# NoSQL Databases Published articles for NoSQL Databases. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Different Types of Database Management Systems DevFeed: [Different Types of Database Management Systems]() Original publisher: [Read original article]() Author: Sumit Malviya Published: 2026-03-20T10:30:23Z Content type: article Language: en Sources: [Backend Archives | Talent500 blog]() Topics: [Databases](), [Database](), [data](), [Software](), [Scalability](), [NoSQL](), [MongoDB](), [MySQL](), [Oracle Database](), [sql-server]() Tags: [applications](), [backend](), [backup](), [big-data-and-dbms](), [choosing-the-right-dbms-for-your-needs](), [cloud-based-dbms](), [cloud-native](), [comparison-of-dbms-types](), [conclusion](), [consistency](), [data](), [database](), [definition-and-importance-of-dbms](), [different-types-of-database-management-systems](), [emerging-types-in-database-management-systems](), [evolution](), [evolution-of-database-management-systems](), [examples](), [hierarchical-database-management-systems](), [industries](), [integrity](), [management](), [mysql](), [network-database-management-systems](), [nosql](), [nosql-databases](), [object-oriented-database-management-systems-oodbms](), [recovery](), [relational-database-management-systems-rdbms](), [relational-databases](), [scalability](), [systems](), [types](), [types-of-database-management-systems]() ### AI overview This article explains what database management systems are, why they matter, how they support storage, queries, security, integrity, backup, and recovery, and how major DBMS categories have evolved. It also introduces examples including MySQL, Oracle Database, Microsoft SQL Server, and MongoDB. ### Source excerpt Data is one of the most valuable assets for organizations in this AI-powered digital era. From banking systems and e-commerce [...] The post Different Types of Database Management Systems appeared first on Talent500 blog. ## Firestore introduces Pipeline operations and an advanced query engine DevFeed: [Firestore introduces Pipeline operations and an advanced query engine]() Original publisher: [Read original article]() Author: Morgan Chen; Tyler Crowe; Minh Nguyen Published: 2026-01-15T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firestore](), [Databases](), [NoSQL](), [Google](), [SDKs]() Tags: [admin-sdk](), [databases](), [firebase](), [firestore](), [javascript](), [nosql-databases](), [offline](), [pipeline](), [real-time](), [sdks]() ### AI overview Firestore announces Pipeline operations and a redesigned query engine for its Enterprise edition. The update adds chained query stages, aggregations, map operations, regular-expression string matching, and optional indexes. ### Source excerpt With a new advanced query engine and querying methods in the SDKs, Firestore is more powerful than ever. ## Eventual Consistency and Durability DevFeed: [Eventual Consistency and Durability]() Original publisher: [Read original article]() Author: Nicolas Liochon (noreply@blogger.com) Published: 2015-05-12T15:51:00Z Content type: article Language: en Sources: [Nicolas Liochon]() Topics: [NoSQL](), [Databases](), [data]() Tags: [acid](), [availability](), [cap-theorem](), [consistency](), [data](), [database](), [databases](), [distributed-system](), [durability](), [latency](), [network](), [nosql](), [nosql-databases](), [partition](), [storage]() ### AI overview The article explains eventual consistency in NoSQL databases and distinguishes it from ACID consistency. It discusses durability, defines eventual consistency using cited formulations, and introduces a replicated key-value database model using N, W, and R parameters. ### Source excerpt "A son can bear with equanimity the loss of his father, but the loss of his data may drive him to despair." (Machiavelli, quoted from memory) While traditional databases are ACID, with the 'D' meaning Durability, the NoSQL databases are mainly described by their memory model, i.e. Strong Consistency vs. Eventual Consistency. We're going to see that durability is not something you can forget about so easily. Definition of Eventually Consistent Eventual Consistency was defined by Werner Vogels in [E2] "the storage system guarantees that if no new updates are made to the object, eventually all accesses will return the last updated value." It's one definition among many. Baillis and Ghodsi mention for example [E3] "Informally, it guarantees that, if no additional updates are made to a given data item, all reads to that item will eventually return the same value." This definition only says that the value will converge to something (say '42'). In 1995, Terry used [V5] "All servers move towards eventual consistency. That is, the Bayou system guarantees that all servers eventually receive all Writes.". As most implementations available today are inspired by the Dynamo paper and Vogels' works, it's safer to stick to Vogels' definition, who knew them all, and has chosen his words carefully. In the CAP series, I explained the importance of this definition. And finally, this consistency is not the same thing as the consistency in ACID. Using an eventually consistent database So if we have an eventually consistent key-value database, we can implement a client that would be doing something like this for example: ecStorage.put("anId", "someData"); while (ecStorage.get("anId") == null) sleep 1 second; We know that this program will finish: even if the first "gets" may not see our write, eventually they will see it. Eventually Consistent database - possible implementation The most usual implementation is the one described once again by Werner Vogels, once again in his [E2] paper: N