# nosql Published articles for nosql. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Design Nested Documents for a Blogging App DevFeed: [How to Design Nested Documents for a Blogging App]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2026-05-22T12:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [MongoDB](), [NoSQL](), [data-modeling](), [Content Management System]() Tags: [cms](), [data-modeling](), [database](), [how-to](), [mongodb](), [nosql]() ### AI overview A tutorial on designing nested documents for a blogging application in MongoDB. It examines document modeling choices for authors, blog posts, and comments, and explains why modeling these relationships as separate documents may not provide the best MongoDB experience. ### Source excerpt So you want to build your own content management system (CMS), also sometimes known as a blog? This is a classic example when learning how to use a database, whether it be a relational database manage... The post How to Design Nested Documents for a Blogging App appeared first on DataCamp. ## Building a REST API With Express Framework and MongoDB DevFeed: [Building a REST API With Express Framework and MongoDB]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2026-05-08T12:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [Express](), [MongoDB](), [REST API](), [Tutorial](), [Node.js](), [JavaScript](), [Cross-origin resource sharing (CORS)](), [.env](), [JSON]() Tags: [api](), [code](), [cors](), [database](), [development](), [express](), [javascript](), [json](), [mongodb](), [node-js](), [nosql](), [rest](), [rest-api](), [tutorial]() ### AI overview A tutorial on building a REST API with Express Framework, MongoDB, and Node.js. It explains how JSON flows between clients, application logic, and MongoDB, then covers prerequisites, dependencies, CORS, environment configuration, and project structure. ### Source excerpt Almost every modern web application will need a REST API for the frontend to communicate with, and in almost every scenario, that frontend is going to expect to work with JSON data. As a result, the b... The post Building a REST API With Express Framework and MongoDB appeared first on Hevo. ## Firestore adds generally available pipeline operations with full-text search, JOINs, geospatial queries, and data manipulation DevFeed: [Firestore adds generally available pipeline operations with full-text search, JOINs, geospatial queries, and data manipulation]() Original publisher: [Read original article]() Author: Morgan Chen; Minh Nguyen Published: 2026-04-27T00:00:00Z Content type: release Language: en Sources: [Firebase Blog]() Topics: [Firestore](), [NoSQL](), [Database](), [Serverless](), [serverless architecture]() Tags: [database](), [firebase](), [firestore](), [full-text-search](), [geo-queries](), [geospatial](), [javascript](), [nosql](), [real-time](), [serverless](), [serverless-architecture]() ### AI overview Firestore pipeline operations reached general availability on the enterprise edition, adding full-text search, geospatial queries, JOIN capabilities through subqueries, and data manipulation while retaining Firestore's NoSQL and serverless features. ### Source excerpt With support for full-text search, joins, and geospatial queries, Firestore is ready to meet all of your app's querying needs. ## Build a Movie Watchlist with Node.js, TypeScript, and MongoDB DevFeed: [Build a Movie Watchlist with Node.js, TypeScript, and MongoDB]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2026-04-13T13:37:28Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [MongoDB](), [Node.js](), [TypeScript](), [Express](), [REST API](), [API](), [Development](), [web applications]() Tags: [api](), [configuration](), [express](), [how-to](), [javascript](), [json](), [mongodb](), [node-js](), [nosql](), [rest-api](), [tutorial](), [typescript]() ### AI overview A tutorial that builds a small movie watchlist REST API with Node.js, TypeScript, Express, and MongoDB. It covers project setup, prerequisites, schema design, MongoDB Search for full-text search, configuration, and development commands. ### Source excerpt Almost every modern web application will need a REST API for a client to talk to, and in almost every scenario, that client is going to expect JSON. The best developer experience is a stack where you ... The post Build a Movie Watchlist with