# Declarative Data Analytics: a Survey

## TL;DR

This survey reviews declarative data analytics frameworks, focusing on programming models and optimization techniques, highlighting current advancements and open challenges in applying declarative paradigms to data science and machine learning.

## Contribution

It provides a comprehensive overview of declarative data analysis systems, analyzing their programming models, optimization strategies, and identifying gaps for future research.

## Key findings

- Wide range of declarative frameworks analyzed
- Optimization techniques vary across systems
- Open challenges include scalability and expressiveness

## Abstract

The area of declarative data analytics explores the application of the declarative paradigm on data science and machine learning. It proposes declarative languages for expressing data analysis tasks and develops systems which optimize programs written in those languages. The execution engine can be either centralized or distributed, as the declarative paradigm advocates independence from particular physical implementations. The survey explores a wide range of declarative data analysis frameworks by examining both the programming model and the optimization techniques used, in order to provide conclusions on the current state of the art in the area and identify open challenges.