7 Top Database Choices for Optimizing Performance in Machine Learning

The most crucial component in any machine learning project is the DBMS (database management system) used. With the help of an ideal DBMS, you can handle a massive volume of data that can be effectively sorted, stored, and derive meaningful insights for business decision making. According to a Stack Overflow Survey back in 2019, Redis is the most popular Machine Learning database lately, and MongoDB is one of the most featured databases.

Based on this study report and a survey conducted among the top DBAs handling high-end machine language projects, we have listed down the 7 top databases used at best in various machine learning projects.

Top machine learning project databases

  1. Apache Cassandra

It is an open-source NoSQL database system that is mostly scalable and built to effectively handle a massive volume of data in the quickest possible way. This is a highly popular DB used by top providers like Netflix, GitHub, Reddit, Instagram, etc. Cassandra features Hadoop integration and MapReduce support. Let us further discuss the significant advantages of Cassandra.

Cassandra advantages

  • Better fault tolerance: In Apache Cassandra, data is replicated automatically to multiple nodes and offers better fault tolerance. The failed nodes also get replaced with no downtime.
  • Elasticity: Cassandra can simultaneously handle read and write throughput, which can increase linearly as new machines are getting added to the system.
  1. DynamoDB

DynamoDb from Amazon is another fully managed, durable database, which is multi-regional. It has top-notch built-in security features too. The backup and restoration and in-memory cache for internet-scale machine learning applications are also commendable. This database is effectively used by business giants like Samsung, Lyft, Toyota, Airbnb, etc. DynamoDB also features encryption at rest, which will help eliminate any additional complexity and operational burden in securing sensitive data.

DynamoDB advantages

  • High durability and availability: DynamoDB spreads automatically as data and traffic expand for the tables across sufficient numbers of servers to handle the read and write throughput and the storage requirements. All these are done along with maintaining a very consistent and quicker performance.
  • Performance at scale: This DB can also provide consistent and single-digit ms response at a scale. The global tables of DynamoDB replicate the same data across many nodes at different AWS regions to offer faster local access to data in globally distributed systems.

For more information about database scaling and remote administration, contact RemoteDBA.com experts.

  1. Couchbase

Couchbase is also an open-source database server, which offers a highly distributed, document-oriented, NoSQL database. It features a faster key-value store and managed cache for the split-millisecond database operations. There are also purpose-built indexers for speedier querying and an extensive query engine to handle SQL-type queries too.

Advantages of Couchbase

  • Unified Programming Interface: This DBMS offers a uniform, powerful, but simple API across various programming languages. The API can also be compliant with different programming languages, tools, and connectors, making it easier for the machine learning app developers to build better apps.
  • SQL and Big data Integration: Couchbase also features built-in SQL and Big Data integration capabilities, which will let the users leverage the tools, enhance processing capacity, and better data management wherever the database resides.
  • Cloud and Container Deployments: Couchbase also supports various cloud platforms and different available containers. It also enables virtualization technologies at best.
  1. MLDB

MLDB is expanded as a Machine Learning Database, an advanced open-source DBMS to solve an increased number of machine learning and big data app-related problems. It is found to be useful in solving a wide range of issues ranging from simple data collection, storage, analysis, through supporting advanced machine learning models for real-time deployment and predication. In terms of MLDB, machine learning models are parameterizedby the output of well-structured procedures and run over the datasets, which contain training data.

Advantages of MLDB:

  • Ease of use: MLDB offers a very comprehensive implementation in SQL SELECT, which also treats the complete datasets as unique tables with relational rows. It will also make database systems easier to learn and use for the big data analysts aware of the RDBMS systems.
  1. Elasticsearch

Elasticsearch is an open-source distributed database build on Apache Lucene. It has the functional capabilities of analytics and search engines for various structured and unstructured data as numerical, textual, geospatial, and more. Elasticsearch functions as the central component of Elastic Stack, a complete set of open-source tools to aid in data ingestion, storage, enrichment, analytics, and graphical visualization.

Elasticsearch advantages:

  • Extensive features: Besides it, core abilities in terms of scalability, speed, resilience, etc., Elasticsearch also comes packed with many features as index lifecycle management, data roll-ups along with efficient storing of data and searching.
  • Quicker: Elasticsearch has excellent abilities in terms of full-text search and also suited ideally for the time-sensitive use cases like infrastructure monitoring, security analytics, etc.
  1. Microsoft SQL Server

Microsoft SQL Server is a C and C++ based database, which is an ideal relational database management system. This database has proven abilities to help in gaining better insights from the data with querying on all non-relational, relational, structured, and unstructured data.

MS SQL Server advantages:

  • Flexibility: You can use any development language or platform with open-source support along with MS SQL Server.
  • Ability to manage the Big Data environments: Using MS SQL Server, you can handle even the most complicated big data ecosystems easily. Big Data Clusters enable this functionality, which provides all vital Big Data elements of the data lake as Apache Spark, Hadoop Distributed File System (HDFS), etc.
  1. MySQL

This DB is also written on simple C and C++ and is identified as one of the most popular existing open-source RDBMS, which is powered by Oracle. This has been used at many successful technology giants across the globe like Twitter, Facebook, YouTube, etc.

MySQL advantages:

  • Scalability and security: This DBMS includes various data security layers that can help protect the most sensitive data. It also offers optimum scalability for increasing handline amounts of data.
  • Backup: You can find mysqldump as a logical backup for MySQL, which features enterprise and community editions.

Along with the above, you can find many relational and non-relational databases now used for machine learning applications like MongoDB, MariaDB, PostgreSQL, etc. Along with SQL and NoSQL, there is another category of databases that newly arise on the horizon, known as NewSQL, which we will discuss later.


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