Data Integration Made Easy: GUI-Based ETL & ELT Pipelines
The biggest data leadership problem is managing AI expectations without addressing data inaccessibility and proliferation. Data teams struggle with silos, real-time processing, and quality
Job failures and performance limits increase data integration costs. One-purpose integration tools hinder data pipeline design and implementation that meets SLAs on performance, cost, latency, availability, and quality
Data integration allows you to utilise a simple GUI to create professional extract, transform, load (ETL) or extract, load, transform (ELT) data pipelines for specific use cases
It allows batch or real-time data processing on-premises or in the cloud. Continuous data observability manages monitoring, alerting, and quality issues from one platform
To achieve SLAs for performance, cost, latency, availability, quality, and security, align integration approaches
No matter where the data is located in the data fabric on-premises, in the cloud, or in a hybrid environment ingest data from applications
Create robust and scalable data pipelines using modular, repeatable templates and standardised procedures like DataOps, then scale them up for production
Utilise a single platform to manage all forms of data, including unstructured, semistructured, and structured data
Strengthens AI’s contextual awareness and capabilities, unifies disparate data sources, and drives model training
Deliver dependable and easily assimilated data, identify unexpected data incidents early, and fix them more quickly