The recent trends in Database Management reflect how organizations are improving their storage and how they process data. Organizations can drive their business growth by improving their Database Management platform.

Database Management typically involves the use of software to support automated data services.

Database Management is not the same as Data Management, nor is it Data Governance. Data Management deals with the overall organization of data, the steps needed to achieve efficiency, and the steps involved in gathering useful business intelligence from the data. Data Governance focuses on the practices of gathering and storing trustworthy data and enforcing the regulations and laws regarding data and personal privacy.

Database Management and Data Governance are actually subdivisions of the larger overall Data Management strategy.

The first Database Management system (DBMS) was created in the 1960s by Charles Bachman and was called the Integrated Data Store because it could take data from differing sources and “integrate” it into a single, coherent storage system. From there, it evolved into a platform of fairly simple software programs that provided users in different geographical locations with access to data stored at a centralized location. 

Modern Database Management platforms have evolved into a storage system that will automate various administration tasks. Each DBMS platform comes with its own unique design, and its selection should be based on the goals of the business.

Listed below are some up-and-coming Database Management trends, as well as some persistent ones. 

Metadata Management

Database Management should include metadata, which is a small amount of data attached to a larger amount of data, such as a file or an image, and used to describe and identify it. When organized, metadata can be used to locate stored data quickly and efficiently.

Sadly, many organizations have data systems that are disorganized and overcrowded. The vast amounts of data that are generated daily are difficult to organize, in turn making it difficult to find when it’s needed. A robust metadata management strategy can organize the data, improving its quality and accuracy. Businesses working with a well-designed metadata management strategy make decisions based on accurate data, unlike businesses with no organized metadata.

Automated metadata tools can help in developing and building data catalogs, business glossaries, and graphs.

Graph Databases and Artificial Intelligence

Graph databases are not a new concept; however, many developers have begun experimenting with graph databases in developing artificial intelligence. Graph databases include relationships when retrieving data. This is a closer model of how the human brain works than the system of columns and rows used by SQL. As a consequence, developers are experimenting with graph databases as a foundation for training artificial intelligence. 

Graph databases typically use NoSQL storage systems to resolve the challenges of working with unstructured data. NoSQL provides graph databases with a framework to index and retrieve data in the most efficient manner. 

Bridging SQL and NOSQL

Until recently, databases were split into two basic categories, SQL and NoSQL. Current technological advancements support the development of bridges being built between the two databases. These bridges (data lakehouses and data warehouses) promise users the best of both systems and allow them to access NoSQL databases the same way they would access SQL databases.

A data warehouse is a form of data storage used by organizations to store massive amounts of data, which can be accessed easily for research and analytics. In a data warehouse, typically, all structured and unstructured data is transformed into an SQL format before being stored. This process makes it easy to access the data. 

Data lakehouses are a fairly new form of Data Management platform that processes unstructured data. They are a solution for the problem of finding data in a data lake. The data lakehouse design separates its unstructured and semi-structured storage from its computing processes. Data lakehouses (and data lakes) typically use an inexpensive NoSQL form of storage, called object storage. 

In-Memory Databases

An in-memory database (IMDB) is data storage that saves all its data in the computer’s main memory (its random-access memory, or RAM). IMDBs are gaining popularity because they respond much more quickly than traditional disk drives. The reduced response time takes place because the data doesn’t have to be transformed or cached – it’s just sitting in the system, waiting to be used. The industries benefiting from the use of in-memory databases include gaming, telecommunications, banking, and travel.

In-memory databases are becoming a trend because they provide faster response times than accessing discs, data warehouses, or data lakes. 

Moving to the Cloud

New businesses (and people expanding their business) find the cloud a convenient and inexpensive way to provide customers with access to their services and/or products, and to process their collected data. Cloud service providers offer a wide variety of services, allowing an organization to design a data system they couldn’t otherwise afford. If done with a focus on cost-effectiveness, moving to the cloud can provide significant benefits.

Many businesses start off using the cloud for data storage. Cloud environments offer the ability to store data remotely on a cloud through the internet. This service can free up data storage space on in-house hard drives and make data accessible to people from anywhere in the world with an internet connection. 

Use of the various cloud services (other than data storage) can also be beneficial to businesses. According to Murphy Vandervelde, a senior manager at Turbonomic:

“In today’s business world, speed and agility are two buzzy concepts that all companies aim to achieve. For the longest time advancements on the development side of the house outpaced that of the infrastructure, but with the evolution of public cloud providers, hyperconvergence, PaaS and a multitude of other offerings, IT now has the means to achieve the speed and agility that is required of them.”

Automated Database Management

Automating Database Management has become one of the most popular trends, primarily because it eliminates human error and accomplishes tasks much more quickly. Database automation tools support a variety of automated services. These are some examples of database automation:

• Automated data processing: Automated data processing handles large amounts of data quickly and efficiently, with a minimum of human interaction.
• Automated backup and restoration: Automates the entire backup and restoration process without any human intervention.
• Automated load balancing: Provides I/O resource management by dynamically reacting to load changes and automatically correcting the volume controller ownership to deal with any load imbalance issues as workloads move across the controllers.
• Automated audits and reporting: Collects the necessary information from differing sources and integrates the data. It reduces time-consuming manual activities and can result in significant savings.

Augmented Database Management

Merging artificial intelligence with database operations creates an augmented Database Management system. 

Augmented Database Management includes the use of artificial intelligence for improving or automating Database Management tasks. It uses machine learning algorithms to automate such tedious, time-consuming processes as data mining, Data Quality inspections, data cleansing, and finding data relationships. Database Management tasks historically requiring large amounts of human labor can be accomplished more quickly and efficiently using artificial intelligence to initiate automated services. 

Security

As long as there is a criminal element, there will be a need for continuously improving security. There have been a significant number of major and minor data breaches in the last few years. Three examples of major data breaches that took place in the past two years are:

• Hafnium attack: A Chinese hacking group (called Hafnium) attacked Microsoft. Their attack impacted over 30,000 organizations in the United States, and included government agencies, local governments, and businesses.

• Facebook breach: Hackers accessed the personal information of millions of people, including their phone numbers, birthdays, and some email addresses. 

• Colonial Pipeline ransomware attack: Attackers breached the company using a single compromised password. This incident caused them to halt the flow of fuel through its mainline to regions of the United States, in turn causing fuel shortages.

Database administrators should work with security to eliminate internal weaknesses that make data vulnerable cyber criminals.

Staying Aware of Database Management Trends

Evolving technologies and structural improvements have brought about some fairly impressive changes in how computers process data. Faster response times and improved performance are a continuous goal that has supported these developing technologies.

Technological advancements continue to improve the ways computers handle data. Staying aware of Database Management trends can help businesses stay competitive by deciding which advancements are actually useful for them and provide improvements. If a tool appears potentially useful, it’s worth doing some pros and cons research before making the purchase.

Image used under license from Shutterstock.com