There are multiple database management tools for data scientists that can be used for searching and storing information and data. Within the world of data science and database design, these tools tend to utilize structured data and the SQL programming language, because relational database management systems (RDBMs) are one of the most commonly used repositories for storing and managing datasets.
While many data scientists specialize in a specific RDBM, it is quite common to use different database systems or to migrate from one database to another, as well as moving a dataset from one system to another. It is useful for data science students and professionals to have a knowledge of the compatibility between relational database management systems and how that compatibility can benefit them when working on a data science project and database design.
What are Relational Database Management Systems?
Many times, relational database management systems (RDBMs) are synonymous with the term SQL databases or relational databases. This is because RDBMs are known for their utilization of the SQL programming language to communicate with a structured dataset that is stored within a relational database. Structured datasets are stored relationally when information and data is organized within the format of rows, columns, and tables. This format is a hallmark of relational databases and allows data science students and professionals to make comparisons between the rows and columns. These comparisons are usually returned through statistical analysis and modeling which shows the unique relationships within the dataset, as well as to make predictions based on the patterns that those analyses bring forth.
Defining Compatibility in Relational Databases
Due to their reliance on the SQL programming language and the structure of relational databases, relational database management systems can be defined as compatible when there is an ease of transferability, communication, and connection between the systems. Ease of transferability in relational databases is seen when it is simple or efficient for a data scientist to transfer or migrate their data from one system to another. Consequently, many relational databases have created specific pipelines and protocols to make the process of moving data from one database to another as straightforward as the initial importation of data.
Ease of communication is also seen when you can use the same language(s) to work within the databases, and/or if the databases are engineered to communicate with each other. Due to the fact that relational databases primarily rely on SQL, this communication is somewhat of a given, but some relational databases have their own methods of communication. Finally, ease of connection can also demonstrate the compatibility of relational databases within the same company or product ecosystem. Companies like Oracle and Microsoft offer multiple database management systems. These tools are always already connected due to their existence in the same family of software and data science tools.
Most Compatible Relational Database Management Systems
While there are dozens of relational database management systems available, and many of which are compatible with each other, the following list includes some of the most compatible RDBMs as well as the unique features which allow for this compatibility between the systems.
PostgreSQL and SQL Server
There are many similarities between PostgreSQL and SQL Server, as each of these products is accessible to data science students and professionals. Both PostgreSQL and Microsoft SQL Server are well-known and commonly used database management systems that both operate using the SQL programming language (although Microsoft SQL Server uses its own unique version of the SQL syntax called T-SQL).
Due to their popularity, the compatibility of these databases with each other is primarily seen in features such as Microsoft SQL Servers Linked Server can be used to connect to PostgreSQL. Linked servers allow data science professionals to connect one database management system to another so that it is possible to query data from multiple sources. Learning one of these databases is useful when working with the other, so migrating from one database to the other should be easier due to the compatibility between the two.
Amazon Aurora and MySQL
One of the hallmarks of modern-day database management is the creation of database systems that combine multiple databases over several machines and servers. While some products allow for this type of combinatory and high-level scalability, many SQL databases are only capable of vertical scalability within the same system. In contrast to these more traditional systems, Amazon Aurora offers migration support and compatibility with SQL databases.
Amazon Aurora is compatible with MySQL and PostgreSQL, harnessing the cloud-based, open-source capabilities of these database systems. This compatibility also allows for faster processing speeds, even as the amount of data storage is increased, and is an example of how databases can work together by utilizing the best features of multiple products to create an even better data storage system.
Oracle Database and IBM DB2
As Enterprise database management systems, the compatibility between Oracle Database and IBM’s DB2 addresses many of the concerns that come with ensuring the safety and security of a dataset when it is being migrated from one storage system to another. Both Oracle Database and IBM DB2 have similarities in terms of their schemas and data types. This makes it very easy to migrate data between the systems, and many companies that are working towards database consolidation or moving from one system to another can trust in the compatibility of this enterprise software.
Need to learn more about Relational Database Management Systems?
Since there are so many relational database management systems available to data science professionals, there are many similarities, overlaps, and compatibility between them. Noble Desktop’s SQL courses offer more general instruction in the SQL programming language and the relational databases which are reflected in theirs. The SQL Bootcamp explores the SQL programming language, relational databases, querying, and manipulating data tables. In addition, the SQL Server Bootcamp outlines the uses and benefits of Microsoft SQL Server. As part of the Microsoft ecosystem of data science tools, knowledge of SQL Server is an excellent introduction to a host of highly compatible relational database management systems and NoSQL databases.