One of the most critical components in machine learning projects is the database management system. With the help of this system, a large number of data can be sorted and one can gain meaningful insights from them. According to the Stack Overflow Survey report 2019, Redis is the most loved database, whereas MongoDB is the most wanted database.
In this article, we list down 10 top databases used in machine learning projects.
(The list is in alphabetical order)
Apache Cassandra is an open-source and highly scalable NoSQL database management system that is designed to manage massive amounts of data in a faster manner. This popular database is being used by GitHub, Netflix, Instagram, Reddit, among others. Cassandra has Hadoop integration, with MapReduce support.
Advantages:
Couchbase Server is an open-source, distributed, NoSQL document-oriented engagement database. It exposes a fast key-value store with managed cache for sub-millisecond data operations, purpose-built indexers for fast queries and a powerful query engine for executing SQL-like queries.
Advantages:
Amazon DynamoDb a fully managed, multi-region, durable database with built-in security, backup and restore, and in-memory caching for internet-scale applications. This accessible database has been using by Lyft, Airbnb, Toyota, Samsung, among others. DynamoDB offers encryption at rest which eliminates the operational burden and complexity involved in protecting sensitive data.
Advantages:
Elasticsearch is built on Apache Lucene and is a distributed, open-source search and analytics engine for all types of data including textual, numerical, geospatial, structured and unstructured data. Elasticsearch is the central component of the Elastic Stack which is a set of open-source tools for data ingestion, enrichment, storage, analysis, and visualisation.
Advantages:
The Machine Learning Database (MLDB) is an open-source system for solving big data machine learning problems, from data collection and storage through analysis and the training of machine learning models to the deployment of real-time prediction endpoints. In MLDB, machine learning models are applied using Functions, which are parameterised by the output of training Procedures, which run over Datasets containing training data.
Advantages:
Written in C and C++, Microsoft SQL Server is a relational database management system (RDBMS). This database helps in gaining insights from all the data by querying across relational, non-relational, structured as well as unstructured data.
Advantages:
Written in C and C++, MySQL is one of the most popular open-source relational database management systems (RDBMS) powered by Oracle. It has been used by successful organisations such as Facebook, Twitter, YouTube, among others.
Advantages:
MongoDB is a general-purpose, document-based, distributed database which is built for advanced application developers. Since this is a document database, it mainly stores data in JSON-like documents. It provides support for aggregations and other modern use-cases such as geo-based search, graph search, and text search.
Advantages:
PostgreSQL is a powerful, open-source object-relational database system which uses and extends the SQL language combined with many features that safely store and scale the most complicated data workloads. This database management system aims to help developers build applications, administrators to protect data integrity, build fault-tolerant environments and much more.
Advantages:
Redis is an open-source, in-memory data structure store which is used as a database, cache and message broker. It supports data structures such as strings, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, etc. The database has built-in replication, Lua scripting, LRU eviction, transactions and different levels of on-disk persistence.
Advantages:
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