use cases

layline.io is a multi-purpose platform for high-performance, resilient, distributable data treatment. the concept caters to a lot of existing and future use cases.

data volumes grow exponentially and companies increasingly drive and adopt digital real-time business models. in the course of this transformation they also transform their infrastructure to cloud-native architectures. this creates more demanding event processing requirements and pushes things to the edge (literally). legacy systems are simply not designed to cope with these changes and will eventually all be replaced.

due to layline.io’s generic approach, it satisfies a vast amount of use cases and environments, from simple standalone data transformations to very large distributed real-time event-processing.

the following list provides some guidance for use cases suitable for layline.io, but is by no means comprehensive:

data transformation

transform data from one format into another. apply any sort of data enrichment, filtering and routing. feed into any target.

data filtering

filtering data from data streams based on custom filtering rules. combine with enrichment, routing, and transformation.

data mediation

typical data mediation scenarios, involving elements of data transformation, multi-connectivity, complex custom data formats, massive data volumes.

systems monitoring

feeding systems data from any source in real-time for the purpose of systems monitoring.

etl / elt

big data loading and transformation routines for small to very large and complex scenarios.

cep – complex event processing

typical complex event processing scenarios which require utmost flexibility,  scalability and adaptability.

edge computing

deployment in sophisticated distributed environments. typical for edge computing setups where computing needs to be autonomous and spread over geographies.

stream data processing

real-time streaming data scenarios with multiple distributed ingestion points. sophisticated data treatment and non-stop operation.

batch data processing

traditional batch processing, but in a cloud-native architecture and at much larger scale than possible with legacy systems.