por With the proliferation of Big Data, the different types of Big Data databases have become an essential element in information management, mass storage, management, analysis and dissemination.
It is not only used in statistics, but also applied to different sectors such as entertainment and social networks such as Facebook and Twitter. For these reasons, It is important to know the types of databases you work with in Big Data.
Databases
Databases are broadly classified by their way of structuring the information and the language they use to be managed, which can be SQL and NoSQL.
In Big Data, the databases generally used are NoSQL for various reasons:
High number of data sources: internet, IoT, studies, etc.
Different types of data: structured (tables), unstructured (documents, videos, etc.), semi-structured, etc.
Large amount of data.
High data volatility that change quickly and have to be processed quickly.
5 types of NoSQL databases
There are different data models as well as data types, so there are also different databases for Big Data in NoSQL.
This is how artificial intelligence is handled for databases
Columnar databases
Columnar big data databases are NoSQL databases more similar to conventional relational databases. They store structured data in individual columns, rather than tables.
You should also know that big data databases use groups of columns. They work well for machine generated data, structured data sources too large to be handled by a single computer, and for quick data queries.
If you want a fast and accurate machine-data analysis, these may be the best type of database. Apache Cassandra and Apache HBase are some of them.
Documentary databases
These types of databases They rely on document storage rather than structured data. They are suitable for unstructured data, such as open text in a letter or email, and for semi-structured data such as academic documents.
It is recommended that you pay attention to them if you are thinking about text analysis of overly large documents for conventional databases. The best known are MongoDB and Apache Couch DB.
Graph databases
These types of databases use a graphical structure that is fundamentally a diagram of the relationships within the datainstead of tables.
They are good database engines for promote web applications that have to provide information very quickly, such as those used for online shopping and social media platforms.
You’ll need to look at these types of databases if your primary interest is a fast application. The most recognized are Neo Technology’s Neo4J and Microsoft Horton.
Key-Value
These databases are designed for simple and easy application development.
They are ideal for situations where you need to work with applications that can be developed quickly and where all other considerations are secondary. The most widespread are Basho Technologies’ Riak and Redis.
XML
These types of databases use the XML language, which is the underlying language of the Web and many other information exchange systemsto define the data structure.
They are efficient for managing data that you cannot obtain with any other type of databases, and a good match when you have a large amount of data in non-traditional formats, such as video and audio.
You will turn to these types of databases when you need to delve deeper into the analysis of unstructured data such as voice or video analytics. In these types of databases we can mention, for example, Mark Logic and Sedna.
Benefits of Big Data databases
Because capacity can be added or reduced quickly and efficiently at any time, NoSQL allows organizations to easily scale to encompass large data initiatives.
Cost effectiveness
NoSQL uses hardware low costso the cost savings compared to RDBMS becomes even greater as more capacity is needed to work with petabytes and exabytes of data.
Additionally, businesses only need to deploy the amount of hardware required to meet capacity requirements rather than making large hardware investments.
Flexibility
Whether a company is developing web or mobile applications, the fixed data models of relational databases prevent or drastically reduce the capacity of an organization to adapt to the evolution of big data application requirements.
NoSQL allows developers to use the data types and query options that best fit the application’s specific use case, allowing for faster, more agile development.
Performance
With relational databases, increased performance comes with high overhead and manual overhead.
On the other hand, when compute resources are added to a NoSQL database, performance increases proportionally so businesses can continue to deliver a fast user experience.
Availability
Typical RDBMS systems are based on primary/secondary architectures that are ccomplex and can create single points of failure.
We have shared the main types of databases for Machine Learning and Big Data, their data management, to store and process them, as well as the benefits they bring.
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