Learn the Art of flirting with Machines – Databases for Machine Learning Projects

Machine intelligence is the last invention that humanity will ever need to make.

– Nick Bostrom

In Machine Learning Projects, one of the crucial components that is popularly in use is the Database Management System.

Such a system helps in storing large amounts of data which is essential to gain valuable insights from them. 

There are numerous databases that are commonly in use for machine learning projects.

Below enlisting the top databases useful for Machine Learning projects-

1. Microsoft SQL Server

Microsoft SQL Server is a relational database Management System(RDBMS) that is written in C and C++. 

With the help of Microsoft SQL Server, one can achieve crucial insights from all the data by querying across structured, unstructured, relational as well as non-relational data.

Benefits of Microsoft SQL Server

a. Flexibility

Microsoft SQL Server offers such flexibility where one can use the platform and language of any choice with open-source support.

b. Can manage the Big Data environment

 It is easy to manage a Big data environment having Big Data clusters with the help of SQL Servers.

2. Apache Cassandra

Apache Cassandra is a highly scalable and open-source NoSQL database management system.

Instagram, Netflix, Reddit, GitHub, are some of the famous companies using this popular database. 

Apache Cassandra is designed in such a way that it can manage massive amounts of data in a faster and efficient manner. 

Benefits of Apache Cassandra

a. Fault Tolerance

In Cassandra, for fault tolerance the data is automatically replicated to multiple nodes.

Along with this, it is easy to replace the failed nodes with no down time.

b. Elastic Scalability

 As Cassandra is designed with the read and write throughput, so as new machines are added, it increases linearly.

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3. Elasticsearch

Elasticsearch is a distributed, open-source search and analytics engine built on Apache Lucene.

One can make use of it for all types of data such as numerical, geospatial, structured, unstructured, and textual. 

Elasticsearch is a major component of Elastic stack, where Elastic stack is a set of open-source tools for data ingestion, analysis, storage, enrichment, and visualization.

Benefits of ElasticSearch

a. Numerous Features

Elasticsearch has various built-in features for efficient storing and searching data, such as data rollups and index lifecycle management.

b. Faster in manner

Elasticsearch is best for time-sensitive use cases like infrastructure monitoring, security analytics, etc.

4. MLDB

Machine Learning Database(MLDB) is quite useful for solving Big data Machine Learning problems.

It is an open-source system used to solve problems ranging from data collection and storage. 

This is possible through analysis and the training of Machine Learning models to the deployment of real-time prediction endpoints. Machine Learning models are applied using functions in MLDB.

Benefits of MLDB

a. Easier to use

MLDB makes the database system easy to learn and use.

Because it provides an implementation of the SQL SELECT statement, where datasets are treated like tables, and rows as relations.

5. MySQL

MySQL is powered by Oracle and is an open-source, popular relational database management system. 

It is written in C and C++. Many famous organizations such as Facebook, YouTube, Twitter, etc make use of it.

Benefits of MySQL

a. Scalability and Security

This database management system protects sensitive data as it includes security layers. It offers scalability and thus handles a large amount of data.

6. PostgreSQL

PostgreSQL is an open-source and powerful object-relational database system.

The aim of this database system is to help administrators protect data integrity, developers build applications, and much more. 

To store and scale data workloads, PostgreSQL uses SQL language in combination with various other features. 

Benefits of PostgreSQL

a. Security 

PostgreSQL has a powerful row-level security and access-control system.

b. Extensibility 

PostgreSQL provides greater extensibility as it has data wrappers that connect other databases with a standard SQL interface.

7. Couchbase

Couchbase Server is a NoSQL document-oriented engagement database. It is open-source and distributed. 

With managed cache, it exposes a fast key-value store for sub-millisecond data operations, purpose-built indexers for fast queries.

Benefits of Couchbase

a. Container and cloud Deployments

Couchbase supports various container, virtualization technologies, and all cloud platforms.

b. Big data and SQL Integrations

  A user can leverage tools, processing capacity, and data as the Couchbase has built-in Big data and SQL Integration.

8. Redis

Redis supports data structures such as bitmaps, hyperloglogs, strings, geospatial indexes, sorted sets with range queries, etc. 

It is useful as a database, cache, and a message broker and is an open-source, in-memory data structure store. 

Advantages of Redis

a. Redis-ML

It is a module of Redis which implements machine learning models as built-in Redis data types. 

It easily deploys trained models from any platform in a production environment.

Conclusion

So, these are some of the popular databases useful in Machine Learning Projects.

You just need to know your requirement and choose the one that suits the best according to your project needs.