Sparkabstract

Sparkabstract is a modular software framework designed to simplify large-scale data analytics through an engine-agnostic, declarative abstraction over data processing. It provides a high-level data model and a collection of optimization passes that can be targeted to multiple distributed execution back-ends, allowing the same analytical logic to run on different runtimes with minimal changes. The project emphasizes a clear separation between analysis definition and execution strategy, aiming to improve portability and performance across diverse environments.

History and development notes indicate that Sparkabstract originated as an academic initiative to explore portability between batch and streaming engines. An initial open-source release followed a collaborative development phase, with contributions from university researchers and industry practitioners. The project operates under an open-source license and relies on community governance to guide feature planning and compatibility standards.

Architecture and features include a front-end that offers bindings for common languages such as Python and Java, and a back-end layer that supports pluggable execution engines. The system includes a cost-based optimizer, rule-based rewrite rules, and a set of abstract operators for filters, joins, aggregations, and data transformations. It supports both batch and streaming workloads, provides connectors to common data sources, and permits user-defined functions. A key design goal is to preserve analytic intent while allowing engine-specific optimizations during compilation to a physical plan.

Usage and ecosystem typically involve defining a logical plan in a declarative API, applying the optimizer, and compiling a physical plan for a chosen back-end. The framework is intended to integrate with data science workflows and machine learning pipelines, emphasizing portability and consistent semantics across engines.

See also: Apache Spark, data processing frameworks, declarative analytics, query optimization.