Node.js, TypeScript, and MongoDB appeared first on DEV. ## Zero downtime Upgrade: Yelp's Cassandra 4.x Upgrade Story DevFeed: [Zero downtime Upgrade: Yelp's Cassandra 4.x Upgrade Story]() Original publisher: [Read original article]() Author: Mark Surnin and Muhammad Junaid Muzammil, Software Engineer Published: 2026-04-07T00:00:00Z Content type: article Language: en Sources: [Yelp]() Topics: [Apache Cassandra](), [upgrade](), [NoSQL](), [Database](), [observability](), [Kubernetes]() Tags: [2026](), [apache](), [cassandra](), [clusters](), [downtime](), [kubernetes](), [nosql](), [observability](), [reliability-engineering](), [upgrade]() ### AI overview Yelp's Database Reliability Engineering team describes upgrading more than a thousand Cassandra nodes from 3.11 to 4.1 on Kubernetes without downtime. The article covers the motivation, expected reliability and performance improvements, operational guardrails, certificate handling, repairs, logging, and compatibility work for related components. ### Source excerpt The Database Reliability Engineering team at Yelp seamlessly upgraded more than a thousand Cassandra nodes with zero downtime. This post takes you behind the scenes of our upgrade strategy, from planning sessions to flawless rollouts. Background Motivation Apache Cassandra is a distributed wide-column NoSQL datastore and is used widely at Yelp for storing both primary and derived data. Yelp orchestrates Cassandra clusters on Kubernetes with the help of operators, as explained in our operator overview post. Upgrading from Cassandra 3.11 to 4.1 offered several observability and reliability improvements, in addition to performance gains. Based on public benchmarks, we expected to... ## 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. ## Why FinTech Is Moving to Distributed SQL Databases DevFeed: [Why FinTech Is Moving to Distributed SQL Databases]() Original publisher: [Read original article]() Author: David Weiss Published: 2025-12-12T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Databases](), [SQL](), [systems](), [Transactions](), [consistency](), [Resilience](), [Availability]() Tags: [acid](), [availability](), [compliance](), [consistency](), [databases](), [distributed](), [fintech](), [global](), [legacy](), [nosql](), [payments](), [resilience](), [sql](), [trading](), [transactions]() ### AI overview This article explains why FinTech organizations are considering distributed SQL databases as they scale financial workloads across regions and clouds. It emphasizes correctness, consistent ACID transactions, resilience, availability, regulatory compliance, and the limitations of legacy relational and patchwork NoSQL systems. ### Source excerpt FinTech teams are operating under more pressure than ever. As digital financial services become the default for consumers and enterprises alike, the margin for error has all but disappeared. Users expect every interaction to be instantaneous, accurate, and always available, with very little patience for anything less. ## KVS: The key-value storage engine that powers the Fury ecosystem DevFeed: [KVS: The key-value storage engine that powers the Fury ecosystem]() Original publisher: [Read original article]() Author: Ariel Zach Published: 2025-10-01T14:09:08Z Content type: article Language: en Sources: [Mercado Libre Tech]() Topics: [NoSQL](), [Persistence](), [Scalability](), [Low Latency](), [Availability](), [Data Management]() Tags: [architecture](), [availability](), [data](), [database](), [databases](), [development](), [distributed](), [fury](), [infrastructure](), [key-value-store](), [kvs](), [low-latency](), [nosql](), [persistence](), [resilience](), [scalability](), [security](), [storage]() ### AI overview This article explains KVS, a key-value storage system in the Fury ecosystem. It describes how unique key-value pairs enable fast access, and reports that KVS supports large-scale distributed persistence with automatic scaling, low latency, availability, and recovery mechanisms. ### Source excerpt In modern software development, the efficiency, scalability, and resilience of data storage systems are critical to any platform's success. In this context, KVS (Key-Value Store) emerges as a fundamental component within the Fury ecosystem, offering a robust and flexible solution for data management. This article explores what KVS is, how it works, and why it's so