Databases for Machine Learning – Here is What You Need to Know

Author

Robert Koch

I write about AI, SEO, Tech, and Innovation. Led by curiosity, I stay ahead of AI advancements. I aim for clarity and understand the necessity of change, taking guidance from Shaw: 'Progress is impossible without change,' and living by Welch's words: 'Change before you have to'.

Databases are a critical element in machine learning today. It helps you train various machine learning and artificial intelligence (AI) models. The excellent benefits that these technologies offer are the primary reason behind their growing use of this technology.

In the past few decades, many new datasets have been available. As a result, it might be a challenge to choose the best one for your tasks. However, it also allows businesses to choose from the large number of datasets that can be the perfect fit for the application plan.

So, what are the best databases for machine learning that you can find in the market? Should you go for a free AI database or a customized one? And what is the advantage of using customized databases for your ML tasks? We’ll discuss all those things in this article.

Table of Contents

• Best Databases for Machine Learning and Artificial Intelligence
• Advantages of Using Customized Databases?
• How to Choose the Right AI Database for Your Needs?
• What makes AI databases different from traditional databases?
• Problems That You Might Encounter With a Free Database
• Integration Scenarios and Challenges
• Conclusion
• FAQs

Best Databases for Machine Learning and Artificial Intelligence

Choosing the correct databases for your machine learning and Artificial Intelligence tasks can ensure you get the desired results. We have listed the top ten databases and their core features to make things easy. You can choose any one of them according to your needs.

• Redis
  

  Redis is a top-notch open-source, in-memory data structure many people currently use in the market. You can use it as a database for machine learning and AI projects or tasks.

  The best thing about Redis is that it supports various data structures like bitmaps, geospatial indexes, sorted sets, etc. Additionally, you can also find the following features if you choose Redis as a database:

  • Transactions
  • Lua scripting
  • LRU eviction
  • Different levels of on-disk persistence
  • Built-in replication

  It also comes with an automatic failover process. You can also use Redis to write complicated code with fewer and easy lines. So, if you are looking for a robust database for your machine-learning tasks, then Redis is an optimal choice.

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• PostgreSQL
  

  Another exceptional open-source database system that we have on the list is PostgreSQL. This robust tool uses the SQL language and various other features that store the most complex data workloads.
  The best thing about PostgreSQL is that it allows developers to build apps and services to protect data integrity. In addition to that, there are many other things that you can try with this powerful database system.

  Extensibility is a critical feature of PostgreSQL that helps it to stand out. It includes foreign data wrappers, which can easily link different databases or streams with a standard SQL interface. Moreover, it is highly safe since it has a powerful access-control system.

• MySQL
  

  We cannot put MySQL off the list when we talk about AI databases. The brains at Oracle are behind this fantastic and popular database that came into the market in 1995. Many big names in the tech industry have been using this database, such as Facebook, Twitter, YouTube, etc.

  So, what is the reason behind the popularity of MySQL? First, it provides enterprise-grade gestures that make it an optimal choice for enterprises. Next, it has an adjustable community license that you can get for free. Moreover, MySQL has also made some upgrades to its commercial licenses.

  Additionally, it has many data security layers to protect confidential information. The scalability you can get from MySQL for large amounts of data is also unmatchable. Another great thing about this database system is that it supports semi-structured data (JSON) and structured data (SQL). The MySQL Cluster also lets you perform various multi-master ACID transactions.

• MongoDB
  

  MongoDB was the first document database that surfaced in 2009. The primary aim of MongoDB is to manage document data, and it has seen rapid improvement in its overall structure over the last few years. One of the things about MongoDB is that it is the best and most popular document database.

  Additionally, it is also a leading name when it comes to NoSQL databases. If you face issues when saving semi-structured data in the database, then MongoDB is the best solution to this problem.

  You can also use the auto-sharding that MongoDB comes with for horizontal scaling. Another great advantage of this database is its built-in replication via primary-secondary nodes.

