In recent years, cloud solutions have become so deeply embedded in the development and operation of information technology that it is difficult to find a technology that does not have a quality cloud service.
Databases, widely used as cloud services, are no exception. Cloud databases are presented both in combination with other cloud services and as separate services with hybrid cloud systems.
Many cloud service providers are now developing general and specialized databases with internet access and easy scalability.
Here we look at the cloud solutions available as Amazon services. These services cover almost every possible database option and offer great prices for running them.
Why are Cloud Databases important?
The advantages of using databases in cloud systems can be seen in the following cases:
- a large amount of data stored in the project and frequently updated;
- The requirement for database replication;
- The need to scale up and down the number of nodes to process database queries;
- The need to locate the database as close as possible to the customer service office in order to reduce the time required to process the request;
- Tightly integration with the other project cloud services;
- Can be stored quickly and archived in the cloud at low cost.
What to consider when hosting a database in the cloud:
- Computer system security, encryption of database queries;
- Some data cannot be stored in the cloud according to the regulations of some countries, the system architecture must be carefully planned so that sensitive data is stored locally;
- Diligently reviews a particular cloud location’s certification to meet the information systems standards required by the customer;
- Choose the appropriate database type to achieve database speed and ease of use.
AWS Database Services
Let’s look at the AWS databases by type. AWS has a wide range of cloud coverage for databases, so we’ll briefly describe each solution and its benefits. To choose a database in the project, you should consult experienced specialists in the field of information systems and programmers, for example, from Svitla Systems.
Relational (SQL) Databases
A relational database is a work organization system with a set of data and predefined relationships between them. Data in a database is organized as a set of tables, and tables are made up of columns and rows. Tables store information about the objects represented in the database. Each table column contains a specific data type, each cell contains an attribute value. Each table sink is a set of related values related to a single object or entity. Each row in a table can have a unique identifier called a primary key. In this way, rows from several tables can be linked with foreign keys. Relational queries can be used to access data in multiple ways without having to reorganize database tables. Using a relational database allows us to:
- Work with data using a standardized Structured Query Language (SQL) to create complex queries to process information from a database;
- To monitor and enforce data integrity (i.e. data completeness, accuracy and consistency), applying business rules to data in spreadsheets using constraints and triggers to ensure data accuracy and reliability;
- Develop stored procedures to simplify working with relational queries;
- Use a transactional engine to aggregate database queries to meet ACID (atomicity, consistency, isolation, durability) requirements;
- Offers simplicity and ease of understanding for programmers. The only information construct used is the
- table, which uses strict design principles based on the
- mathematical apparatus to achieve complete data independence, i.e. H. Application changes are minimal when the underlying relational database is changed.
Amazon Aurora is a MySQL or PostgreSQL compatible relational database dedicated to the cloud. This database combines the speed and accessibility of traditional commercial databases with the simplicity and cost-effectiveness of an open source database. Application code built for MySQL or PostgreSQL can be quickly reconfigured to use Aurora and take full advantage of it.
Amazon Aurora can run up to five times faster than standard MySQL databases. And compared to standard PostgreSQL databases, Aurora is three times faster.
This service offers more security, multi-region availability, and the reliability of commercial databases at a price approximately 10 times cheaper. Amazon Aurora is powered by Amazon Relational Database Service (RDS).This automates time-consuming administrative tasks such as hardware provisioning, database configuration, installation of updates and backup.
Amazon Relational Database Service (Amazon RDS) is a cloud-based relational database with the ability to work with SQL queries. The main benefit of using RDS is the automation of time-consuming administrative tasks such as hardware provisioning, database configuration, patching and backups.
As described in the user documentation, RDS provides the following database engines: Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle Database, and SQL Server. AWS Database Migration Service is also great for migrating and replicating existing databases to Amazon’s relational database service.
If you need to process a large amount of information analytically, you can use Redshift, which allows you to query structured and semi-structured data. This data can be a few petabytes in size, reside in a data warehouse, operational database, or data lake using standard SQL tools, and aggregate this data. Redshift makes it easy to store query results in S3 Data Lake and then send them to various analytics services such as Amazon EMR, Amazon Athena, and Amazon SageMaker. Additionally, there is also the option to enable Advanced Query Accelerator (AQUA) mode, a new hardware-accelerated distributed cache that allows Redshift to run faster than other cloud storage solutions (up to 10x faster).
Amazon DynamoDB is a fast database of key values and documents. DynamoDB runs in multiple regions with multiple active servers and has built-in security, backup and recovery, and in-memory caching. DynamoDB can handle over 10 trillion queries per day and bursts of over 20 million queries per second. This key-value database offers less than 10 milliseconds of latency at any scale. DynamoDB can be used with AWS AppSync and GraphQL to create interactive web and mobile apps with real-time updates. Note that this database uses the key-value paradigm, accessed through the API, rather than SQL as with relational databases. Additionally, Amazon DynamoDB offers 99.99% availability and replicates six copies of your data across three AWS availability zones in each region where DynamoDB tables are deployed.
An in-memory database is a database that stores tables and records in memory. A resident DBMS, or in-memory database management system, is a type of software system that operates in the in-memory computing paradigm. In-memory DBMS offer better performance than DBMS with databases on permanent storage media thanks to the possible optimization of storage and processing conditions in byte-addressable RAM. Typically, these databases have a block organization and are connected via a bus or network interface.
