehouses
- History of engineering focus on raw processing performance over breadth