This article compares PostgreSQL and MySQL, covering their definitions, core similarities, key differences in performance, scalability, features, ecosystem, and community, and provides practical guidance on selecting the right database for various project scenarios.
Despite the hype around NoSQL, this article argues that SQL and relational databases remain the superior choice for transactional data, tracing two decades of research, highlighting the limitations of NoSQL, and showing how modern databases now incorporate the best features of both worlds.
The global database market is accelerating beyond $1 trillion, driven by cloud adoption, AI workloads, and the rise of vector and NoSQL databases, while traditional relational vendors slow, creating a unique growth dynamic for enterprises seeking the right data solutions.
This article introduces SQL as the standard language for relational databases, explains how data is organized into tables with primary and foreign keys, and walks through essential keywords like SELECT, WHERE, and JOIN to query and relate data effectively.
Data lakes and data warehouses serve different purposes in big‑data architectures; this article explains their definitions, core attributes, five major distinctions—including data retention, type support, user coverage, adaptability, and insight speed—and offers guidance on selecting or combining the two approaches.
This article explores how bank DBAs continuously improve their capabilities through standardized daily inspections, automated environment provisioning, careful upgrade planning, development support, and disciplined incident response, while also encouraging a broader understanding of diverse database technologies and core relational concepts.
This article chronicles the 30‑year dominance of relational databases, the challenges posed by object‑oriented and high‑concurrency workloads, and how four alternative data‑store families—Redis, MongoDB, Neo4j, and HBase/Cassandra—gave birth to the modern NoSQL movement.
The article explores MySQL's Hash Join algorithms, comparing Basic, On-disk, Grace, and Hybrid variants, while detailing rule-based optimizer decisions, memory management, disk spilling mechanisms, and outer join execution strategies through source code analysis.
This article traces the fifty‑year history of relational databases, from early storage systems and the IDS and CODASYL network model to Codd’s relational model, the rise of SQL, its standards, competitors, and the principles that have kept SQL dominant in modern data management.
The 2020 DB-Engines ranking, based on search engine visibility, developer discussions, job postings, professional platform usage, and social media mentions, ranks 363 databases, highlighting Oracle, MySQL and SQL Server as the dominant relational leaders while showcasing the rapid rise of various NoSQL categories.
This article explains how to evaluate business architecture, data volume, data types, and team expertise to select the most suitable database—whether relational with strong ACID guarantees for OLTP, analytical OLAP systems, or various NoSQL options—while highlighting Huawei Cloud offerings for different scenarios.
This article walks through the complete lifecycle of a SQL SELECT statement—using simple Citizen and City tables—to illustrate the six processing stages (FROM/JOIN, WHERE, GROUP BY, HAVING, SELECT, ORDER BY/LIMIT), showing how each clause transforms the data step by step.
This article reviews the historical evolution, market size, and technical advantages of relational databases, explains the shift from centralized to distributed architectures, and highlights OceanBase's recent breakthroughs and community events as a modern distributed relational solution.
The article examines the 2019 DB-Engines ranking that crowned MySQL as the DBMS of the Year, discusses its historical development, compares it with Oracle and SQL Server, analyzes the rise of NoSQL systems, and explores the implications of hybrid features in modern relational databases.
This article explains the six relational database normal forms, illustrates how to identify entities, attributes, keys, and prime versus non‑prime attributes, and walks through step‑by‑step examples that transform a denormalized employee‑performance table into 1NF, 2NF, and 3NF compliant structures while addressing redundancy and anomalies.
The 2019 DB-Engines ranking crowned MySQL as the most popular DBMS, with Oracle and Microsoft SQL Server following, and discusses how relational databases dominate while NoSQL systems remain vital, highlighting evolving features and the implications for future database technology choices.
This article introduces cloud database services, compares relational and non‑relational options, outlines traditional database challenges, highlights the key benefits of cloud databases, explains high‑availability MySQL and read‑write separation architectures, and lists common industry use cases.
This article explains the inner workings of relational databases, covering their architecture, indexing, time‑complexity analysis, query optimization, merge sort, client and query managers, caching strategies, transaction handling, ACID properties, concurrency control, and lock management.
Former MariaDB CTO Ivan Zoratti shares his 35‑year tech career, the evolution from MySQL to MariaDB, why he transitioned to Neo4j, and his insights on graph versus relational databases, highlighting the innovations and future potential of graph technology.
This article traces the evolution of relational databases, explains the rise of cloud‑native computing, and details how Alibaba Cloud’s PolarDB combines storage‑compute separation, RDMA networking, shared‑disk architecture, and advanced replication techniques to deliver high‑performance, scalable, and cost‑effective database services.
The article lists eight common frustrations with MySQL—from deep‑rooted bugs and rigid relational schemas to confusing storage engines and missing native JSON support—explaining why these issues still trouble developers despite MySQL’s popularity.
The article lists eight common complaints about MySQL—from deep‑rooted bugs and inflexible relational schemas to confusing forks, storage‑engine chaos, profit‑driven licensing, weak native JSON support, and proprietary extensions—highlighting why many developers consider alternatives.
The article examines common misconceptions about Hadoop, compares it with relational databases, and shares Facebook's data‑analysis practices, highlighting when Hadoop is appropriate and the broader considerations of using open‑source big‑data frameworks.
The article enumerates eight fundamental issues with MySQL—including deep‑seated bugs, rigid relational schemas, complex JOIN handling, confusing branch and storage‑engine choices, profit‑driven licensing, lack of native JSON support, and proprietary modules—while urging developers to consider more flexible alternatives.
