Database CDC
With our built-in connectors, you can capture new data from your databases, such as Postgres, and deliver those changes in real-time with your pipelines.
Real-time processing of metrics with sub-second latency. No batch processing or delays.
GlassFlow comes with logs and monitoring so you get alerted when an update occurred an error.
Every event is stored in our dead letter queue and can be reprocessed even 30 days later.
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The bottleneck
Schema Evolutions that cause pipeline breaks
Changes in source schema, such as added fields or altered data types, can break downstream pipelines.
Unscalability
Processing large volumes of changes in near real-time without increasing latency can strain resources.
Missed manipulation of data
Applying transformations to data on the fly while maintaining low latency and accuracy can be difficult. Even for smaller manipulations like cleaning, filtering, etc.
The solution
In-built data schema checks
GlassFlow in-built data schema check will notify you immediately when a change happens and lets you decide how you want to reprocess the data.
6 Mio events per sec
GlassFlow is built with technology that is highly scalable and can process millions of events within seconds.
Data Transformation in Real-Time
You have full flexibility using your Python libraries for your functions and enrich data from external real-time APIs while keeping real-time processing latency.