• MLDB
  

  The MLDB stands for Machine Learning Database, one of the best open-source systems in the market. This system’s primary goal is to handle all the machine learning tasks.

  This system can take advantage of various uses, such as utilizing it for collecting and storing data by instructing machine learning models. The stand-out feature of the MLDB is that it is pretty easy to use compared to other datasets. The primary reason is that it comes with an extensive implementation of the SQL SELECT statement.

  Therefore, it indicates that the MLDB treats the datasets as tables. Consequently, it becomes easier for data analysts who understand the existing Relational Database Management System (RDBMS) to use the datasets.

• Microsoft SQL Server
  

  The Microsoft SQL Server is also one of the most popular databases. You can use this robust relational database management system (RDBMS) to get relevant insights into all kinds of data. The database is written in C and C++ and has been in the market for over three decades.

  This robust Multi-model database provides support to structured and semi-structured data. You can also use it for spatial data if you want to. Also, the Microsoft SQL Server supports server-side scripting via various programming languages, such as Python, Java, etc.

• Apache Cassandra
  

  Last but not least, we have Apache Cassandra on our list. It is one of the market’s most popular and best machine learning and AI databases. This scalable NoSQL database management system allows you to scale more significant amounts of data quickly.

  This database is used by even popular tech companies and social media sites like Reddit, Instagram, and Netflix. The stand-out feature of this database is that the data in it replicates itself to various nodes for fault tolerance. Also, the design of this database is for both read and write throughput. As a result, it raises the linearly when you add new machines.

What are The Advantages of Using Customized Databases?

Organizations that embrace new technological trends quickly have a better chance of getting a competitive edge over others. Therefore, it is best to go for a customized database since it can offer you a wide range of benefits. Let’s go over a few of them.

• Proper Management of the Data
  

  A significant advantage of having a customized database is that it allows you to manage your data quickly. You can use it for reporting, creating workflows, automating alerts, and many more. Since everything about this digital world is related to data, it is vital to ensure that you properly manage it.

  Not just that, you can also ensure that your team can easily understand the database and use it for your machine learning tasks. It will help you get optimal results for your efforts.

• Much Better in Terms of Speed
  

  When working on a machine-learning task, you want things to go quickly. And in most cases, the free databases are slow and require you to perform different tasks. On the other hand, building a customized database gives you a compact system that doesn’t burden your IT infrastructure.

  The database will be designed so you and your employees can easily use it without too much trouble. You can quickly input the data or use the databases for any other purpose without going through a lot of hassle. Most importantly, it will help you when your business grows since the right solution scales up without any extra work.

• Less-Costly in the Long Run
  

  Most people choose free databases since they consider them to be a less costly option. However, it might surprise you that using a customized database will cost you less in the long run.

  When we talk about incorporating new technology, it isn’t only about the cost to acquire but also the changes you need to make in infrastructure to accommodate it. Also, the time your resources spend on that technology is a cost many people don’t consider.

  Therefore, it might seem that using a free database will cost you less on the surface, but if you dig deeper, it will be expensive for you in the long run. Customized databases don’t require you to make any changes to your IT system and infrastructure. Also, since it is easy to use, your team won’t spend too much time understanding how to make the most out of it.

• Support and Assistance
  

  Since databases are critical for your machine learning tasks, any issues in them can bring the entire project to a halt. It can waste your time and resources since you won’t be able to proceed any further without it working correctly. This problem will likely occur if you are using a free database.

  And there is a good chance you won’t have any customer support or technical team to assist you with the problem in the database. On the flip side, if you get a customized database from a database development provider, they will also provide you with technical support.

  Database development service providers want to ensure that their clients get a robust and error-free database in the first place. Their technical experts can help even if there is a problem or something the clients fail to understand about the database. Therefore, it is another great advantage of using a customized database.

How to Choose the Right AI Database for Your Needs?

