Learn about the Best 5 Databases for Machine Learning and Data Science Projects
Advancing technology has made machine learning and other crucial aspects of artificial intelligence an important part of everyday life. From marketing ads suggestions, video suggestions on YouTube, and social media feeds, AI and ML have penetrated nearly all realms.
Unfortunately, few companies, besides tech behemoths, such as Apple and Google, are yet to leveraging machine learning to its full capabilities.
However, with several ways to learn, implement, and maintain machine learning, more small businesses are poised to enjoy the benefits of this advanced technology.
That said, data is a crucial ingredient to ML success. Most usable data for machine learning projects is stored in database of different kinds. With a plethora of databases for ML projects available it is not always easy to choose the best one for each case.
In this article we will 5 best databases for machine learning projects, give an overview and highlight their pros and cons. Lets get to it!
The Best 5 Databases for Machine Learning Projects
MySQL is probably the most popular open-source database backed by Oracle. Since its inception, it has remained a top relational database management system used by giant companies like YouTube, Twitter, Facebook, and Uber, and because of it we’ve put it the first on the list of Databases for Machine learning projects.
The popularity of MySQL is because of its enterprise-grade features and a free and flexible community license for developers.
Key benefits of MySQL include:
- It comes with data security layers that protect private data
- Easily scalable for large data sets
- Open-source management system with two licensing models
- Supports structured and semi-structured data
- Backup software that supports data backup from multiple storage engines
Couchbase is the second open-source, distributed document-based engagement machine learning database. The database performs expertly through cloud and has interesting features, such as memory-first architecture, geo-distributed deployments, and workload isolation, which supports various ML applications.
The database maintains up to 99.999 availability with negligible (sub-millisecond) latencies. Key benefits of Couchbase include:
- Offers a unified programming interface – its data platform provides a uniform and powerful app development API compatible with various programming languages, tools, and connectors. This simplifies application development.
- SQL and big data integrations – allow app developers to leverage various tools, data, and processing capacity from their preferred source.
- Cloud and container deployment – the database supports cloud platforms and various container technologies.
3. Apache Cassandra
Apache Cassandra is another excellent open-source machine learning and artificial intelligence database management system. It is specially designed to quickly handle large amounts of data, making it a favorite for most popular apps, such as Netflix, Reddit, Instagram, and GitHub.
Benefits of Apache Cassandra include:
- Can handle massive data sets
- Allows for horizontal scaling
- Among the top scalable databases
- Fault-tolerant through automatic data replication
Elasticsearch is a widely used machine learning database built on the popular Apache Lucene. Like other databases mentioned, it is an open-source database that supports all data types, including structured, unstructured, textual, numerical, and geospatial. The database features various Elastic Stack tools that ease data ingestion, enrichment, analysis, storage, and visualization.
Advantages of using Elasticsearch include:
- Plenty of built-in features such as index lifecycle management for data searching and storage
- Efficient full-text search
- Automatic sharding eases horizontal scaling
- Best for security analytics, infrastructure monitoring, and other common data-security tasks
5. Microsoft SQL Server
Microsoft SQL Server is another RDBMS (Relational Database Management System) written in C++ and C. It is an excellent choice for drawing insights from various data types through querying non-relational, relational, unstructured, and structured data sets. Microsoft SQL Server was a widely used commercial database in Windows Systems in the last three decades and remains a leading commercial database and one of the best databases for machine learning projects.
Advantages of Microsoft SQL Server include:
- Excellent flexibility – can support different programming languages
- Supports semi-structured, structured, and spatial data
- Easy management of big data environment
- SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis
- Teate, Renee M. P. (Author)
- English (Publication Language)
The Summary of the Best 5 Databases for Machine Learning Projects
Databases are crucial in training various machine learning and artificial intelligence models. The increased adoption of AI and ML models in various industries led to the explosion of databases available on the market, which makes it daunting to find the right database for your projects.
With the previous list we hope to have given you a short and top-notch list of database options to consider for machine learning projects.
That was all, thank you very much for reading How to Learn Machine Learning, and have a fantastic day!