Symend migrates from Azure Data Factory to Airbyte, cutting data latency 75% and projecting $900K annual savings
Symend's legacy data infrastructure built on Microsoft Azure Data Factory could not scale with its growing customer base and AI ambitions, suffering from performance, scalability, cost, and reliability problems that limited its ability to leverage data effectively.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Enterprise source data ingestion
Data is ingested from enterprise sources including Microsoft SQL and unstructured data in S3/Blob via easy-to-manage connectors.
Tools used
AirbyteMicrosoft Azure Data FactoryCortex AI
Outcome
Replacing Azure Data Factory with Airbyte eliminated cascading pipeline failures, reduced data refresh latency from 2 hours to as low as 30 minutes — a potential 75% performance improvement — and is projected to save approximately $900,000 annually, while enabling AI-powered scoring models, sentiment analysis, and a self-service chatbot for business users.
What failed first
Azure Data Factory lacked sufficient parallelization, causing cascading failures where a single pipeline failure would halt all subsequent client jobs.
Results
Time savedfrom 2 hours to an hour, and in some cases as low as 30 minutes