important for Fury and its users. What is KVS? KVS, or Key-Value Store, is a data storage system based on the key-value paradigm. Unlike traditional relational databases, which organize data in tables and rows, KVS stores each data element as a unique key-value pair. This approach simplifies access and manipulation of information, enabling extremely fast and efficient operations. In Fury, KVS isn't just another database -- it's the fundamental engine that drives distributed, scalable, and fault-tolerant data persistence and retrieval. Currently, KVS processes over 642 million operations per minute, including 572 million reads and 70 million writes. The underlying infrastructure scales automatically and maintains low latency even under high-demand scenarios. The service is used by more than 7,600 applications within Mercado Libre (around 25% of the total), making it a key component of the ecosystem. Overall, it stores over 9 petabytes of data with mechanisms that ensure availability and recovery in case of failures. How does KVS work? Each piece of data is stored under a unique key. To retrieve or modify a value, you only need to know its key, eliminating the need for complex queries and relationships. This speeds up both read and write operations. KVS is built on top of the Fury ecosystem, which provides key guarantees such as scalability, security, and traffic control. It uses an architecture based on a fully managed NoSQL solution, designed to automatically adapt to demand. Thanks to this infrastructure, KVS inherits advanced scalability capabilities, allowing it to grow automatically and transparently, c ## Migrating Your Content Management System (CMS) Assets With MongoDB and Node.js DevFeed: [Migrating Your Content Management System (CMS) Assets With MongoDB and Node.js]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2025-05-09T12:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [Content Management System](), [MongoDB](), [Node.js](), [migration](), [Development](), [axios](), [Environment Variables]() Tags: [axios](), [cms](), [developer](), [environment-variables](), [javascript](), [migration](), [mongodb](), [node-js](), [nosql](), [tutorial]() ### AI overview A tutorial explaining how to migrate content from a MongoDB-based content management system to external publishing platforms. It covers exporting written content and downloading media assets from third-party servers using Node.js, MongoDB, Axios, and environment variables. ### Source excerpt Content platforms evolve as business strategies shift. At MongoDB, we embraced external publishing platforms like Dev.to, Medium, The Polyglot Developer, etc. to better engage developer communities, r... The post Migrating Your Content Management System (CMS) Assets With MongoDB and Node.js appeared first on DEV. ## Scaling Nextdoor's Datastores: Part 1 DevFeed: [Scaling Nextdoor's Datastores: Part 1]() Original publisher: [Read original article]() Author: Slava Markeyev Published: 2025-03-19T15:08:13Z Content type: article Language: en Sources: [Nextdoor]() Topics: [Scalability](), [Databases](), [Caching](), [Back end](), [Django](), [Python](), [PostgreSQL](), [NoSQL]() Tags: [backend](), [caching](), [database-consistency](), [database-scalability](), [databases](), [development](), [django](), [nosql](), [postgresql](), [python](), [rdbms](), [scalability]() ### AI overview This introductory post in a Nextdoor blog series examines scalability challenges involving database load and cache consistency. It reviews common industry solutions, their caveats, and the difficulty of moving from entrenched relational data models to NoSQL or distributed SQL datastores. ### Source excerpt At Nextdoor, the Core-Services team is responsible for the primary set of databases and caches that power the Nextdoor platform. This blog series explores our 2024 initiatives to enhance the scalability of this critical infrastructure. When we sat down at the whiteboard we sought to address two related problems: How can we reduce load on our primary database(s) and better utilize database read replicas? How can we improve our cache consistency? In this post we'll provide a primer on the common industry-wide solutions we've previously employed along with discussing their caveats and pitfalls. In subsequent posts we'll dive into the technical details of the components of our solution and how