If you're a Software Developer, you should understand why 𝐌𝐨𝐧𝐠𝐨𝐃𝐁 is the Go-To Database for modern apps. 👇 𝐃𝐨 𝐲𝐨𝐮 𝐤𝐧𝐨𝐰 𝐰𝐡𝐚𝐭? 𝐌𝐨𝐧𝐠𝐨𝐃𝐁 𝐰𝐚𝐬 𝐜𝐫𝐞𝐚𝐭𝐞𝐝 𝐨𝐮𝐭 𝐨𝐟 𝐧𝐞𝐜𝐞𝐬𝐬𝐢𝐭𝐲. 😉 The founders faced challenges building large-scale web applications with existing databases. As the internet grew, with more dynamic websites and apps, the old database tools couldn't keep up. MongoDB was designed to fill this gap. It offered the flexibility, scalability and ease of use that developers needed for the new web. MongoDB is primarily written in C++, but utilizes JavaScript for its shell and Python for some tools and drivers. 📌 𝐋𝐞𝐭'𝐬 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 - 𝐌𝐨𝐧𝐠𝐨𝐃𝐁 ◾ At its core, MongoDB => NoSQL, document-oriented database. ◾ Traditional relational databases => store data in tables and rows But, ◾ MongoDB => 'internally' => stores data in a binary format called BSON (Binary JSON). ◾ BSON is a superset of JSON. ◾ It can represent everything JSON can, plus additional data types like dates and binary data. ◾ BSON is more compact than JSON, reducing storage space and improving performance. ◾ This structure allows for a more natural representation of real-world objects and relationships. 📌 𝐋𝐞𝐭'𝐬 𝐛𝐫𝐞𝐚𝐤 𝐝𝐨𝐰𝐧 𝐌𝐨𝐧𝐠𝐨𝐃𝐁'𝐬 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 [1.] Document Model Flexible, JSON-like documents store data, enabling varied structures and nested objects. [2.] Collections Groups of documents, analogous to tables but schema-less, offering adaptable organization. [3.] mongod Core process handling storage, queries and administration. (primary daemon process) [4.] mongos (if sharded) Routes queries in a distributed cluster to the correct data partitions (appropriate shards). [5.] Replica Sets Multiple nodes maintain data copies, ensuring high availability and fault tolerance. [6.] Sharding Horizontal scaling by distributing data across multiple servers for large datasets and high traffic. [7.] Indexes Data structures to speed up queries, supporting various types for different access patterns. [8.] WiredTiger Default storage engine, optimized for memory and disk usage with concurrency control. [9.] Aggregation Framework Powerful pipeline-based framework for complex data analysis and transformation. [10.] Query Language Expressive language for filtering, sorting, projecting, and aggregating data. 📌 𝐒𝐨𝐦𝐞 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 1. Content Management 2. Real-Time Analytics 3. Internet of Things (IoT) 4. Mobile Apps 5. Product Catalogs 📌 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 1. Transactions Uses a 'snapshot isolation' model, which can have implications for applications that require strict ACID guarantees. 2. Data Modeling Flexibility demands thoughtful design. 3. Joins MongoDB does not support traditional SQL joins. 4. Operational Overhead MongoDB can require more operational expertise. 5. Cost MongoDB can be expensive to run, especially at scale. Follow - @techNmak

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⚠️ Visual Representation: git pull & git push