Database management systems provide a backbone from which enterprises can draw data for applications and workloads, but they also offer additional features that protect data, like backup and recovery options.
Database management system (DBMS) software provides an environment for enterprises to categorize and query their data. Often, this software supports multiple data types, and it helps enterprises organize data based on the specific rules they set. DBMS software is designed for any business that needs to store its data in a highly structured yet flexible environment.
Today’s database software market has many providers, but finding the right solution requires knowing your business’s needs and challenges. Considerations when choosing a DBMS include enterprise size, application needs, and data types supported by the solution.
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Microsoft SQL Server is a database management system for larger organizations, particularly those with big data operations. Microsoft can assist businesses in migrating data from SQL Server to Azure.
Microsoft offers other cloud solutions that businesses can use in conjunction with SQL Server, like Azure databases. Azure Synapse is another big data tool: it’s an analytics service for multiple data technologies, like SQL, Spark, and Power BI integrations. If your business is looking for a database that integrates with a variety of big data tools, consider SQL Server.
Image credit: Microsoft
SQL Server offers Big Data Clusters, a solution that allows businesses to run clusters of SQL Server, Spark, and Hadoop Distributed File System containers on Kubernetes. Note that support for the SQL Server 2019 Big Data Clusters is set to end in 2025, so consider other big data options—like Synapse—if your business is looking at SQL Server. Big data support also allows businesses to query their unstructured data.
Through SQL Server, enterprises can create a business intelligence platform: Microsoft allows users to combine multiple data sources and use data visualization tools like Power BI and Excel through its Analysis Services. The services can be deployed on either Azure or SQL Server. Enterprises that rely heavily on analytics and want to develop a BI solution that works with their database will benefit from Microsoft’s tools for SQL Server.
Also read: 12 Key Steps for Unstructured Data Analysis
MongoDB is a database management provider that offers support for unstructured data operations and edge-to-cloud data syncing. MongoDB Realm, which offers managed back-end services, allows businesses to build mobile applications and deploy the apps on Android, iOS, and the web. Realm also offers object-oriented data models and syncing between cloud environments and Realm Mobile Database.
Image credit: MongoDB
Multiple MongoDB users also mentioned a 16 MB size limit on documents within the database; the database also requires a large amount of random access memory. If your business needs to store large documents that exceed that size, you may want to consider another database.
MongoDB offers a free community server edition of the database, with a 512 MB cluster. Users also have the option to migrate from the free edition to Atlas.
PostgreSQL is an open-source database management system that supports over fifteen data types, including JSON, XML, and custom types. It also supports multiple advanced indexing features, including GiST and bloom filters; indexing improves data retrieval speeds. These features are useful for businesses that need flexibility of data types and the ability to speedily access data.
PostgreSQL has been ACID compliant (atomicity, consistency, isolation, durability) for over twenty years. If your business is in the financial or healthcare industry and has heavy data protection requirements, consider a database like PostgreSQL that is ACID compliant.
pgAdmin, PostgreSQL’s development and administration platform, allows users to create and edit PostgreSQL objects, as well as view object statistics when possible. It permits asynchronous, synchronous, and logical replication, which gives enterprises more flexibility to choose a replication strategy that works best. PostgreSQL can be deployed in desktop mode or server mode, which permits multiple users across the web.
Image credit: PostgreSQL
PostgreSQL also has a community that’s segmented into multiple topic category email lists, including announcements and general support. PostgreSQL also has a wiki that contains user-generated content and directions for contributing code to the database. Users that want to be active in a database community will benefit from PostgreSQL’s documentation and content.
Users noted that PostgreSQL is sometimes slow while executing large or heavy queries. Consider PostgreSQL for applications with stable workloads, but it’s not the best choice for mission-critical enterprise apps that consistently process big data.
MySQL is an open-source database for businesses of any size. MySQL’s enterprise edition offers data-at-rest encryption for files of the database. It also integrates with Pluggable Authentication Modules (PAM) and Active Directory for user authentication.
Image credit: MySQL
MySQL Heatwave is a cloud-managed database service, offered through Oracle, that allows machine learning and OLAP workloads to run from a MySQL database. It provides analytics without requiring ETL duplication, which follows a three-step process to pull data out of a store to eventually be analyzed. Internal analytics require less preparatory work for data analysts.
By using Heatwave, developers can run OLTP and OLAP workloads from a MySQL database, and the service also integrates with business intelligence tools like Tableau and Looker. Businesses that want additional services, particularly analytics, included in their database solution will benefit from a tool like HeatWave.
MySQL Cluster, a technology that clusters in-memory databases together, is intended for real-time access to distributed data sets. Because multiple databases are used for the same operations, the database architecture is more fault tolerant—if one storage node fails, another can take its place.
Cluster offers replication for clusters in distributed geographic locations, and it can run on commodity hardware. MySQL Cluster supports up to four replicated copies of data, and users can store over 100 terabytes in one cluster. The solution offers five nines of availability.
MySQL is a widely used database; because it’s open source and highly popular, developers have access to a wide variety of online forum posts that can assist them with questions or problems.
Oracle Database is a DBMS intended to be used for enterprise grid computing, a method of IT infrastructure that combines distributed server resources for use by one enterprise. The most recent release is Oracle 21c, which added in-database machine learning algorithms. Data scientists are able to design predictive models that run on the data within database tables, rather than having to move the data to a sandbox environment.
21c also introduced blockchain tables, which apply a cryptographic hash to rows once they’re added to a table. The row can’t be edited later, which ensures that recorded transactions aren’t tampered with and remain accurate.
