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FunDa: scalable serverless data analytics and in situ query processing.

Elyes Lounissi1, Suvam Kumar Das2, Ronnit Peter2

  • 1National School of Computer Science (ENSI), University of Manouba, Manouba, Tunisia.

Journal of Big Data
|May 12, 2025
PubMed
Summary
This summary is machine-generated.

We introduce FunDa, a serverless data analytics framework for on-premises environments. FunDa overcomes limitations of current serverless solutions for stateful, long-running tasks like complex analytics and SQL queries.

Keywords:
DaskDBData analyticsFunDaIn situ query processingServerless

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Data Analytics

Background:

  • Serverless cloud computing offers a pay-what-you-use model beneficial for ephemeral tasks.
  • Existing serverless paradigms struggle with stateful, long-running data analytics and query processing.

Purpose of the Study:

  • To propose FunDa, an on-premises serverless data analytics framework.
  • To extend the capabilities of serverless computing for complex data workloads.
  • To enable serverless operations in on-premises and public cloud environments.

Main Methods:

  • Extending the DaskDB system for unified data analytics and in situ SQL query processing.
  • Developing a robust architecture for on-premises serverless data analytics.
  • Evaluating the system on benchmarks with varying scale factors in on-premises and AWS Cloud settings.

Main Results:

  • FunDa demonstrates the ability to support automatic scaling for data analytics workloads.
  • The framework achieves low-latency execution of complex data analytics tasks.
  • Experimental results show increased flexibility for serverless users in on-premises and cloud environments.

Conclusions:

  • FunDa effectively addresses the limitations of traditional serverless computing for stateful, long-running data analytics.
  • The framework provides a viable serverless solution for on-premises data processing.
  • FunDa enhances the flexibility and performance of serverless data analytics in diverse environments.