Current Trends and Applications of Databases 2022
With more data created over the past two years than in all of human history, the need to effectively manage, manipulate and secure these information assets has never been more critical. This demand has traditionally been met by major database vendors. However, over the past decade, a myriad of challengers have entered the fray to bring order to the chaos in the face of the ongoing data explosion.
Data base have subsequently seen a dramatic evolution in recent years, with some flavors going the floppy way and others thriving to this day. Veteran DBAs will remember cutting their teeth on early DBMS offerings from Informix, SQL Server, and Oracle (the latter two are still dominant), while millennial developers remember the simplicity open source MySQL/LAMP stack and PostgreSQL. Finally, the current generation of DevOps engineers prefer the unstructured agility of NoSQL databases, like MongoDB and DynamoDB.
As it stands, most databases fall into one of two categories: relational database management systems (RDBMS) and new unstructured and/or specials. The first has been around since the 1970s and consists of linked tables, which in turn consist of rows and columns. Relational databases are manipulated using Structured Query Language (SQL), the de facto standard language for performing create, read, update, delete (CRUD) functions.
RDBMS is the dominant database type for enterprise computing and its SQL language, the lingua franca for communicating with databases. SQL-based RDBMSs are still 60.5% databases in deployment, according to a recent survey by ScaleGrid.io. In fact, this continued popularity of the SQL language has translated into big data offerings, like the aptly named SQL-on-Hadoop and Apache Hive, to adopt the language, to name a few.
The advent of the cloud has seen data processing capabilities scale horizontally like never before, just in time to support the meteoric production of structured and unstructured data brought about by the Internet. As the latter grows in importance, some have argued that a new database paradigm is in order. Thus, NoSQL was born – a broad category that today includes all databases except those that use SQL as their primary language. Since NoSQL databases have no defined schema or structure requirements, they are ideal for today’s software environments that use DevOps toolsets and CI/CD pipelines.
5 trends in the database market
The Global Database Management Systems (DBMS) Market is estimated to be worth nearly $63.1 Billion for the year 2020 and is projected to reach $125.6 Billion by 2026, growing at a CAGR 12.4% over the period, according to Expert Market Research.
Following are the key trends driving the growth of the database market:
1. SQL back to the top
Ten years ago, new NoSQL entrants seemed like formidable challengers to supplant the long-dominant SQL-based DBMS. Nowadays, it is more or less recognized that SQL will remain a cornerstone of DBMS for the foreseeable future. Even new machine learning-based offerings, such as MindDB’s ML framework and AWS Redshift ML, have integrated SQL as the default query language.
2. ML-Driven Databases
Speaking of ML, the growing trend of embedding ML models where the data resides is becoming common practice among vendors, with solutions such as Oracle Autonomous Database and Microsoft SQL Server Machine Learning Services on the enterprise side and the aforementioned solutions. MindsDB and Single store starter offers.
3. Integration of microservices
Today’s modern software engineering teams design and build applications using a microservices approach. In other words, they design applications as a series of smaller, API-driven services. This improves scalability and agility, but can be problematic for organizations with pre-existing data stored in traditional monolithic databases. Fortunately, many of the newer database offerings, the more notable NoSQL providers, such as MongoDB and AWS DynamoDB, provide the schema flexibility, redundancy/scalability requirements, and support for serverless architecture patterns required for microservices.
4. In-memory databases
Today’s mission-critical software solutions require minimal database latency for optimal performance. Unfortunately, traditional DBMSs rely on slow disk read/write operations to store data on media (e.g. HDDs, SSDs). For this reason, in-memory databases have become strong alternatives for these critical use cases: because records are stored and retrieved directly from memory (RAM), faster and more reliable performance is possible. Moreover, popular solutions such as Say it again supports more data structure types and custom access patterns, allowing software code simplification (read: no data structure conversion/serialization needed).
5. Stronger Database Security Layers
As cyberattacks and data breaches continue to dominate global technology headlines, the focus has been on securing the data layer of the software application more than ever. More and more vendors are augmenting their offerings with stronger built-in security features. For example, Oracle now incorporates persistent encryption and automated patching at the database level, while Amazon RDS includes a built-in firewall (i.e. security groups) for data access. rule-based database.
Regardless of type or flavor, databases will continue to function as the backbone of modern Internet applications, enabling large amounts of data to be processed and stored reliably and efficiently. Of course, the definition of great has changed over the years.
In general, datasets that are not manageable through traditional spreadsheets are ideal for the DBMS. And with the ever-increasing demand for databases that support specialized use cases, such as time series and geospatial applications, you can expect to see a myriad of burgeoning features from new and improved DBMS offerings. traditions on the near horizon.