  With the future striving toward data-centric decision-making, seamless access
to databases is of utmost importance. There is extensive research on creating
an efficient text-to-sql (TEXT2SQL) model to access data from the database.
Using a Natural language is one of the best interfaces that can bridge the gap
between the data and results by accessing the database efficiently, especially
for non-technical users. It will open the doors and create tremendous interest
among users who are well versed in technical skills or not very skilled in
query languages. Even if numerous deep learning-based algorithms are proposed
or studied, there still is very challenging to have a generic model to solve
the data query issues using natural language in a real-work scenario. The
reason is the use of different datasets in different studies, which comes with
its limitations and assumptions. At the same time, we do lack a thorough
understanding of these proposed models and their limitations with the specific
dataset it is trained on. In this paper, we try to present a holistic overview
of 24 recent neural network models studied in the last couple of years,
including their architectures involving convolutional neural networks,
recurrent neural networks, pointer networks, reinforcement learning, generative
models, etc. We also give an overview of the 11 datasets that are widely used
to train the models for TEXT2SQL technologies. We also discuss the future
application possibilities of TEXT2SQL technologies for seamless data queries.
