5 data management infrastructure technologies to evaluate

It's the start of a new year -- is your organization ready to face the data management challenges ahead? Here are five technologies to consider adopting.

In addition to keeping an eye on nascent technologies, it's a must for data infrastructure managers to evaluate the current tools that can help their organizations better manage data on an operational level. And the start of a year is a good time to think about ways to improve your data management platforms and processes.

Here are five data management infrastructure technologies your organization should look at for possible adoption in 2019, which promises to be another busy year for database administrators (DBAs), data architects, developers and the other IT professionals tasked with setting up and managing systems.

1. Hybrid cloud and on-premises data architectures

The goal of a hybrid cloud is to combine public cloud and on-premises platforms in a way that presents a transparent and seamless architecture to IT administrators and developers. For example, hybrid clouds enable organizations to easily deploy databases on the infrastructure that best fits the requirements of the applications they support. In addition, DBAs can use the same tools and techniques to support both cloud and on-premises systems.

Although this aspect of data management infrastructure is still in its infancy, we can expect to see an increasing number of features and services that allow organizations to provision cloud databases on servers running in on-premises data centers. Some hybrid cloud offerings to investigate include Microsoft Azure Stack, Oracle Cloud at Customer, VMware Cloud on AWS, Amazon's own AWS Outposts and the Cisco Hybrid Cloud Platform for Google Cloud.

2. Open source databases in the cloud

Many IT shops have reservations about deploying open source databases, particularly for critical applications, when 24/7 support wasn't available from major vendors. Now that AWS, Microsoft, Google and Oracle all offer open source technologies in the cloud, however, IT managers can take advantage of database products that are relatively easy on the budget and also provide peace of mind with enterprise support.

Amazon RDS cost comparison among Oracle, SQL Server and three open source databases
How the cost of open source databases compares to Oracle and SQL Server as part of Amazon RDS

AWS supports MySQL, MariaDB and PostgreSQL as part of the Amazon Relational Database Service. Microsoft likewise offers Azure Database versions of those three technologies, while Google's Cloud SQL service gives users a choice between MySQL and PostgreSQL database engines. Oracle limits itself to MySQL, which it owns. Smaller vendors include EnterpriseDB for PostgreSQL and Percona for MySQL.

3. Backup and disaster recovery as a service

The cost of the on-premises data management infrastructure required for data backups and disaster recovery systems can have a huge impact on IT budgets.

A critical responsibility for DBAs is ensuring that they can restore database systems after hardware and software failures, malware attacks, user errors, and other unfortunate events. But the cost of the on-premises data management infrastructure required for data backups and disaster recovery systems can have a huge impact on IT budgets. Data volumes that were unheard of a short time ago are now commonplace. In addition, regulatory frameworks demand an ever-expanding amount of storage for backup copies of data.

The cloud, though, has enabled backup as a service (BaaS) and disaster recovery as a service (DRaaS), which offer reduced hardware and administrative costs and the ability to customize setup as needed. In the cloud, IT shops can choose between backup and disaster recovery options that range from using the easily scalable compute and storage resources on cloud platforms to adopting a hands-off BaaS or DRaaS environment.

4. Database and IT automation tools

The overall data management infrastructure technology stack is becoming more complex, not less. Also, increasingly restrictive time constraints faced by many data management and IT teams often relegate them to a utility provider role that is responsible simply for keeping the lights on as opposed to being a strategic partner to developers and business users.

IT automation's goal is to use technology to improve the quality of repetitive processes and reduce the amount of time DBAs and other highly skilled IT personnel spend performing low-level administrative tasks. Infrastructure teams can use that additional free time to think more strategically and help business operations improve their use of databases and other systems.

Automation technologies range from niche offerings that automate a specific set of activities -- Oracle's Autonomous Database cloud service, for example -- to products that provide a complete orchestration framework and set of tools designed to generate enterprise-level efficiencies.

5. Purpose-built data stores

In the past, choosing the database technology for an application was simple: Developers were typically required to force-fit their data into an organization's relational database management system (DBMS) of choice. Now, the number of applications that need atypical processing and storage capabilities continues to increase. Smaller database vendors and market leaders alike have responded by creating a variety of purpose-built database platforms.

In addition to the numerous NoSQL offerings that are available, IT shops can now choose specialized databases like Amazon Timestream for time series data and Amazon Quantum Ledger Database for blockchain-like transaction ledgers. Other examples include Microsoft's Azure Cosmos DB for applications that require a globally distributed, multimodel database and Google Cloud Spanner, a massively scalable relational DBMS also designed for worldwide deployments.

Even Oracle, the leading relational vendor, offers a set of purpose-built products, including Oracle Spatial and Graph and the Oracle TimesTen in-memory database. Such technologies might be useful in your data management infrastructure.

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