SQL is one of the most popular programming languages within data science, and it is known for its capabilities in communicating with a database after a collection of data has been stored. As big data has become more popular within the world of data science and database design, this also means that the management of SQL databases has also become bigger. In contrast to traditional forms of database management, big database management focuses on not just managing one system or database, but working with multiple databases and creating data warehouses, or other forms of distributed processing models. Using SQL for big database management allows data scientists and database administrators to learn more about how to manage and store big data through the use of multiple databases, software, and servers.
How is SQL Used for Database Management?
Before we discuss big database management, we must first define database management. Database management can be simply defined as the routine maintenance of previously established or newly created databases in order to ensure ease of access, as well as the safety and security of the data being stored. While there are many types of databases, SQL databases are one of the most popular systems within the world of data science and database management. In contrast to other database management systems, which are primarily used when working with complex or non-normative data, SQL is primarily used to communicate with structured data and more traditional databases.
SQL’s role within the realm of data science prioritizes engagement with SQL databases or relational database management systems. Relational database management systems are structured in a rows and columns format which is very similar to a spreadsheet in their design and data types. When using SQL for database management, data science students and professionals are not only able to learn more about the SQL programming language but also database design, as SQL is primarily used to analyze and manage data.
This means that the SQL programming language is an essential data science too l as SQL databases are especially useful for cleaning and organizing data, and there are many programs that also specialize in the analysis and visualization of data within, and outside of, a SQL database. With that being said, many SQL databases offer several challenges when it comes to the storage of high-volume data.
When data is being collected within a SQL database, obtaining more storage requires vertical scalability which limits the amount of additional storage that the system can handle. SQL database management is generally capped after a certain amount of data is collected, meaning that one SQL database cannot collect more information than the system can handle. This can be especially difficult if the information and data being collected by a system is quickly amassed. SQL databases tend to rely on structured data and many companies collect unstructured, forms of data that might not be compatible with these databases.
SQL Database Management vs. Big Database Management
Due to the limitations of SQL databases that operate on a model of vertical scalability and structured data, there has been a shift in database management which focuses on systems that allow for more horizontal scalability and diversity in joining different data types and databases together. In contrast to vertical scalability, horizontal scalability allows a data scientist or database administrator to spread data across multiple compatible database management systems. Big database management relies on the use of new technologies which can build nodal networks of databases that link multiple machines together to store a greater volume of data.
This is primarily accomplished through the use of cloud-based computing systems and distributed processing models. Instead of storing data on a single physical server or machine which requires a constant increase in storage, cloud computing allows data professionals to store information and data in the cloud, so that it is not confined to one system. This ensures that data is accessible to multiple people through several systems, such as virtual machines or environments. Cloud computing creates a network or distributed system in which multiple systems are able to communicate with each other from their respective machines. For a database system, this means that several databases can have access to the same data at the same time, making it faster to query data when working on a data science team or within a large company or big data project.
While database management tends to focus on managing a single database at a time, big database management tends to require the management of multiple databases by several team members all at the same time. Through working with multiple databases and team members, an organization or business is able to share the load of working with big data. This also ensures that the process of returning queries is faster and more efficient because one system is not being overburdened with carrying all of the data in the system.
Big Database Management Systems and Tools
When working with SQL databases, there are several database management tools that specialize in big database management. Many SQL databases now allow for the creation of data warehouses. Data warehouses allow data scientists or database administrators to connect multiple databases to each other, through a series of nodes. This ensures that multiple machines are sharing the load of a big database, and also makes it easier to add on new machines if more storage or processing power is required. Some of the database management systems which allow for the creation of data warehouses include, but are not limited to, Microsoft SQL Server and Azure, as well as PostgreSQL and the use of data lakes in IBM’s database management systems.
Another aspect of big database management is the ability to work with multiple types of data within the same system. Instead of engaging with data that has been cleaned and organized for uniformity, many big database management systems allow data professionals to work with data in its raw form to maintain the integrity of the data across databases. This also means that data science professionals who want to learn more about big database management must also adopt new methods of classifying data that make it easy to understand and navigate a dataset that may be more complex and multivariate than usual. Big database management not only focuses on creating new technological tools to manage information and data, but also new systems of data stewardship and governance.
Interested in SQL for Database Management?
Whether you are interested in database management or data science, learning the SQL programming language gives you the skills to communicate with databases, and any instruction in SQL is generally paired with training in relational databases which were created to interact with the structured querying language. Each of Noble Desktop’s SQL courses not only focuses on learning SQL but applies instruction in SQL to working with specific database management systems. The SQL Bootcamp includes an introduction to the language as well as engagement with the PostgreSQL database. The SQL Server Bootcamp includes an in-depth exploration of using the SQL programming language with Microsoft's SQL Server database management system. Any student or professional that is interested in pursuing a career as a data scientist or database administrator can expand their knowledge of SQL with one of Noble Desktop’s courses or bootcamps.