In the last blog, I covered Shifting Roles with Relational and Big Data Platforms and how that is affecting the people and their roles within companies. This blog will focus on the actual platforms and what it means to be Relational vs NoSQL. What are these hybrid environments today?
As Relational and Big Data platforms evolve, the industry is using the confusing terminology of “hybrid environments”, referring to a mix of platforms and/or deployments. What does this all mean? Can we break this down in layman's terms? The answer is, yes, we can.
The terminology of hybrid environments means that companies have mixed types of databases, e.g. SQL, NoSQL, and where are they located, whether in the cloud or locally. “On premise” just means that your company is hosting the database locally. The cloud means it’s hosted somewhere else. Your company is usually paying for someone else to manage it and you have to connect remotely to get connectivity.
SQL is a technology, it stands for Structured Query Language. What does that mean? Well, exactly what it says, it is a query language that is used to pull data out of a structured database, also called a relational database because it contains specific relationships between the data tables. The key word is Structured. In order to query a database you follow structured rules as defined by the specific relational platform This enables you to easily retrieve desired data from the database. The downside of following a structure and retrieving large amounts of data is that it can be time-consuming.
NoSQL is considered Unstructured, i.e. you don’t need to follow any structure. This can provide flexibility for ingesting and managing very large data sets from a variety of data sources. On the other hand, calling it “unstructured” is misleading because a NoSQL database still has to make data accessible. You still have to have a data architecture and there are different types of architectures to be considered.
What does this mean for the database? A couple of things, one of which is scalability. Users have to scale a relational database on powerful servers.The data has to be distributed, handling tables across different servers. The second thing is complexity. The design of the data needs to fit into tables. The database structure will be complex and again, difficult to handle in large volumes.
Now, the NoSQL deployment automatically spreads your data onto multiple servers without requiring a defined structure. Servers can be added or removed from the data layer. NoSQL can cache data in system memory. This is in contrast to SQL databases where this has to be done using a separate infrastructure, therefore making it faster to retrieve.
In this IT Pro webinar, we take a deep dive into these areas of SQL vs NoSQL. We provide a look at a tool, called Aqua Data Studio, that takes advantage of all of these types of databases. This webinar will educate you on the differences of relational versus big data platforms.
Sign up to watch the IT Pro Webinar: Changing Lanes: Shifting Between Relational and Big Data Platforms, presented by Mel Beckman, a Senior Contributing Technical Editor for IT Pro Today and Lisa Waugh a Senior Product Manager at Idera Software.
Also read the related blog post: Changing Lanes: Shifting Roles with Relation and Big Data Platform by Lisa Waugh, Idera Senior Product Manager.
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This is really interesting information for me. Thanks for sharing!