they fit together. Table of Contents Background primer (this post) Decreasing database load with dynamic routing Appropriately serializing data for caching Keeping the cache consistent A time-bounded, eventually-consistent cache Background Nextdoor's backend, built using the Python-based Django web framework, powers the core product experience for neighbors, government agencies, and local businesses. The power of Django and similar frameworks (Rails, Spring, etc) is that they allow development teams to focus on implementing business logic rather than getting caught up in the details like learning and writing SQL. The Object Relational Mapping, ORMs, included in these frameworks provide a lever that allows developers to define data models and relationships between them in the application's language without ever needing to worry about SQL. As some readers are all too aware, relational data modeling comes at a cost. Without careful data modeling, performant access to relational data largely depends on that data residing on monolithic databases. NoSQL or distributed SQL datastores are often advertised as solutions to the scalability challenges of relational databases like PostgreSQL. However, many companies face significant obstacles in transitioning to these modern datastores. Their ## New Chainguard Images December 2024: Adoptium, AWX, CouchDB DevFeed: [New Chainguard Images December 2024: Adoptium, AWX, CouchDB]() Original publisher: [Read original article]() Published: 2025-01-16T00:00:00Z Content type: release Language: en Sources: [Chainguard: Unchained]() Topics: [Adoptium](), [openjdk](), [Ansible](), [Java](), [NoSQL](), [Open Source](), [Erlang](), [.NET MAUI](), [Process]() Tags: [adoptium](), [ansible](), [apache](), [awx](), [chainguard](), [chainguard-images](), [container-images](), [couchdb](), [erlang](), [helm](), [java](), [jdk](), [linkerd](), [net-11](), [nosql](), [open-source](), [opentofu]() ### AI overview Chainguard describes its December 2024 batch of 39 new Chainguard Images, highlighting images for Adoptium, AWX, and Apache CouchDB. The article notes FIPS-enabled variants and summarizes the intended uses and image characteristics. ### Source excerpt Chainguard released several new images in December 2024, including images for Adoptium, AWX, CouchDB, and others. Check out the full list. ## DynamoDB For System Design Interviews DevFeed: [DynamoDB For System Design Interviews]() Original publisher: [Read original article]() Author: Evan King Published: 2024-11-19T21:30:02Z Content type: tutorial Language: en Sources: [Evan King]() Topics: [DynamoDB](), [Amazon Web Services](), [NoSQL](), [Database](), [Transactions](), [Provisioning](), [Application Development](), [configuration](), [Learning](), [vendor lock-in]() Tags: [aws](), [configuration](), [data-storage](), [database](), [dynamodb](), [learning](), [nosql](), [provisioning](), [system-design-interview](), [transactions]() ### AI overview This tutorial explains DynamoDB for system design interviews. It covers DynamoDB's managed operation, scalability, key-value and NoSQL data model, transaction support, lack of open-source internals, and practical considerations including vendor lock-in and whether interviewers permit its use. ### Source excerpt By the Co-founder of www.hellointerview.comIntro DynamoDB is a fully-managed, highly scalable, key-value service provided by AWS. Cool, buzz-words. But what the hell does that mean and why does it matter? Fully-Managed -- This means that AWS takes care of all the operational aspects of the database. The fully-managed nature allows AWS to handle all operational tasks -- hardware provisioning, configuration, patching, and scaling -- freeing developers to concentrate on application development. Highly Scalable -- DynamoDB can handle massive amounts of data and traffic. It automatically scales up or down to adjust to your application's needs, without any downtime or performance degradation. Key-value -- DynamoDB is a NoSQL database, which means it doesn't use the traditional relational database model. Instead, it uses a key-value model that allows for flexible data storage and retrieval. The moral of the story is that DynamoDB is a super easy to use and can scale to support a wide variety of applications. For system design interviews in particular, it has just about everything you'd ever need from a database. It