A cloud database is similar to an onsite data management platform, with the exception of it being hosted and managed on cloud computing platforms. Those platforms are accessed remotely via the internet. They can be offered as a managed service or hosted on a virtual machine. In simple terms, a cloud database is not deployed to an on-premise site. A cloud database provides advantages such as scalability, high availability, high performance, easy deployment and management, accessibility, and cost efficiency. Using a cloud database can be helpful for new developers using MySQL.
It also gives an opportunity to gain exposure to common standards and practices in the development industry, which promotes growth. However, latency, downtime, database migration, security, and disaster recovery are some of the factors to consider when opting for cloud database services offered by cloud database management platforms.
Cloud data management platforms take advantage of cloud computing infrastructure and services to store, manage, and provide access to data online. Cloud infrastructure provides a host of powerful servers, network resources, and storage resources that makes computing on a large scale cheaper and faster. These infrastructures are housed in data centers located across different regions. This can improve latency, given the client’s location. The most common cloud infrastructures are Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure, IBM, and Oracle.
Database solution providers such as PlanetScale also offer cloud-based services, like our Managed Cloud package. Our services enhance development workflow at competitive pricing.
In order to set up a cloud database, a developer has to first choose which infrastructure service to use. Whichever infrastructure service is chosen would have a specific database service that would have various database options such as relational (MySQL, PostgreSQL, etc.), NoSQL (MongoDB, Cassandra etc), and other types of databases.
Necessary resources can be programmed and configured through a graphical user interface (GUI) or application programming interface (API) provided by the cloud provider.
To perform create, read, update, and delete (CRUD) operations on the database, the use of Structured Query Language (SQL) would suffice, as most cloud databases support SQL. For non-relational databases, the use of specialized APIs would also enable CRUD operations.
The two major types of cloud databases are relational or SQL databases and non-relational or NoSQL databases. NoSQL can easily be mistaken to mean no use of SQL but some databases, such as MongoDB, offer support for SQL-like language to query data. So NoSQL really means Not Only SQL.
A relational database service arranges data into tables consisting of rows and columns. It uses a set of predefined relationships between tables, known as foreign keys, to establish connections and maintain data integrity. When a relational database is deployed in the cloud, it becomes a relational cloud database. This implies that the database and its associated infrastructure, including storage, processing power, and networking, are provided and managed by a cloud service provider. Examples of popular cloud relational databases include PlanetScale, Amazon RDS, Google Cloud SQL, and Microsoft Azure SQL Database.
A NoSQL database is a type of database system that is designed to store and manage unstructured or semi-structured data, and most especially, real-time data. NoSQL databases offer flexible data models and horizontal scalability, making them great at handling various data types and high-velocity workloads. A NoSQL cloud database is simply a NoSQL database deployed in the cloud. Cloud service providers offer NoSQL database services, such as Amazon Web Services (DynamoDB, DocumentDB), Google Cloud Platform (Firestore, Bigtable), and Microsoft Azure (Cosmos DB, Table Storage).
As discussed earlier, there are many cloud infrastructure providers to choose from. When it comes to working with MySQL in a cloud environment, a few popular options include AWS with Amazon RDS, GCP with Google Cloud SQL, and Azure with Azure Database for MySQL. The choice comes down to preference and application requirements.
After choosing a provider, you’d have to create a database instance. Depending on the provider you choose, there are different methods of creating an instance. To do this, you’ll have to log in to the cloud provider’s console and navigate to the database services section. Then provide a name for your database instance and specify “MySQL” as your database engine. Other configuration options include desired computing and storage resources, authentication and access controls, backup, maintenance, replication, and monitoring and alerting.
Connecting to the cloud database can be done through tools like MySQL Workbench, command-line clients (e.g., MySQL CLI), or APIs. To connect, you’d first have to obtain the connection details for the cloud database instance. This should contain the endpoint or hostname, port number, and credentials (username and password). The connection details prevent unauthorized access to your database.
If you have an existing database on MySQL, you might want to migrate it to the cloud. Migration services tend to differ, depending on the provider but often enough, there would be a guideline provided by the cloud provider that shows the step-by-step process on how to carry out migration.
After connecting to the database and migrating existing data, you can query the database using SQL statements such as SELECT, INSERT, UPDATE, and DELETE. Statements such as CREATE TABLE, ALTER TABLE, and DROP TABLE can be used to manipulate database tables. To use constraints, statements such as PRIMARY KEY and FOREIGN KEY would suffice. Essentially, MySQL statements would work the same way on a cloud database as they would work on an onsite database.
To ensure efficient data processing, faster response time, and optimal resource utilization, performance optimization is a great practice. The primary step to ensure performance optimization is database design and query optimization. Database design involves designing a well-structured database schema with appropriate normalization, defining primary and foreign key relationships, using appropriate data types and column sizes, and creating indexes on frequently queried columns. Query optimization involves the use of appropriate indexes, and minimization of costly operations such as joins, subqueries, and full table scans. Other advanced methods include monitoring resource allocation and capacity, vertical and horizontal scaling, implementing caching mechanisms, using load balancers and replication, and implementing data partitioning.
As technology evolves, cloud databases evolve as well to meet the ever-changing demands of modern applications and infrastructure. Some key trends are:
At the forefront of trends and innovation in cloud databases are Serverless Architecture and DBaaS or Database SaaS. Serverless databases provide developers with fully managed database solutions, so they can focus on development without needing to manage infrastructure, scaling, and provisioning, or worry about database administration tasks. PlanetScale is the world’s most advanced MySQL serverless database platform. We offer advanced features and capabilities for developers and organizations. Click here to know more about our deployment options or simply sign up with us today.
Containers and container orchestration platforms like Docker and Kubernetes have become increasingly popular for deploying and managing applications. Cloud databases are adapting to this trend by providing seamless integration with containerized environments. MySQL databases can be deployed as containers, allowing for easier deployment, portability, and management across different environments. Container orchestration platforms also offer features for automated scaling, high availability, and workload management, enabling efficient management of MySQL databases at scale.
Several noteworthy emerging technologies in the realm of cloud databases are revolutionizing performance, scalability, and data processing capabilities. One such technology that stands out is the integration of Machine Learning (ML). Cloud database platforms are integrating machine learning capabilities to enhance query optimization, performance tuning, and data analytics. By leveraging machine learning algorithms, MySQL databases can automatically analyze query patterns, optimize execution plans, and provide intelligent recommendations for database performance tuning. Another good technology is an In-Memory database which stores data in RAM, resulting in faster data access and processing speeds. An example of a popular In-memory database is Redis.
Cloud databases are database management systems deployed on cloud infrastructure as opposed to an on-premise environment. This allows for remote accessibility and management, regardless of location via the internet. One of the major advantages of cloud databases is scalability – handling large amounts of data and applications as the need arises. This makes it quite flexible, as they can be set up and decommissioned without hassle.