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Multi-Engine Data Processing and Federated Workflows with Apache Wayang

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Background

Organizations increasingly have data distributed across multiple systems: databases, data lakes, streams, warehouses and external services.
Traditional pipelines require separate code for each engine, creating fragmentation and duplicated logic.

Objective

Provide a unified way to define data-flow logic once, and allow the execution engine to decide where and how to run it across multiple platforms.

Technology: Apache Wayang

Apache Wayang is a cross-platform processing engine.
A user writes a pipeline once; Wayang:

  • analyzes the logical plan
  • chooses the optimal execution engine(s)
  • executes across supported backends (databases, Spark, Flink etc.)
  • maintains consistency and abstraction

Site:
https://wayang.apache.org/
Blog article:
https://www.novatechflow.com/2025/06/what-are-performance-implications-of.html


Case Study: Federated Multi-Engine Execution

A data flow may need to combine:

  • relational data in PostgreSQL
  • streaming data from Kafka
  • analytical queries on a Spark or Flink backend
  • data-lake writes into Iceberg

Traditionally, this requires multiple pipelines. With Wayang, a single logical plan can be executed across all of them.


Implementation Notes

  • Logical plan translated into WayangPlan.
  • Optimizer selects execution backends based on cost, data size and operator characteristics.
  • Runtime dispatches tasks to the appropriate engine.
  • Provides measurable gains when data lives across multiple platforms.

Benefits

  • Single code path across heterogeneous systems.
  • Better performance through cross-engine optimization.
  • Reduced duplication of data-flow code.
  • Natural integration for federated or distributed datasets.


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