Modern digital economy in the USA is based solely on the ability to collect, store, process, distribute, and retrieve data. Whether we are talking about high-performance trading platforms in Wall Street, or massive video streaming websites developed in Silicon Valley, the ability to store, process, and retrieve information from digital databases is what makes the difference between successful startups and failing projects. In this digital revolution era, Database Management System (DBMS) is the key technology for engineers to thrive in the competitive US software market.

Choosing a particular DBMS will determine performance of a system, its hosting infrastructure costs, speed of development process, and overall architectural scalability. With modern software engineering trending towards microservice approaches and decentralized cloud deployments, developers can no longer use monolithic approach to database technologies. Modern development requires the technique known as polyglot persistence: utilizing more than one type of database throughout a software project stack. This guide reviews the best database management systems every engineer should know in order to deliver top-quality products.

1. Relational Database Management Systems (RDBMS)

Relational Database Management Systems are currently considered the foundation of any enterprise corporate software stack. Such databases operate based on the relational algebra model organizing information in predefined structures that consist of tables, columns, and rows. They use Structured Query Language (SQL) to manipulate and modify information. The main criterion that distinguishes a professional-level database management system is its compliance with ACID properties (Atomicity, Consistency, Isolation, Durability). Every transaction must complete in entirety or not at all.

PostgreSQL

Also called Postgres, this database management system is known as the standard choice for engineers in America due to its excellent reliability and compliance. Postgres uses advanced object-relational data models, handling traditional relational data, while being extremely capable of working with advanced data types such as JSONB, geometries, and enumeration fields. It includes sophisticated indexes, efficient concurrency, and active development community.

MySQL

This database is the most popular open-source solution in the world and the main engine for legendary websites like WordPress, Facebook, YouTube, etc. Due to its excellent read speed and amazing horizontal scalability based on replication chains, MySQL is an essential database for developers of all types of web pages. Despite its lack of several architectural features, compared to PostgreSQL, current versions are still equipped with such important technologies as document store, window functions, and advanced JSON sorting capabilities.

Oracle Database & Microsoft SQL Server

If you deal with large-scale enterprises, legacy financial institutions, or health insurance networks in the USA, proprietary solutions are your best choice. Oracle database provides the most sophisticated transaction processing and advanced real-time data analytics. Also, it offers the functionality of enterprise grid computing. Microsoft SQL Server is famous for its deep integration with Azure cloud infrastructure and robust business intelligence features.

2. Document Oriented Database Management Systems (NoSQL)

In the era when static web pages started transforming into interactive and user-oriented ones, there was a huge amount of information that couldn’t be organized in strict schemas due to its semi-structured nature. This was the reason of emerging of NoSQL databases whose main features were horizontal scalability, flexibility, and high availability. NoSQL databases don’t match rows and tables; instead, they store data in independent records called documents (typically, JSON or its binary version BSON).

MongoDB

MongoDB is the pioneer and market leader in the document-oriented database niche. Using flexible documents as storage format, MongoDB enables programmers to develop new application features without downtime-inducing database schema migrations. A document can include arrays and sub-documents that resemble objects inside programming languages like JS, Python, and C#. The built-in horizontal sharding feature allows developers to distribute writes across different cloud server nodes. This database can be useful when developing content management applications, e-commerce catalogs, and user profiles management software.

3. Key-Value Stores

Key-value stores can be viewed as the most straightforward and high-performant DBMS type in existence. Essentially, this database operates as a gigantic dictionary or hash table with every item storing a unique identifier – a key – and data associated with it. Due to simplicity of this model, it is able to provide outstanding read and write times, processing million requests per second on minimal physical resources.

Redis

Redis (stands for Remote Dictionary Server) is an in-memory key-value data structure store mostly used as ultra-high speed caching and session management engine. It loads all of its data into host system’s volatile RAM, rather than writing them into physical solid-state drives. This way, it achieves unprecedented sub-millisecond latency. Modern developers use Redis to manage session states, cache expensive relational database requests, track global leader boards, and limit API requests.