Amazon ElastiCache for Memcached
Memcached is software that performs data caching service in RAM based on a hash table. Therefore, Amazon ElastiCache for Memcached is a Memcached-compliant cloud storage service for key-value data that can be used as a cache layer or data store. One of the benefits of using this cloud service is the ElastiCache client for your self-aware memcached cluster. This saves time as it makes it easier to connect your application to the memcached cluster.
Amazon ElastiCache for Redis
Redis (Remote Dictionary Server) is an open-source, resident NoSQL database management system that works with key-value data structures. As such, Amazon ElastiCache for Redis is a very fast in-memory data store that offers sub-millisecond latency for real-time web applications. As mentioned in the description of this cloud service, Amazon ElastiCache supports up to 340 TB in-memory clusters. At the same time, this cluster can be scaled up to 500 nodes. Data can be split into multiple blocks to provide high levels of scalability and availability.
Amazon DocumentDB is a document database that makes it easy to store, index, and quickly search JSON data. Amazon DocumentDB is a non-relational database with a MongoDB-compatible API that gives users the performance, scalability, and availability they need to run mission-critical workloads at any scale. As an AWS managed service, users can take advantage of MongoDB without having to manage database infrastructure, backups, and availability themselves. In Amazon DocumentDB, storage and processing are separate.
Wide column Databases
Amazon Keyspaces (for Apache Cassandra) is a scalable, highly available, and managed database service compatible with Apache Cassandra. You can use this service to run Cassandra workloads on AWS. Apache Cassandra, on the other hand, is a non-relational, fault-tolerant, distributed DBMS designed to create a highly scalable and reliable store for massive amounts of hashed data. Amazon Keyspaces is essentially serverless, so tables automatically scale in the right direction based on application traffic, and the customer only pays for the resources they use. With Amazon Keyspaces, you can build applications with virtually unlimited bandwidth and storage, and handle thousands of requests per second. This encrypts data by default, with the ability to continuously back up table data with point-in-time recovery.
Graph databases are designed for highly correlated data with complex relationships. Amazon Neptune Graph Base is available on the Amazon Cloud System, which supports the popular W3C Property Graph and Resource Description Framework (RDF) models. The corresponding Apache TinkerPop Gremlin and SPARQL query languages are also supported, making it easier to build queries to navigate complex datasets efficiently.
If you are looking for a fast, scalable, serverless time-series database service for Internet of Things (IoT) and enterprise applications, Amazon Timestream is the right choice. As mentioned on Amazon’s website, Amazon Timestream can store and analyze more than 1,000,000,000,000 events a day at 1,000 times faster speed, and the budget for this efficient solution is 10 times less than relational data databases.
This is a very impressive result and will save you a lot of money when working with millions of IoT devices or surveillance systems with complex architectures. Amazon Timestream is the right system for IoT devices, DevOps applications, and analytics applications. But as mentioned earlier, as a non-relational database, Amazon Timestream doesn’t support SQL queries.
Amazon Quantum Ledger Database (QLDB)
If your project requires the creation of a decentralized database to provide cryptographically verifiable, immutable, transparent transaction logs owned by a trusted central authority, consider using the Amazon Quantum Ledger Database (QLDB).Accounting books are required to record the history of the economic and financial activities of an organization, fintech or bank. In this case, an approach via blockchain frameworks like Hyperledger Fabric and Ethereum, which can also be used as ledgers, helps a lot. The Amazon website describes: “With QLDB, the change history of your data is immutable – it cannot be changed or deleted – and with the help of encryption you can easily verify that no unintentional changes have been made to your data.” Application. The QLDB uses an immutable transaction log, called a journal, that tracks every change to application data and maintains a complete and auditable history of changes over time.
Customer Use Cases
The most common use case we’ve seen from our customers is using an RDBMS to host any transactional application, including web and mobile applications. These RDBMSs were often implemented in RDS to take advantage of all the basic management features available, allowing us to focus on data storage and access requirements instead. (As AWS likes to say, we strongly prefer AWS to do the “undifferentiated heavy lifting” of running a highly available clustered database, including redundant storage, replication, backups, OS upgrades, etc.) It’s a common practice to offset the additional costs associated with using a managed service, using Reserved Instances for databases, which itself is a trade-off between flexibility and time commitment when utilizing the foreseeable size and location of the database for the foreseeable future. These databases most commonly included Aurora MySQL, PostgreSQL, and SQLServer.
Another common use case we observed related to an increased need for high-performance, low-latency storage instead of complex data relationships to better support mobile applications. DynamoDB was often the best solution, with a thin layer of GraphQL-based APIs on AWS AppSync where relationships between datasets could be enforced while maintaining high performance. DynamoDB is similar to RDS in that it is a fully managed service, which is also considered “serverless” because there is no underlying computing infrastructure available to users of the service. It is fully managed by AWS.
Therefore, cloud databases are rapidly entering the service of various computer systems. This applies to both relational SQL databases and non-relational NoSQL databases. At the expense of cloud database hosting, they offer a cheaper and higher quality solution that works reliably in the needs of geolocation users.
Our highly qualified specialists from Luneba Solutions solve diverse tasks in cloud systems not only on Amazon but also in other clouds like Azure, Heroku, Digital Ocean etc.
You can use our company for various tasks related to cloud systems contact, including database migration and required services related to cloud databases.