ChatGPT, Midjourney, Dall-E, Copilot, and other Artificial Intelligence technology tools have completely changed how most businesses work. Around 63% of companies plan to increase their spending on Artificial Intelligence, Machine Learning, and and Natural Language Processing. In Software Development, it might be obvious how Generative AI can improve processes. I bet we've all watched OpenAI's GPT-4 introduction, where they created a Discord bot in minutes!
Database Management is another key field that takes advantage of AI. It may not be the most exciting part of the Product Development Lifecycle. Yet, Database Management is crucial for robust and large applications and for making informed decisions. In this post, you'll learn a little about how AI has become a game-changer for database management.
What is Database Management?
Data is one of the cornerstones of any business as it facilitates gaining valuable insights and building large and scalable applications. Companies store their data in databases to get the most out of it. Therefore, we strongly believe that designing and managing databases is one of the most important aspects of running a company.
Database Management includes structuring and organizing data so users can handle and access it. As you can imagine, there are quite a few ways to approach effective Data Management and tools that make the job easier for you. We're talking about relational and non-relational Database Management Systems (DBMS). You must've heard about MongoDB, MySQL, PostgreSQL, Redis, and Oracle.
Main Features of Database Management Systems (DBMS)
1. Data Modeling
Think of data modeling as organizing and representing data, commonly business data. It goes down to designing the structure and relationships of the data stored in the database. A Database Manager must define entities, attributes, and their interconnections. A DBMS normally has features like indexing, schema definition, views, and data constraints. Moreover, it supports data types and query language (like SQL). This way, data modeling gets a bit less complex for developers.
2. Data Storage
Probably the main aspect of Data Management processes is data storage. They make it easy to store, organize, and handle data. Plus, they allow for indexing, compression, and caching to optimize access and retrieval. Relational databases use related tables, columns, and rows to organize data. Non-relational databases use documents that can store various types of data.
3. Data Querying and Manipulation
DBMS provides a query language developers use for insertion, deletion, and modification. DBMS like MySQL and PostgreSQL use SQL, while MongoDB uses its query language (MQL). Developers can efficiently retrieve, filter, and sort complex data with queries.
4. Data Performance
DBMS involves tools with intuitive UIs that allow users to visualize, analyze, and handle complex data. They optimize query execution and system performance with improved response times. This way, they streamline developers' work to a large degree.
5. Data Security and Scalability
DBMS uses sophisticated mechanisms for securing the company's data. With so many businesses adopting cloud solutions, robust data security is necessary. Watching over users' data for potential security threats is something most businesses must take care of. DBMS also provides solutions for scaling massive amounts of data.
How Can AI Improve Database Management?
Let's get back to business specific AI-powered Database solutions. Using AI to optimize database management isn't just convenient because of the amounts of data businesses work with are getting far too large for us. You can no longer expect individuals to be able to guarantee data compliance, security, efficiency, and governance. It isn't feasible to manually manage so much data. That's where AI comes in to optimize almost every aspect of DBMS, helping engineers focus on logic and architecture. Machine Learning algorithms and Generative AI can easily help get actionable insights like automating SQL query optimization. Security is another aspect of paramount importance AI can improve in database management tasks and automation processes. It can help detect and prevent anomalies and fraudulent queries that could result in catastrophic leaks. AI allows for database management systems with self-healing capabilities and constant monitoring.
MongoDB and AI
MongoDB Atlas has already embraced the generative AI's power for data processing and AI-powered tools. It uses AI algorithms to analyze user behavior and preferences. That empowers apps to deliver personalized content for users. Moreover, they recently added vector search to MongoDB Atlas for implementing AI functionalities. MongoDB Atlas AI capabilities allow users to deploy and scale MongoDB clusters easily. As expected, Atlas also provides automated monitoring, security, and backups.
PostgreSQL and AI
Another less modern but very popular DBMS is PostgreSQL. It may not have MongoDB's fancy AI tools out of the box. Yet, it's still possible to use ChatGPT to improve how you use PostgreSQL and get the most out of it. As mentioned, some common practical cases include querying and indexing. Besides, you can use it to generate basic schemas and reporting.
MySQL and AI
MySQL has been around for nearly three decades. It gained immense popularity, which explains why almost 80% of all websites still use it as their primary DBMS. So, as you can expect, plenty of tools and out-of-the-box AI solutions are available. One of the most notable tools that link the power of AI with MySQL is HeatWave. When it came out in 2020, it empowered MySQL to work better with Online Transaction Processing (OLTP) and Online Analytics Processing (OLAP). Yet, its latest analytical capabilities include Machine Learning and AI-driven Automation. MySQL HeatWave can automate provisioning, data loading, query execution, and failure handling. Other tools integrating MySQL with AI include Google Cloud BigQuery and Amazon Redshift.
Oracle and AI
Oracle is so popular and large that it's almost synonymous with databases. It doesn't surprise that it has developed a wide range of AI solutions. Oracle AI services and solutions include embedded AI apps like Cloud ERP, CX, SCM, and HCM. Also, Oracle stands out in Generative AI capabilities. It aims to streamline business performance processes and automate tasks by embracing the power of AI. It also has pre-built Machine Learning models. This way, developers can use them to implement AI into their projects easily.
Modern AI Database Management Systems
Now, let's explore some modern AI-powered DBMSs you may not have heard of.
● MindsDB. MindsDB is an AI-powered database that provides a SQL-like experience for developers. It's an open-source platform that easily integrates Machine Learning into any app. What's cool about MindsSB is that it introduced a new concept for databases, AI-tables. Using them, developers can embed predictive models into their databases. MindsDB is useful for bringing AI and Machine Learning into relational databases. In fact, you can use common SQL statements to interact with it. On top of that, it also supports direct DB integrations. So, it doesn't seem to force you to stick to relational databases.
● CockroachDB. More good news continues to come. CockroachDB is another modern SQL database that brings cutting-edge AI capabilities. Yes, it has a weird name. But when you see how powerful it is, it will blow your mind. For starters, they built it from the ground up using GO. A practical use case for CockroachDB is image recognition with Google ML. CockroachDB is highly scalable and has multi-cloud services and geo-partitioning. Moreover, it ensures strong consistency, resilience, and availability.
● TiDB. Like CockroachDB, TiDB is a SQL-distributed open-source database for modern apps. It uses Machine Learning to optimize queries and improve performance based on patterns. Also, it supports automatic sharding to enhance efficiency. It integrates with Kubernetes, Terraform, Zapier, and other automation tools. Besides, it combines the best tools from relational and non-relational databases.
● RedisAI. That's right. Redis isn't only about caching, streaming, and messaging. It also supports AI models with RedisAI. It brings tensors and models to Redis so that apps can make real-time inferences. Its most popular feature is data locality, meaning all data stays in memory. This way, RedisAI can ensure top-notch performance. That's why experts recommend it for apps that require low latency.
Most popular and modern databases have embraced AI-powered data analytics tools for high application performance. That can only translate to better software products that improve the overall experience for users. In addition, there are more and more options to bring the power of Generative AI and Machine Learning models to our apps. As a result, it's getting easier to develop AI solutions that meet the demands of users.