Linx
Professional low-code backend development for high-performance API and process automation.
High-performance data integration with AI-driven automation for the hybrid cloud.
IBM DataStage is a world-class data integration solution designed for high-performance extraction, transformation, and loading (ETL) across heterogeneous environments. As a core component of the IBM Cloud Pak for Data ecosystem, DataStage 2026 focuses on 'AI-augmented data engineering,' leveraging a containerized parallel processing engine (PX engine) that scales dynamically on OpenShift environments. Its architecture supports both batch and real-time processing, ensuring low-latency delivery for mission-critical analytics. The platform distinguishes itself through its AI-driven 'Auto-Design' capabilities, which suggest optimal data mappings and transformations based on historical metadata. In the 2026 market, DataStage is positioned as the bridge between legacy mainframe systems and modern multi-cloud data fabrics, offering deep integration with Snowflake, Databricks, and AWS Redshift. Its Shift-Left DataOps approach allows for seamless Git-based CI/CD workflows, automated testing, and integrated data quality rules, making it the preferred choice for regulated industries like banking and healthcare that demand rigorous compliance and extreme scalability.
A high-performance engine that uses data pipelining and partitioning to process data across multiple CPU nodes simultaneously.
Professional low-code backend development for high-performance API and process automation.
The industry's first AI-powered, end-to-end data management platform for multi-cloud environments.
Turn natural language into complex, production-ready SQL queries across 100+ data sources.
The spreadsheet with superpowers: Integrated AI, live data connections, and web-app publishing.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Uses machine learning models trained on millions of common mapping patterns to suggest field-level transformations.
Allows users to design flows centrally but execute them on engines located near the data (e.g., in AWS or Azure).
Embedded probabilistic matching and standardization algorithms for data cleansing within the ETL flow.
Integrates with Kubernetes to spin up and down compute pods based on the size of the incoming dataset.
Automatically analyzes a DataStage job and determines if logic should be pushed down (ELT) to the database or kept in the engine (ETL).
Native integration with Bitbucket, GitHub, and GitLab for branching, merging, and versioning of job designs.
Extracting complex EBCDIC data from z/OS systems into a modern AWS S3 data lake.
Registry Updated:2/7/2026
Write to S3 using S3 Connector.
Ensuring physical store inventory matches online availability within seconds.
Providing data to analysts without exposing sensitive customer info.