Choosing the right AI database for your needs involves a careful consideration of your specific requirements, projected data growth, and the types of analysis you’ll perform. Below is a structured approach to help you navigate this decision-making process.

Understanding Your AI Workload

Before diving into the features and types of databases, you need to have a clear understanding of your data. This means looking at the nature of the data you’re dealing with, such as text, images, or videos. Consider how much data you’ll be working with and at what speed it will be coming in. The complexity of the analysis is also crucial. Are you running simple queries or building complex machine learning models? Knowing this helps you understand the kind of database capabilities you’ll need.

Key Features to Look For

Performance and speed are non-negotiable when it comes to AI databases, as they directly impact your ability to process data in a timely manner. The ability of the database to grow with your data, known as scalability, is another essential feature. AI applications often require flexibility in data modeling, so a database that supports various data structures is beneficial. Concurrency, or the database’s ability to handle multiple operations simultaneously, is particularly important for real-time data processing.

Evaluating Database Types

NoSQL databases are often preferred for their ability to manage large volumes of unstructured data, which is common in AI. NewSQL databases bring together the scalability of NoSQL with the reliability of traditional SQL databases. If your AI applications involve intricate data relationships, a graph database could be more appropriate. For analyzing data over time, time-series databases might be required. Some AI tasks, especially those involving deep learning, benefit from the high-speed processing capabilities of GPU-accelerated databases.

Cost and Operational Considerations

Looking beyond the initial price tag to the total cost of ownership is crucial. This includes the long-term expenses related to scaling, maintenance, and support. It’s also wise to consider the vendor support and the user community around the database, as they can be invaluable resources. For projects handling sensitive data, the database must comply with relevant security and privacy regulations. Lastly, the user experience is important – the database should be something your team can work with effectively without a steep learning curve.

Making the Decision

Before making a final choice, it’s recommended to conduct a proof of concept to see how the database performs with your data and use case. Benchmarking can offer quantitative data to compare how different databases might perform under specific conditions. And if you’re ever in doubt, consult with experts. Their experience can help steer you towards a database that aligns with your technical requirements and business goals.

What makes AI databases different from traditional databases?

AI databases are designed to handle the complexities and demands of AI workloads, which differ significantly from the tasks traditional databases are typically used for. Understanding these differences can help clarify why a specialized AI database might be necessary for certain applications.

Data Structure and Management

Traditional databases are optimized for structured data that fits well into tables, like financial records or customer information. AI databases, on the other hand, are built to handle a variety of data types, including unstructured data like images, audio, and text. They also offer flexible schemas or even schema-less data management to accommodate the fluid nature of AI data.

Performance Requirements

AI applications often require real-time data processing and high-throughput to train models and make predictions. AI databases are engineered to deliver this level of performance, often leveraging in-memory processing, distributed architectures, and advanced indexing to speed up data retrieval and computation.

Scalability and Flexibility

The scale of data used in AI can be massive and grow unpredictably. AI databases are designed to be highly scalable, both in terms of storage and computational power, to meet the needs of large-scale machine learning tasks. They provide the ability to scale out (adding more nodes) rather than just scale up (adding more power to a single node), which is a common limitation in traditional databases.

Advanced Analytics and Machine Learning Integration

AI databases often come with built-in analytics capabilities and direct integration with machine learning frameworks and libraries. This integration simplifies the pipeline from data storage to model training and inferencing. In contrast, traditional databases may require data to be moved to a separate analytics environment for such tasks.

Problems That You Might Encounter With a Free Database

Most businesses that want to use a database for their machine learning and artificial intelligence projects only consider the cost aspect. They don’t consider the other factors that might lead to future problems. Here are a few challenges that businesses using a free database might face.

• Compatibility Problems
  

  Compatibility is critical when choosing the correct databases for your machine-learning project. If you ignore this aspect, it will lead to problems in the later part. Most proprietary hardware requires a specialized driver to run open-source databases.

