Pathway Live Data Framework vs Scanopy

Pathway Live Data Framework is a Python framework for building analytics and AI pipelines across stored data and live streams. One pipeline can handle batch processing or streaming ETL, while Differential Dataflow updates results when events arrive out of order. Its Rust processing engine runs Python with multithreading and multiprocessing. AI tooling covers online machine learning, retrieval-augmented generation pipelines, and vector indexes for current data. Documentation lists more than 350 connectors, including SharePoint, and allows custom connectors. Python and SQL programming APIs are available, along with a REST endpoint for serving queries, answers, and real-time features. Deployments can use Docker or Kubernetes and work with OpenTelemetry. Pathway can preserve computation state for recovery. Community is free, self-hosted, and limited to 8 GB RAM and four cores; Scale is free or paid with a license key.

Scanopy discovers and documents network hosts, services, and topology from scans. It produces Physical (L2), Logical (L3), Workloads, and Applications views, then refreshes documentation on a schedule rather than continuously polling. Discovery uses a single daemon on the network, with no agents required on individual endpoints. Listed integrations include Docker, Podman, Instant On, UniFi Controller, gNMI, and SNMP; Instant On and gNMI are marked beta. Depending on the plan, diagrams and data can be exported or shared in formats including PNG, SVG, HTML, PDF, Mermaid, Confluence, and CSV. API access is listed for paid plans, and the API has a limit of 300 requests per minute. The self-hosted, open-source Community Edition has no time restriction and is limited to one network and one seat. Paid plans start at $11.99 a month billed yearly. Scanopy documents assets and connections, but is not a replacement for monitoring device health, bandwidth, or alerts.