even supports transactions now! Which neutralizes one of the biggest criticisms of DynamoDB in the past. Importantly, DynamoDB is not open-source, so we can't as easily describe its internals like we did with breakdowns of open source technologies like Kafka and Redis. Instead, we'll focus more on you interact with it. In order to look under the hood, we'll rely on the limited information AWS provides via documentation and the DynamoDB Whitepaper. In this deep dive, we'll break down exactly what you need to know about DynamoDB in order to field any question about it in a system design interview. Along the way, you'll also acquire practical learning that you can later apply in your own projects. Let's break it down! Candidates often ask me, "am I even allowed to use DynamoDB in an interview?"The answer is simple, ask your interviewer! Many will say yes, and just expec ## Query DynamoDB tables with SQL DevFeed: [Query DynamoDB tables with SQL]() Original publisher: [Read original article]() Author: Cameron Archer Published: 2024-10-30T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [DynamoDB](), [SQL](), [NoSQL]() Tags: [dynamodb](), [nosql](), [sql](), [the-data-base]() ### AI overview The article presents querying DynamoDB tables with SQL and says Tinybird makes NoSQL data accessible to people who know SQL, avoiding PartiQL. ### Source excerpt Query DynamoDB with SQL instead of fighting with PartiQL. Tinybird makes your NoSQL data accessible to anyone who knows SQL. ## How Discord Processes 30+ Petabytes of Data DevFeed: [How Discord Processes 30+ Petabytes of Data]() Original publisher: [Read original article]() Author: Richard Oliver Bray Published: 2024-09-25T13:02:10Z Content type: article Language: en Sources: [Hacking Scale by Better Stack]() Topics: [Discord](), [data](), [scylladb](), [NoSQL](), [C++](), [Machine learning]() Tags: [c-plus-plus](), [data](), [discord](), [nosql](), [scylladb]() ### AI overview This article explains how Discord analyzes billions of daily messages and manages large-scale data processing. It describes message storage in ScyllaDB, data cleaning, machine-learning use cases, and Discord's development of Derived to automate data transformation for analysis. ### Source excerpt Discord's genius approach to automating insights from billions of messages ## SQL vs NoSQL Explained DevFeed: [SQL vs NoSQL Explained]() Original publisher: [Read original article]() Author: Nelson Djalo Published: 2024-07-02T16:00:57Z Content type: comparison Language: en Sources: [Amigoscode Newsletter]() Topics: [NoSQL](), [SQL](), [Databases](), [clickhouse]() Tags: [acid](), [business-intelligence](), [consistency](), [data-analysis](), [database](), [database-scalability](), [durability](), [microsoft](), [mysql](), [nosql](), [olap](), [oracle](), [postgresql](), [rdbms](), [relational-database-management-systems-rdbms](), [relational-databases](), [schema](), [sql]() ### AI overview This comparison explains the differences between SQL and NoSQL databases, including their characteristics, scalability models, and common use cases. It covers SQL database features such as structured schemas and ACID compliance, along with relational and OLAP database categories. ### Source excerpt Choosing the Right Database for Your Needs ## NoSQL Postgres: Add MongoDB compatibility to your Supabase projects with FerretDB DevFeed: [NoSQL Postgres: Add MongoDB compatibility to your Supabase projects with FerretDB]() Original publisher: [Read original article]() Author: Thor Schaeff Published: 2024-01-31T07:00:00Z Content type: tutorial Language: en Sources: [Supabase Blog]() Topics: [MongoDB](), [Supabase](), [NoSQL](), [Docker](), [fly.io](), [Node.js]() Tags: [docker](), [fly-io](), [mongodb](), [node-js](), [nosql](), [postgres]() ### AI overview This tutorial explains how to use FerretDB to add MongoDB compatibility to Postgres-backed Supabase projects. It covers running FerretDB locally with Docker, connecting with MongoDB tools and the Node.js client, performing basic document operations, and deploying FerretDB to Fly.io. ### Source excerpt NoSQL Postgres: Add MongoDB compatibility to your Supabase projects with FerretDB ## How Apple built iCloud to store billions of databases DevFeed: [How Apple built iCloud to store billions of databases]() Original publisher: [Read original article]() Author: Engineer's Codex Published: 