![]() ![]() Data analysis algorithms written in Emma are analyzed and optimized holistically for data-parallel execution on a co-processor engine like Flink or Spark in a macro-based compiler pipeline. ![]() Emma programs can benefit from deep linguistic re-use of native Scala features like for-comprehensions, case-classes, and pattern matching. ![]() Opportunities for optimization are missed out due to hard-coded execution strategies or isolated (per-dataflow) compilation.Įmma offers a declarative API for parallel collection processing.Due to the number of abstraction leaks, program code can be hard to read.Details and subtleties of the target engine execution model must be well understood to write efficient programs.Programs written for distributed execution engines usually suffer from some well-known pitfalls: More information about the project is available at. Emma supports state-of-the-art dataflow engines like Apache Flink and Apache Spark as backend co-processors. Our goal is to improve developer productivity by hiding parallelism aspects behind a high-level, declarative API. Emma is a Scala DSL for scalable data analysis. ![]()
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