SQL vs. NoSQL: Data Warehouses

The centrality of big data within the field of data science has led to several changes in the methods of collecting and storing information and data. In comparison to traditional methods of database management, which focus on storing a uniform collection of data within a single database, big data systems tend to require a greater amount of complexity in the storage of information and data.

Data warehouses allow data science professionals to organize several relational databases under one linked system, all at the same time. Data lakes allow data science professionals to collect and manage big data projects which may rely on different data types such as structured and unstructured data. Many data science professionals benefit from learning more about who to use database management tools in the creation of data warehouses and data lakes.

What is a Data Warehouse?

A data warehouseis a model of database architecture which allows data analysts to optimize the use of relational database management systems. Data warehouses are commonly seen within the business and finance industries, and this model is highly compatible with SQL systems, by relying on schemas that are formatted for structured datasets. In this sense, data warehouses prioritize SQL databases and are generally incompatible with NoSQL databases. A data warehouse is most commonly used when working with business intelligence (BI) tools and analytics which offer insights on a relational database.

How to Create a Data Warehouse

There are several steps to setting up a data warehouse, which includes first identifying the type of database management system that you are interested in using and then identifying the different types of databases that will be linked within the data warehouse.

Identify a SQL Database Management System

The first step in creating a data warehouse is identifying the type of database management system that you want to work with. While some systems specialize in the creation of data warehouses, other systems are more closed, so it is important to keep the capabilities of your system in mind. Cloud-based database management systems like Amazon Redshift tend to work well with a data warehouse model because the format of the system is geared towards a connection between data storage systems as well as the mobility of data across those systems.

Identify Nodes and Link Databases

Once you have established the products or system that you want to use, you should build out the architecture of your data warehouse system by identifying which databases will connect with each other. Each of these databases will then act as a node in your data warehouse, therefore it is useful to create a plan of what type of data will be housed in which database and how information and data will be organized within the database(s). At this stage, it is also useful to create a model, chart, or some other visual representation of the nodes in your data warehouse, what types of data each node includes, and how the different databases will connect and communicate with each other.

Manage the Databases

Due to the fact that the primary purpose of creating a data warehouse is the inclusion and connection of multiple databases, a plan for the long-term management and governance of the system is especially important. This plan should include criteria for when, and for what reason, an additional database should be included in the warehouse. Itshould also include instruction on how different users should engage with the data warehouse such as how data should be moved or manipulated. Once the data warehouse is established, and you have connected the different nodes (or databases) to each other, the databases will need to be managed by a data science professional or Database Administrator, and these different plans and systems will make it easier for multiple data scientists or administrators to share data and manage the databases.

From Data Warehouses to Data Lakes

While many data warehouses include the linking of databases that focus on the same data types, there are also times in which you may be interested in combining both structured and unstructured data or SQL and NoSQL databases. Although not every database management system is compatible with another, for those that are, combining different data types allows for more complex and diverse database systems. By creating a repository with several data types under its auspices, these diverse systems are best used when working on big data projects, especially when it comes to the collection of user data and engagement.

Data lakes serve as the primary method of creating a system that combines data types. Offering some of the best features of both SQL and NoSQL databases, data lakes combines both structured and unstructured data, because there is no set data schema for the data lake. Data lakes also allow data science professionals to work with different types of user data such as online transactions as well as the data of mobile applications. Some of the most popular systems that are capable of utilizing data lakes include IBM’s DB2, Oracle’s Autonomous Data Warehouse, and Microsoft’s Azure. Each of these products makes it easier for data scientists and developers to employ machine learning models, predictive analytics, and other forms of exploratory data analysis.

Interested in Learning More About Data Warehouses?

There are several database management systems that specialize in the creation of data warehouses and other methods of joining together multiple databases within a single system. Taking one of Noble Desktop’s SQL Courses can offer instruction in how to work with different database management systems and how to find the right combination of databases for you! For beginners to the SQL programming language and database design, the SQL Bootcamp offers instruction in some of the foundational principles of working with relational database management systems. There is also the SQL Server Bootcamp, which teaches students basic, intermediate, and advanced skills. By learning more about the SQL programming language, data science professionals can gain a solid foundation in database design and the methods of managing a data warehouse.