IBM DataStage

Build DataStage pipelines entirely by using natural language. Leverage an interactive chatbot to type intent and get started developing pipelines faster and easier than ever before.

Automatic virtualization of data sources for flexible data access.

Support for batch data integration pipelines.

Support for data replication integration patterns.

Transform large volumes of complex data at scale with built-in data transformation capabilities.

A singular design interface allows users to create reusable pipelines and choose runtime style depending on the use case—toggle between ETL/ELT/TETL runtimes without manual recoding.

Support for real-time streaming data integration pipelines.

Data cleansing and enrichment capabilities to improve data quality and usefulness.

Built-in data quality monitoring and validation to help minimize pipeline anomalies and deliver more trustworthy data.

Verifies, organizes and transforms address data with CASS certification, parsing, transliteration, geocoding and reverse geocoding.

The full-featured software development kit (SDK) enables programmatic users to build and maintain pipelines in their language of choice—while preserving the reusability of graphical pipelines and offering the flexibility to switch between code and graphical user interface (GUI).

Automatic management of data specifications and mapping for better data governance.

Integrated observability and lineage tracking to monitor and understand data flows.

Automatic load balancing and elastic scaling capabilities for optimized resource utilization.

In-place upgrades and IBM Cloud Pak services entitlement for seamless updates.

Deploy across hybrid and multicloud environments with robust data integration capabilities.

Separation between a fully managed, cloud-based control panel for designing pipelines and a secure data panel for execution wherever data resides, minimizing egress and ingress, latency and security risks.

ELT Pushdown compiler that optimizes flows by enabling full, partial or no pushdown to enhance performance and reduce data transfer.

A best-in-class parallel processing engine executes jobs concurrently with automatic pipelining that divides data tasks into numerous small, simultaneous operations, enhancing speed, scalability and performance.

Simplify pipeline design to offer no-code, low-code and pro-code options—enabling users of all skill levels to build pipelines and deliver high-quality data.