2024-01-14T21:53:05Z Content type: article Language: en Sources: [Engineer's Codex]() Topics: [Apache Cassandra](), [Database](), [Multitenancy](), [Cloud](), [NoSQL](), [Architecture & Design](), [Scalability]() Tags: [architecture](), [cassandra](), [cloud](), [database](), [nosql](), [scalability](), [separate]() ### AI overview This article examines how Apple built iCloud and CloudKit using FoundationDB and Cassandra. It discusses the systems' extreme multi-tenant architecture and engineering choices such as asynchronous processing, stateless design, resource isolation, and layered abstractions to support scalability, reliability, availability, and developer experience. ### Source excerpt Apple uses Cassandra and FoundationDB for CloudKit, their cloud backend service. We take a look into how exactly each is used within their cloud and the problems they've solved. ## NoSQL's Role Evolves as SQL and SQLite Gain Favor for Modern Web Workloads DevFeed: [NoSQL's Role Evolves as SQL and SQLite Gain Favor for Modern Web Workloads]() Original publisher: [Read original article]() Author: Glauber Costa Published: 2023-12-21T00:00:00Z Content type: article Language: en Sources: [Turso Blog]() Topics: [NoSQL](), [SQL](), [SQLite](), [Web]() Tags: [nosql](), [sql](), [sqlite](), [web]() ### AI overview The article argues that SQL databases, especially SQLite, are regaining popularity for modern web workloads because hardware improvements and simpler usage have changed the trade-offs that previously favored NoSQL. It also notes that specialized NoSQL systems continue to serve particular purposes. ### Source excerpt NoSQL's role evolves as SQL, especially SQLite, gains favor for modern web workloads due to hardware advances and ease of use. ## DynamoDB Transactions: An E-Commerce with Amazon DynamoDB DevFeed: [DynamoDB Transactions: An E-Commerce with Amazon DynamoDB]() Original publisher: [Read original article]() Author: Guillermo Ojeda Published: 2023-11-09T18:42:01Z Content type: tutorial Language: en Sources: [Guille Ojeda]() Topics: [Amazon DynamoDB](), [Database](), [NoSQL](), [Amazon Web Services](), [Back end]() Tags: [acid](), [amazon-dynamodb](), [amazon-web-services](), [architecture](), [aws](), [backend](), [customers](), [database](), [databases](), [dynamodb](), [e-commerce](), [nosql](), [operations](), [products]() ### AI overview This tutorial explains how to use DynamoDB transactions in an e-commerce application to atomically verify product stock, decrement inventory, and create an order. It describes the race condition that can occur when separate read and write operations process simultaneous purchases. ### Source excerpt We're building an e-commerce app with DynamoDB for the database, pretty similar to the one we built for the DynamoDB Database Design article. No need to go read that issue (though I think it came up great), here's how our database works: Customers a... ## Understanding How DynamoDB Scales DevFeed: [Understanding How DynamoDB Scales]() Original publisher: [Read original article]() Author: Guillermo Ojeda Published: 2023-10-19T14:40:53Z Content type: tutorial Language: en Sources: [Guille Ojeda]() Topics: [NoSQL](), [Database](), [Serverless](), [Availability](), [Resilience](), [Transactions](), [Provisioning]() Tags: [architecture](), [availability](), [aws](), [databases](), [distributed](), [dynamodb](), [nosql](), [partition](), [provisioning](), [resilience](), [serverless](), [transactions]() ### AI overview This tutorial explains how DynamoDB scales horizontally through partitioning and replication across nodes. It compares provisioned capacity for reads and writes and explains read and write capacity units, consistency, data size limits, and the doubled capacity use of transactions. ### Source excerpt Note: This content was originally published at the Simple AWS newsletter. As you probably know, DynamoDB is a NoSQL database. It's a managed, serverless service, meaning you just create a Table (that's the equivalent of a Database in Postgres), and A... ## Serverless Development with Kotlin, AWS Lambda, and MongoDB Atlas DevFeed: [Serverless Development with Kotlin, AWS Lambda, and MongoDB Atlas]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2023-08-08T14:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [Kotlin](), [AWS Lambda](), [MongoDB](), [Amazon Web Services](), [Serverless](), [Gradle](), [IntelliJ IDEA]() Tags: [aws](), [aws-lambda](), [development](), [gradle](), [intellij](), [kotlin](), [mongodb](), [nosql](), [serverless](), [tutorial]() ### AI overview This tutorial explains how to build a serverless AWS Lambda function in Kotlin that interacts with MongoDB using the MongoDB Kotlin driver. It covers prerequisites, project configuration with Gradle Kotlin DSL, and packaging the application for Lambda. ### Source excerpt As seen in a previous tutorial, creating a serverless function for AWS Lambda with Java and MongoDB isn't too complicated of a task. In fact, you can get it done with around 35 lines of code! However,... The post Serverless Development with Kotlin, AWS Lambda, and MongoDB Atlas appeared first on MongoDB. ## Serverless Development with AWS Lambda and MongoDB Atlas Using Java DevFeed: [Serverless Development with AWS Lambda and MongoDB Atlas Using Java]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2023-07-23T14:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [AWS Lambda](), [MongoDB](), [Java](), [Serverless](), [Development](), [Gradle](), [Cloud]() Tags: [amazon-web-services](), [aws](), [aws-lambda](), [gradle](), [java](), [mongodb](), [nosql](), [serverless](), [tutorial]() ### AI overview This tutorial explains how to build a serverless Java function with AWS Lambda and connect it to MongoDB Atlas. It covers prerequisites, Gradle or Maven dependency management, Lambda boilerplate, packaging, and the initial MongoDB driver setup. ### Source excerpt So you need to build an application that will scale with demand and a database to scale with it? It might make sense to explore serverless functions, like those offered by AWS Lambda, and a cloud data... The post Serverless Development with AWS Lambda and MongoDB Atlas Using Java appeared first on MongoDB. ## Local Development with the MongoDB Atlas CLI and Docker DevFeed: [Local Development with the MongoDB Atlas CLI and Docker]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2023-04-04T14:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [MongoDB](), [Docker](), [Command-line interface](), [Containers](), [Development](), [Deployment](), [Node.js](), [Express](), [web applications](), [API](), [Database](), [Tutorial]() Tags: [api](), [application-development](), [cli](), [code](), [containers](), [deployment](), [developers](), [docker](), [express](), [how-to](), [http](), [javascript](), [mongodb](), [node-js](), [nosql](), [tutorial](), [web]() ### AI overview This tutorial shows how to build a MongoDB-compatible Node.js web application, package it with Docker, and use the MongoDB Atlas CLI to create and destroy Atlas resources during container deployment. It is intended for local development or staging, not production. ### Source excerpt Need a consistent development and deployment experience as developers work across teams and use different machines for their daily tasks? That is where Docker has you covered with containers. A common... The post Local Development with the MongoDB Atlas CLI and Docker appeared first on MongoDB. ## Getting Started with MongoDB Atlas and Azure Functions using .NET and C# DevFeed: [Getting Started with MongoDB Atlas and Azure Functions using .NET and C#]() Original publisher: [Read original article]() Author: Nic Raboy Published: 2023-02-17T14:00:00Z Content type: tutorial Language: en Sources: [Nic Raboy]() Topics: [azure functions](), [MongoDB](), [.NET](), [Serverless](), [Command-line interface](), [Application Development]() Tags: [application-development](), [azure](), [azure-functions](), [c-sharp](), [cli](), [csharp](), [database](), [development](), [how-to](), [mongodb](), [net](), [nosql](), [serverless]() ### AI overview This tutorial explains how to create and locally test an Azure Functions serverless application using the .NET runtime and C# to interact with a MongoDB Atlas database, then prepare it for publishing to Azure. It covers prerequisites, Azure CLI and Azure Functions Core Tools, and creating the function project. ### Source excerpt So you need to build an application with minimal operating costs that can also scale to meet the growing demand of your business. This is a perfect scenario for a serverless function, like those built... The post Getting Started with MongoDB Atlas and Azure Functions using .NET and C# appeared first on Microsoft. [Next page]()