Real-Time Data Transformation
Use Python functions to filter, enrich though external APIs, format or manipulate data in real-time before consuming with your sink systems.
GlassFlows transformation engine is always ready to process your functions without delay.
You have full flexibility using your Python libraries for your functions and enrich data from external real-time APIs.
Build rule-based functions to reach the best possible results for your transformations.
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The bottleneck
Dependency on other systems even for smaller transformations
Solutions like Apache Flink or Lambda rely heavily on other ingestion system like Kafka or EventBridge. That makes even running smaller transformations jobs complex in terms of architecture management.
Complex debugging and monitoring
Debugging and monitoring real-time pipelines can be challenging due to distributed and asynchronous nature. Identifying issues such as bottlenecks, out-of-order events, or state inconsistencies often requires advanced tooling and expertise.
Steep Learning Curve
The majority of real-time transformation tools require deep understand. For e. g. users of Apache Flink need to understand the APIs, architecture, and advanced concepts like state management and event-time processing. New teams face challenges in ramping up and effectively using Flink for complex use cases.
The solution
In-Built stream ingestions
GlassFlow comes with an in-built event broker. Users don't need to set up or manage the brokers part like replicas, connectors, etc. This approach reduced the tooling users need and gets them quicker up to speed.
Event-based debugging
The monitoring area of GlassFlow brings the option for users to debug the transformations per event. This way users are able to understand the evolution of the event and the impact on possible erros.
Fully managed and serverless executions
The approach of GlassFlow is getting users as quick as possible to run their business logics. That achieved by a managed infra and the execution of the functions are completely serverless.
