Over time, many data scientists or companies will arrive at a stage in data collection where they have amassed vast stores of information and data which can either become too complex or too large to store within their current database management system. Whether the database system no longer meets the storage capacity of the data collected, or the data collected is too diverse for the storage system, one solution which can solve many of these challenges is choosing to make the move from one database to another. However, there can be additional challenges or concerns when moving from one type of database to another. It is important for data science students and professionals to learn more about the process of database migration and the reasons why database migration can become a necessity when working with SQL and/or NoSQL databases.
What is a Database Migration?
The process of moving information and data from one database to another is commonly described as database migration. Database migration requires the use of a service or pipeline to move the data from a centralized system to a new database management or storage system. Then, once the data has been moved from its original system and is safely stored within the new system, the original data is either deleted or made obsolete. Depending on the compatibility of database management systems, this can be a simple and straightforward process or a more complex process, as database migration becomes more complicated depending on the type of database to which information and data is being moved.
There are two types of database migration: homogeneous database migration and heterogeneous database migration. These correspond to the type of database management systems being exchanged during the database migration process.
When enacting a homogeneous database migration, data science professionals are moving data between database management systems of the same type or software ecosystem. For example, moving data from one Microsoft SQL database to another Microsoft SQL database can be described as a homogeneous database migration. In contrast, heterogeneous database migration is the movement of information and data from one database management system to a different type of database. This type of database migration may be viewed as moving between databases that support different data types, such as from SQL to a NoSQL database, or even moving from one type of database system to a system from another company, such as moving from Microsoft to Oracle.
Uses and Benefits of Database Migration
There are several reasons that one might need to move information and data from one place to another through database migration. Primarily, database migration is used to move from one type of database management system to a newer system that is more suited to the needs of the data being stored. This often happens in cases when there is a lack of storage within the previous system, or when there is a need for greater consolidation of databases within a system. The following list includes some of the benefits of these uses of database migration.
Moving from SQL to NoSQL Databases
Today, more companies are beginning to diversify the type of database systems that are being used to collect and store data in response to changes in their data collection. As a data science professional begins to collect different data types, there can also be a need to change the type of database management system being used. SQL databases or relational database management systems rely on structured data. If an individual or institution wants to expand their data collection to include non-structured data then adding a NoSQL database, or moving to a database system that supports different types of data will be a necessity. Moving from SQL to NoSQL can present additional challenges due to the differences between the data schemas for each of these platforms, so it is important to understand which SQL and NoSQL databases are the most compatible for database migration.
Lack of Storage and Increased Scalability
Another difference between SQL and NoSQL databases is the difference in scalability and the long-term storage of information and data. While SQL databases are known for vertical scalability which only allows for a certain amount of storage over time, NoSQL databases are known for their horizontal scalability and capacity to support bigger database systems over time. Many relational database management systems are adopting a horizontally scalable model which addresses the lack of storage that can come with using a more traditional SQL database. Through migrating from a database that is vertically scalable, to a database that allows for the creation of data warehouses, lakes, or some other system of working across multiple databases, data science professionals have the freedom to continue building up storage capacity and processing power.
Database Consolidation
One of the most common reasons for database migration is making the move from a system that might be too costly. Database consolidation is a method of hosting multiple databases within the same infrastructure. For example, if working with Microsoft SQL Server, database consolidation involves hosting multiple databases on the same server, which lets you easily share resources and decrease the cost of managing multiple databases with their own storage needs. Many systems also allow you to host multiple databases within a cluster or in the cloud. By consolidating databases into a less costly and more manageable system, a data science team also has the potential to create and work with new database systems. Whether it be moving from enterprise to open-source, or multiple servers to one cloud-based system, working with different database management systems can assist in the professional growth and development of data science professionals and database administrators through learning how to adapt and develop a new database.
Migrating between Database Management Systems?
For data scientists that are making the move from SQL to NoSQL databases, or simply consolidating their data into one system, it is important to learn more about database management tools, design, and software. Through taking any of Noble Desktop’s SQL courses, data scientists and database administrators can learn more about how to incorporate their knowledge of the SQL programming language into the management of compatible, or vastly different, relational database management systems.
The SQL Bootcamp includes instruction in the foundations of using the SQL programming language with relational database management systems, such as PostgreSQL. The SQL Server Bootcamp offers an essential understanding of Microsoft SQL Server, which is one of the most popular systems for consolidating and migrating from one database to another. Noble Desktop’s NoSQL Databases with MongoDB course also includes an introduction to working with document databases. Each of these courses will teach you essential skills that can expand your knowledge of different database management systems, making any method of database migration or consolidation much easier.