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An implementation of SPARQL for Elixir
The main features of marcelotto/sparql-ex are: Domain Specific Languages.
Projects with overlapping indexed features include: apache/beam — Apache Beam is a distributed data pipeline framework and unified data processing model designed to handle both bounded… bkirwi/coast — Experiments in Streaming. espertechinc/esper — Esper Complex Event Processing, Streaming SQL and Event Series Analysis. marcelotto/jsonld-ex — An implementation of JSON-LD for Elixir. marcelotto/rdf-ex — An implementation of RDF for Elixir. absinthe-graphql/absinthe — Absinthe is a GraphQL server implementation and query engine for the Elixir ecosystem. It provides the fundamental…
Apache Beam is a distributed data pipeline framework and unified data processing model designed to handle both bounded batch data and unbounded real-time streams. It provides a system for building scalable, data-parallel workflows that operate across compute clusters using a single programming model. The framework utilizes a cross-runner pipeline abstraction that decouples the data processing logic from the underlying execution backend, allowing the same pipeline to run on different distributed compute engines. It supports multi-language pipeline development by translating high-level code fro
Esper Complex Event Processing, Streaming SQL and Event Series Analysis
Absinthe is a GraphQL server implementation and query engine for the Elixir ecosystem. It provides the fundamental toolkit for building GraphQL APIs, including a processing engine that parses, validates, and executes documents against a defined application schema. The project includes a batch resolver to optimize database performance by grouping field requests and a complexity guard to protect system resources by rejecting queries that exceed defined cost thresholds. It also features a schema adapter that translates internal Elixir naming conventions to external GraphQL casing for client comp
