Diving Into Databases
Big data is a crucial component of big business. Finding out where customers are, what they are searching for, and predicting their future needs can all be obtained through analyzing their data. All that data must be stored somewhere and analyzed, which is where data science comes in.
Data science, an IT career path that demands a mix of data analysis, data mining, and programming skills, dives into the nitty-gritty of data. One important job task is data scientists examining how the data relates to help determine which database to use.
As the name implies, a relational database holds data with defined relationships and interactions. These relationships mean data is defined into tables and rows. Benefits of relational databases include increased speed when processing large amounts of data, a smaller memory footprint that allows easy access to information, and much more. Popular relational databases include Microsoft SQL Server, Maria DB, Oracle Database, MySQL, and IBM DB2.
While relational databases stores data into tables, a graph database sorts information into nodes. Picture a whiteboard for graph databases and an Excel spreadsheet for a relational database. The free-flowing nature of a graph database allows for data interpretation without the constraints of preexisting relationships. Other benefits of a graph database are higher performance for complicated data relationships, flexibility for the type of information that can be stored, and compatibility with agile development practices. Popular graph databases include Microsoft Azure CosmosDB, OrientDB, ArangoDB, JanusGraph, and Amazon Neptune.
Which database for which data
The data and the relationship between it determine which database it lives in. If you retrieve your data often, a graph database would be best. Does your data contain mostly predefined relationships? Then, a relational database would be a way to go.
The future of data offers promise for businesses. If you are looking for help with your data science, In Time Tec is happy to assist. You can even read about how we used Data Science to help a global IT company solve its issue with a data warehouse.
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