Image credit: Oracle
Oracle Database supports multiple data types, including JSON and XML. It also provides transaction controls: each transaction receives an individual ID, and transactions are ACID compliant. If one part of a transaction fails, the transaction does not complete but rather rolls back to the data’s original state. This helps operations stay consistent and prevents inaccurate or imbalanced data.
Oracle’s Real Application Clusters (RAC) support enterprise grid computing by distributing a database’s operations across multiple servers. The servers access shared data storage, and if a user session connected to the clusters has an outage, it can failover to another server. This improves organizational uptime, decreasing the likelihood of total session failure.
Teradata Database is a solution designed specifically for data warehousing and analytics. Teradata’s warehouses rely on the database, which supports multiple installations of data stores ranging in size.
One of Teradata’s main differentiators is its in-database analytics: it supports data mining and modeling, among other analysis techniques. Businesses aren’t required to integrate third-party analytics tools to Teradata to receive data insights.
Teradata Database offers capacities up to 234 petabytes (PB) and scales to 2,048 nodes total. The database manages mixed workloads, using Teradata Active System Management (TASM) to provide admins with workload requests and help them allocate resources according to the business’s needs and priorities.
Image credit: Teradata
Teradata supports symmetric multiprocessing, massively parallel processing, public cloud, private cloud, hybrid cloud, and on-premises architectures. Symmetric multiprocessing allows multiple operations to occur simultaneously, and massively parallel processing (MPP) provides non-shared memory for separate nodes to process the same computation. Enterprises that need to process large volumes of critical data benefit from MPP architectures. Teradata is a solution for businesses with intense processing requirements.
Also read: Teradata vs. Snowflake: Compare Data Warehousing Tools
Redis is a data store that functions as a database, among other tools, for software developers. This open-source “data structure server” supports multiple data types and permits server-side scripting through Lua, a scripting language for applications. Redis is also a very fast data platform, which makes it useful for real-time data access.
Redis has multiple methods of persisting data, including writing dataset snapshots and logging write commands to storage hardware like SSDs. If users don’t want to keep the data or the data needs to be unavailable after a period of time, however, they can disable persistence. Users are able to configure how often snapshots are taken based on a determined number of seconds and dataset changes. This allows them to create backup materials in a time frame that suits their business needs.
Redis Cluster allows enterprises to automatically distribute data among nodes so the corresponding workload can continue to run if one node fails. Clustering can belong to a business continuity plan, which helps organizations prepare for risks and unexpected outages.
Redis also offers detailed documentation on its Cluster TCP ports, data sharding, and configuration parameters, among other features. If you have new developers or teams that are unfamiliar with Redis, the available Cluster documentation is extensive.
Redis offers an API for building and implementing modules. Modules add data models to the existing Redis data structures, allowing developers to extend Redis commands to additional libraries.
Database management system software is a program that allows database administrators to manage stored enterprise data from any device where the database application is installed. Database system administrators are able to make advanced searches and format data. DBMS software provides support for enterprise applications that require regular data queries to meet customer needs.
DBMS software is either relational or non-relational. Relational databases use structured query language (SQL), which uses established commands to present data to users. Non-relational databases aren’t limited to SQL and use multiple methods to manipulate data; the majority of the data structures aren’t clearly related to each other.
A database management system’s primary purpose is organized storage. Though a DBMS may integrate with analytics software or include some BI capabilities, data warehouses are specifically designed for intelligence purposes, not DBMS software. DBMS software is also different from data lakes because the data is thoroughly organized. Data lakes, on the other hand, aren’t intended to be organized but to store massive volumes of data in one non-hierarchical pool. Data stored in lakes is often raw and unstructured.
Also read: What is a Database Management System (DBMS)?
Security, disaster recovery, and automation are all common features of database management system software. Some DBMS software includes additional features that don’t appear in all solutions, like machine learning features and support for unstructured data.
Databases provide storage and access for critical and sensitive enterprise data, which needs to be secured. Database administrators should also be able to set access policies for individuals or groups so the business knows exactly who can see the data.
Even basic disaster recovery features, like the ability to make remote backups to another business location, help protect against total data loss. Look for replication and failover in database software, too.
DBMS software should offer, at minimum, automatic load balancing features and policies set for creating backups. This lightens the burden on human administrators to improve performance and protect data manually.
Machine learning tools, like the option to set up ML models for the database, help enterprises further automate their data processing. If your business has multiple applications that require machine learning technology, look for a database that supports ML modeling.
Some NoSQL databases will store unstructured data, which enterprises increasingly produce. If your enterprise heavily relies on unstructured data, consider a database that supports it.
Also read: Top Big Data Tools & Software
Advantages of DBMS solutions include:
Also read: What is a 3-2-1 Backup Strategy?
If you’re choosing a cloud DBMS, check whether the vendor locks in customers. Vendor lockin makes transitioning to other technology very difficult because the cost of switching solutions is high and the vendor doesn’t support other software. Before purchasing, decide whether you’re willing to use that vendor’s technology alone.
Also count the cost of moving that much data into the cloud, especially if your database has been around for years and already stores many petabytes. Such a migration will take time and could be expensive.
Decide what applications you need to support and choose a DBMS that supports operations necessary for the types of applications your enterprise uses. If your business plans to implement machine learning models in the next couple of years, you may need a more advanced database that already permits ML modeling.
Your business might need documentation if you have less experienced developers. If your enterprise will need heavy support, choose a DBMS provider that has plenty of documentation that’s easy to understand. If a provider doesn’t offer the amount of documentation your developers need, look for highly-rated, consistently available technical support as an alternative solution.
Read next: Best Data Modeling Tools
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