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liquidlabsio

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Funding Links: https://github.com/sponsors/liquidlabsio

GitHub Sponsors Profile

Liquidlabs specialise in realtime distributed systems for the cloud. We believe the next generation of event-driven serverless technology has some significant gaps. Our unique advantage is that we have been working in this space for the last 3 years, with some of the worlds largest companies.
My name is Neil Avery, I'm the founder of Liquidlabs and based in London. I've been the CTO of several companies, worked and founded startups, and love technology.
Project Fluidity is our focus.
It is a streaming observability platform. It allows anyone building real-time microservices, event-streaming, streaming etc or distributed system the ability to visualize the performance. While that doesn't sound very exciting - it is ;) - this is because it breaks down the 3 pillars of observability (metrics, logs and traces) - to a unified view to provide a completely new experience. The approach is to combine all of the source data together to present the streams as flows (transactions, correlations) so a system processing thousands of transactions can easily be visualized. The visualisation works with scaled out metrics showing throughput, latency and overlay data (i.e. state, error etc). From there, trace-like views are available for hot-spotted metrics. The trace level view not only traces remote processes, but also operations within the same process space. These traces can be filtered and categorised, to the point of single trace identification. At this point, all points in the trace can be introspected to show service operation, as well as the log-data that captures the processing.
Why is this different? (user)
Because other cool systems - like honeycomb.io, lightstep and others dont successfully correlated the 3 pillars - they leave that to the user. Fluidity combines the pillars in way that allows them to be overlaid. At the 50k foot view, error or failing dataflows can be filtered and identified then drilled into.
Why is this different? (technical)
The technology is based on the serverless stack. The AWS release uses AWSLambda's, S3 and dynamoDB. The user of the system will self-service it into their cloud premise. Why does this matter? Because the user has complete control over data sensitivity and price. There are also no security concerns because data remains in their tenant - and - access to Fluidity is controlled by them (their admin). Severless technology allows us to cheat scaling distributed systems in an easy way - low complexity, and extremely low cost.
Competing vendors have to run and operate their own cloud solution, ship data and charge the customer for the cost of all of it; SaaS is a race to the bottom. Fluidity doesn't have that problem.
Why is their sponsorship important? How will you use the funds?
Funding will be used to complete the project and build the community. We want the community the thrive. Users should be able to bring their own models, build ML analytics models, integrate with ChatOps, git and more. We also want to take Fluidity to GCP and Azure. Any sponsorship will be appreciated!