- Deliver trusted, integrated data, in real time or batch, from/to any system
- Simplify and unify data and application integration
- Fuel analytical and operational workloads, and
- Minimize DataOps costs stemming from unreliable data pipelines
According to analyst
Gartner
, Enterprise iPaaS is “foundational for supporting application and data integration, and increasingly used for B2B integration and API management.”?
An iPaaS solution is considered Enterprise (EiPaaS) if it supports projects requiring High Availability/Disaster Recovery (HA/DR), security, and Service-Level Agreements (SLAs).
- Entity-based data integration: When data is ingested, processed, and delivered by business entity, reliability of the data pipeline and of the pipelined data is unprecedented.
- Multiple-use data schemas: An enterprise can create a data product schema once (e.g., customer data product), and then apply it to many different use cases, such as Customer 360, Data Masking, Data Migration, Test Data Management, and more.
- Open, scalable, and modular architecture: Data integration, transformation, preparation, governance, enrichment, and delivery are all accessible on a single, extensible platform.
- Any data integration method: Enterprise iPaaS is designed to support?two-way data integration between sources and targets via any number of data integration methods: APIs, messaging, CDC, streaming, JDBC, and virtualization
- In-flight data masking and cleansing: Data masking and cleansing is performed on the fly, to pipeline fresh, clean, and compliant data into data warehouses and data lakes for analytical purposes, or to consuming operational applications.
Enterprise-grade, cloud-native data pipelining requires a data product approach and platform. The net gains lie in data pipeline performance, reuse, scale, and reliability.
A must-read for enterprise IT...