  As modern data pipelines continue to collect, produce, and store a variety of
data formats, extracting and combining value from traditional and context-rich
sources such as strings, text, video, audio, and logs becomes a manual process
where such formats are unsuitable for RDBMS. To tap into the dark data, domain
experts analyze and extract insights and integrate them into the data
repositories. This process can involve out-of-DBMS, ad-hoc analysis, and
processing resulting in ETL, engineering effort, and suboptimal performance.
While AI systems based on ML models can automate the analysis process, they
often further generate context-rich answers. Using multiple sources of truth,
for either training the models or in the form of knowledge bases, further
exacerbates the problem of consolidating the data of interest.
  We envision an analytical engine co-optimized with components that enable
context-rich analysis. Firstly, as the data from different sources or resulting
from model answering cannot be cleaned ahead of time, we propose using online
data integration via model-assisted similarity operations. Secondly, we aim for
a holistic pipeline cost- and rule-based optimization across relational and
model-based operators. Thirdly, with increasingly heterogeneous hardware and
equally heterogeneous workloads ranging from traditional relational analytics
to generative model inference, we envision a system that just-in-time adapts to
the complex analytical query requirements. To solve increasingly complex
analytical problems, ML offers attractive solutions that must be combined with
traditional analytical processing and benefit from decades of database
community research to achieve scalability and performance effortless for the
end user.
