Storing and structuring big data in histological research (vertebrates) using a relational database in SQL

Database systems store data (big data) for various areas dealing with finance (banking, insurance) and are also an essential part of corporate firms. In the field of biology, however, not much attention has been paid to database systems, with the exception of genetics (RNA, DNA) and human protein. Therefore data storage and subsequent implementation is insufficient for this field. The current situation in the field of data use for the assessment of biological relationships and trends is conditioned by constantly changing requirements, while data stored in simple databases used in the field of biology cannot respond operatively to these changes. In the recent period, developments in technology in the field of histology caused an increase in biological information stored in databases with which database technology did not deal. We proposed a new database for histology with designed data types (data format) in database program Microsoft SQL Server Management Studio. In order that the information to support identification of biological trends and regularities is relevant, the data must be provided in real time and in the required format at the strategic, tactical and operational levels. We set the data type according to the needs of our database, we used numeric (smallint,numbers, float), text string (nvarchar, varchar) and date. To select, insert, modify and delete data, we used Structured Query Language (SQL), which is currently the most widely used language in relational databases. Our results represent a new database for information about histology, focusing on histological structures in systems of animals. The structure and relational relations of the histology database will help in analysis of big data, the objective of which was to find relations between histological structures in species and the diversity of habitats in which species live. In addition to big data, the successful estimation of biological relationships and trends also requires the rapid accuracy of scientists who derive key information from the data. A properly functioning database for meta-analyses, data warehousing, and data mining includes, in addition to technological aspects, planning, design, implementation, management, and implementation.