What is GlassFlow?
Learn about what is GlassFlow, why GlassFlow and who is GlassFlow for.
GlassFlow offers a code-first development with a fully managed serverless infrastructure to build a streaming data application, deploy, run, and scale it in a production environment.
You can:
GlassFlow does:
Why GlassFlow?
GlassFlow's goal is to become the #1 streaming data transformation solution for Python engineers. We believe in building easy-to-use solutions and, in this way, democratizing access to building streaming pipelines.
Easy install
GlassFlow allows engineers to integrate it into their projects with minimal effort. This means you can start with GlassFlow by simply installing a Python library without a complex initial setup like creating computing clusters or running JVM. You build a custom data processing pipeline and GlassFlow takes care of auto-deployment. Our platform is built using robust technologies like Kubernetes and NATS and scales to your production workloads.
End-to-end in Python
Build end-to-end solutions entirely in the Python ecosystem. GlassFlow can be used out-of-the-box with any existing Python library (like Pandas, NumPy, Scikit Learn, Flask, TensorFlow, etc.) to connect to hundreds of data sources and use the entire ecosystem of data processing libraries.
Advanced data transformation (Coming soon)
GlassFlow does the real-time in-memory stateful transformations of complex event streams. Stateful operations like joins, transformation, windowing, and aggregations allow you to do advanced processing.
Custom functions with real-time API connections
GlassFlow goes beyond basic data streaming functionalities by allowing the integration of custom functions with real-time API connections. It makes stream processing simple from adding real-time context to your AI apps to serving ML models.
Pipeline ready in minutes
GlassFlow can get your data pipeline up and running in just 15 minutes. This simplicity and speed capability ensures that data ingestion, as well as publishing and subscribing to data streams, are not only simplified but also accelerated. You dedicate more time to developing sophisticated data transformation logic and less time to set it up.
Streamlined configuration with YAML (Coming soon)
GlassFlow further simplifies the pipeline creation process through the use of YAML for configuration. By specifying the pipeline configuration in a YAML file, you can easily outline the sources, destinations, and transformation logic of their data streams.
Build once, use it everywhere
GlassFlow allows Python developers and data engineers to build new data pipelines or modernize existing ones without overwriting the code for batch and stream processing workflow.
Facilitates seamless integration with CI/CD workflow
You can automate the deployment process with the GlassFlow Command Line Interface (CLI). Any updates to data pipelines are smoothly transitioned from development to production environments.
Who is GlassFlow for?
Data Engineers
GlassFlow is specifically designed with Data engineers in mind. Its Python-centric approach ensures that engineers can leverage their existing skills to the fullest, building sophisticated data streaming pipelines without the need to learn new languages or technologies.
Data Teams
GlassFlow is dedicated to simplifying the construction and maintenance of streaming data pipelines. More data professionals can work collaboratively on projects without the need for specialized knowledge in the data streaming domain.
Organizations focused on efficiency
GlassFlow empowers your team to construct and manage event-driven data pipelines with ease. Its serverless, production-ready environment means that your team can concentrate on innovation and data transformation, rather than infrastructure management. You will get a data pipeline launch and run with real-time data from day 1.
Where to go from here?
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