  Data-intensive systems handle variable, high volume, and high-velocity data
generated by human and digital devices. Like traditional software,
data-intensive systems are prone to technical debts introduced to cope-up with
the pressure of time and resource constraints on developers. Data-access is a
critical component of data-intensive systems as it determines the overall
performance and functionality of such systems. While data access technical
debts are getting attention from the research community, technical debts
affecting the performance, are not well investigated. Objective: Identify,
categorize, and validate data access performance issues in the context of
NoSQL-based and polyglot persistence data-intensive systems using qualitative
study. Method: We collect issues from NoSQL-based and polyglot persistence
open-source data-intensive systems and identify data access performance issues
using inductive coding and build a taxonomy of the root causes. Then, we
validate the perceived relevance of the newly identified performance issues
using a developer survey.