  While the equipment manufacturers would give you access to databases, they would charge you for the specialized driver. As a result, it can add up the cost of your machine learning project. Even if you have an open-source driver, chances are it wouldn’t work with your software.

• Hidden Fees
  

  While it might seem like the database is free, you might incur charges later on. Most software is free to use in the initial stages, but they might charge you a small fee after some time or for some extra features. So, the database might be accessible for now, but there will be some hidden charges you are unaware of. It would again increase the cost of your machine learning project and offset the advantage of a free database.

• Liabilities and Warranties
  

  When you are using proprietary software or database, it usually comes with indemnification and a guarantee from the developers. These are an integral part of the standard license agreement you’ll get from a developer. The primary reason for this guarantee is that the developers have complete authority and copyright for the product. However, that is not the case with open-source software licenses since they only have a restricted warranty and no liability or indemnification.

• Difficulty in Using
  

  One thing about using a free database is that it might not be easy for you or your team. You might spend most of your time trying to figure out different things. It would waste much of your time, a critical element in this digital era. If you are slow, someone will get a competitive edge over you.

Integration Scenarios and Challenges

Integrating machine learning databases with analytics and business intelligence tools is crucial for organizations to derive maximum value from their data. However, this integration process can present various challenges. Let’s explore some common integration scenarios and the challenges they may pose.

Integration with Popular Analytics Tools

Tableau Integration

Tableau offers robust connectivity options for various databases. When integrating with machine learning databases:

• Use native connectors for supported databases like PostgreSQL or MySQL
• Leverage Tableau’s Web Data Connector for NoSQL databases
• Utilize Tableau Prep for data cleaning and preparation before analysis

Challenges:

• Performance issues with large datasets
• Limited support for some NoSQL databases
• Complexity in handling unstructured data

Power BI Integration

Power BI provides seamless integration with many database types. For machine learning databases:

• Use DirectQuery for real-time data access
• Implement dataflows for ETL processes
• Utilize custom connectors for unsupported databases

Challenges:

• Refresh limitations for large datasets
• Complexity in modeling relationships for NoSQL data
• Security concerns when connecting to cloud-based databases

Custom Analytics Platforms

When integrating machine learning databases with custom analytics platforms:

• Develop APIs for data exchange
• Implement ETL pipelines for data transformation
• Use data virtualization techniques for unified data access

Challenges:

• Ensuring data consistency across systems
• Managing data latency in real-time scenarios
• Handling schema changes in the source databases

Best Practices for Smooth Integration

To address these challenges and ensure smooth integration:

1. Implement proper data governance: Establish clear data management policies and procedures to maintain data quality and consistency across systems.
2. Optimize database performance: Use indexing, partitioning, and caching strategies to improve query performance, especially for large datasets.
3. Leverage cloud solutions: Consider cloud-based data warehousing solutions that offer built-in integration capabilities with various analytics tools.
4. Implement data virtualization: Use data virtualization techniques to provide a unified view of data from multiple sources without physical data movement.
5. Ensure scalability: Design your integration architecture to handle growing data volumes and increasing analytical demands.
6. Prioritize security: Implement robust security measures, including encryption and access controls, to protect sensitive data during integration processes.
7. Continuous monitoring and optimization: Regularly monitor integration processes and optimize them for performance and efficiency.

By addressing these integration scenarios and challenges, organizations can create a more seamless and efficient data analytics pipeline, enabling them to leverage their machine learning databases more effectively for business intelligence and decision-making.

Conclusion

We hope you now have a comprehensive idea about the databases for machine learning through this article. Data is becoming a critical resource for businesses today. Using it properly can allow businesses to get a competitive edge over others.

Also, the new technological concepts for machine learning and artificial intelligence can help you get a competitive edge over others. So, if you can choose the correct databases for your ML projects, you can get the desired results in no time.