How Big Data Analytics Influences the NoSQL Database Landscape?
Editor’s Note: The following guest post comes from Andrew Thompson, a researcher in Big Data database management systems. IT professionals often debate the merits of SQL vs. NoSQL. In the following article, Andrew is going to share with us his perspectives on why NoSQL is better for Big Data applications
The evolution of Big Data has contributed to the incredible changes in the NoSQL database structures. The early forms of data that most large corporations and start-ups used had finite structure, and volume. The emergence of smart speakers as parts of IoT, the increasing use of mobile devices to connect to e-commerce services and the popularity of clouds has resulted in the inflow of useful data from individual interactions from customers, clients, and competitors. It has caused the drastic change of NoSQL databases and the NoSQL database management systems.
Why do many companies still work with SQL databases?
The most popular database system for data management in the early days included the SQL databases. This database type has a rigid structure, and it is impossible to accommodate Big Data in SQL databases. Big Data has immense volume, incredible velocity, and incomprehensible variety. In short, even continuous modifications to the SQL database structure is not enough to accommodate the bulk of Big Data flowing into the cloud from all directions at all times.
Despite the apparent suitability of NoSQL databases for the collection and storage of Big Data, the most popular database type today are the relational databases like MySQL, Oracle, Microsoft SQL Server and more.
- These relational databases include additional tables for metadata. In simple words, that is data on the data or information that describes the data. It provides useful details about the data source, collection methods, and meaning.
- Their tabular structure is ideal for keeping the data accessible, organized and easy-to-access for authorized parties.
- All SQL databases work on a finite infrastructure. That means the DBA always knows the quantity of the data on the database and its quality.
However, SQL databases are ideal for conventional data analytics including the statistical analysis of known data.
Why should your company opt for NoSQL databases to tackle Big Data?
When a data scientist has to deal with unknown quantities and various qualities of data, NoSQL is their first choice. They don’t organize the data into conventional rows, columns, and tables. Additionally, these databases don’t use the standard SQL query language, although some are compatible with it. They mostly use alternative language that is suitable for each data model.
SQL databases might be suitable for traditional data models, but they become quite useless while storing, sorting and analyzing Big Data. Any form of Big Data pushes the limits of conventional data storage and management technology that challenges DBAs. Most companies that harness the power of Big Data rely on NoSQL databases to complete management of data, and they depend upon the remote database management services. To learn more about remote database management you can visit RemoteDBA.com.
You should opt for NoSQL databases if you face the following concerns about your data:
- You are dealing with data that has little to no structure. It is highly inconsistent in nature.
- The data demands distribution across multiple machines and the cloud.
- While analyzing the data, speed is more important than the accuracy of the process.
- The application you are using requires multiple data types and analysis methods that most SQL databases cannot handle.
What are the different forms of NoSQL databases?
Data scientists, analysis and more prominent corporations usually prefer NoSQL databases due to their sheer variety that can tackle the unstructured data formats of Big Data. Here are the different varieties of NoSQL databases you should check out before making your decision:
1. Content store
These are the databases that are ideal for storing large volumes of complex data formats including audio and video files.
2. Document store
These are semi-structured databases that include business correspondence, journal articles, and registration forms. Most businesses that need to keep huge volumes of consumer data usually use the document store database format.
It is necessary for finding the connection between multiple types of data that flows in from all devices.
4. Key value databases
They include the least complicated of all NoSQL databases. They have a simple structure along with easy scalability and flexibility. Key value databases have a collection of– values or facts and keys or paired identifiers.
They follow the non-first normal form systems. The multi-value databases are complex data structures that have schemas like the relational database models we see around us more often.
6. Navigational databases
Navigational databases are also NoSQL, but they are typically more complex than the average Keyvalue and Multi-value variants. They usually have a hierarchical (one to many) structures or a network (many to many) structures.
7. RDF store
The RDF store databases connect multiple data sources to information processing in device applications. The complex but flexible structure of RDF store allows representation, modeling, and exploration of connection among data triplets.
8. Search engine databases
Search engines usually deal with tons of data that do not follow the structural requirements of relational databases. These databases are great for distributed computing. They provide high scalability. Search engine databases are NoSQL databases that show significant promise in the world of Big Data analytics.
9. Time series databases
It has the design to handle all forms of time series data. This type of data typically comes with a time stamp. The time if the primary axis for the identification of the information.
10 Wide column store
It is ideal for dealing with any data that will not fit on one computer. Therefore, it is the go-to database for Big Data. They are also popular as columnar databases, column families or column-oriented DBMS. Many small businesses dealing with Big Data turn to wide column store databases for storage and organization.
Most XML databases are ideal for handling almost all types and sizes of data. While XML is a markup language, it uses “tags” to identify particular functions within the document. This type of database stores data in XML format mostly, but there are exceptions. When the data format is XML, this type of database is the obvious choice for DBAs.
When a database uses more than any one of these styles to store, organize and explore data, we call it a multi-model database.
Depending on the nature of the data you are dealing with right now, either one or more of these database types can become suitable for storing your business data. Big Data has no structure, and it challenges the well-known concepts of structured data storage. That begs for new technologies of data collection and organization in the NoSQL or Not-Only SQL database formats.
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