4. Wide-Column Stores

When dealing with petabyte-scale of operational data hosted on thousands of nodes across the globe, both relational DBMS and simple document databases cannot offer enough capacity. This gap is filled by wide-column stores which are also called tabular DBMS. The essence of this technology lies in storing data as columnar blocks, reducing space usage and increasing throughput of distributed writes.

Apache Cassandra

Initially developed at Facebook in order to enable massive inbox search feature, Apache Cassandra can work with enormous amounts of data in a highly distributed manner without single points of failure across multiple data centers. This database uses peer-to-peer architecture with identical nodes, thus remaining operable despite failures of multiple server nodes at once. Cassandra is the first choice for global telecommunications networks, industrial IoT data ingestion pipelines, and streaming applications such as Netflix.

5. Graph Databases

Unlike relational databases storing data relationships as foreign keys, calculation of these connections through millions of rows takes substantial processing time. That is why graph databases are designed as the next step in managing relational data – storing data relationships as first-class citizens. Based on advanced mathematics, they store data in graph format as nodes (entities) and edges (relationships between them).

Neo4j

Neo4j is the world’s leading property graph database management system that was specially developed to manage relational and connected data. Neo4j uses unique Cypher query language that enables writing sophisticated graph queries in simple human-friendly statements. The database is deeply integrated into the systems of such leading US businesses as banks, social media companies, and logistics providers in order to power recommendation engines, fraud detections algorithms, identity management networks, and supply-chain optimizations pipelines.

6. Search Engines

Traditional relational and document databases are highly effective at querying explicit database records but naturally bad at text searches and natural language processing. Thus, applications which need the same functionalities as, for instance, Google has implemented should be provided with the search engine to perform these kinds of queries. Dedicated search engines convert the incoming data and provide high-performance fuzzy search capabilities across billions of documents.

Elasticsearch

It is a distributed JSON-based search and analytics engine built on top of the powerful Apache Lucene library. It operates by converting incoming text into inverted index – unique data structure mapping every word to the list of documents that contain them. Thanks to this, Elasticsearch offers outstanding text search capabilities. This search engine lies behind such universal toolset as the ELK Stack (Elasticsearch, Logstash, Kibana) for logging, monitoring, and performance analyses.

Summary Matrix: Database Selection Guide

To help you choose which type of database will fit your future projects best, take a look at this summary comparison matrix.

FAQ

1. What is the difference between SQL and NoSQL databases?

SQL DBMS store data as rigid table grids consisting of rows and columns that should be explicitly defined before inserting any data. This kind of databases guarantees data consistency and ACID compliance. On contrary, NoSQL DBMS use flexible structures such as documents, dictionaries, or math graphs. NoSQL databases are better when dealing with high write load, flexible data schemas, and fast development cycles.

2. Can a project use multiple database systems simultaneously?

Yes. This approach is known as polyglot persistence. A typical case is using PostgreSQL as the main database for authentication, billing, and accounting, Redis as page caching and Elasticsearch as search engine.

3. What does ACID stand for? Why should developers care?

ACID stands for Atomicity, Consistency, Isolation, Durability. These properties guarantee transactional data operations’ safety and reliability. For instance, Atomicity guarantees that in case of transferring money from one account to another, either all or nothing happens, thus not corrupting database.

4. When should I choose PostgreSQL over MongoDB?

Postgres is a perfect choice when your data is strongly structured and highly related. You can choose it when creating finance or health software, accounting, or retail inventories management systems. But if you are building a project involving flexible data structures, working with JSONs, and requiring massive horizontal scalability, you should go with MongoDB.

5. Why is Redis faster than other relational databases?

Redis is faster than others due to its in-memory approach to data storage. Unlike MySQL or Postgres that constantly write transactional data to disk or SSD, Redis stores its whole dataset in volatile memory.