New MongoDB books for better building

Amazon Aurora PostgreSQL Limitless Database is now available in the AWS GovCloud (US-East, US-West) Regions - Aurora PostgreSQL Limitless Database, now available in AWS GovCloud (US-East, US-West) Regions, makes it easy for you to scale your relational database workloads by providing a serverless endpoint that automatically distributes data and queries across multiple Amazon Aurora Serverless instances while… https://lnkd.in/eS8Uyiks

Think scaling MongoDB is hard? MongoDB Atlas makes vertical scaling automatic. Need more? Scale across multiple nodes with options based on how your data behaves. Scaling is supposed to be simple—and with MongoDB, it is. Want more myths cleared up? Check this playlist. 👇 https://lnkd.in/ddxRbfB6

Think scaling MongoDB is hard? MongoDB Atlas makes vertical scaling automatic. Need more? Scale across multiple nodes with options based on how your data behaves. Scaling is supposed to be simple—and with MongoDB, it is. Want more myths cleared up? Check this playlist. 👇 https://lnkd.in/dSyBHzwU

Looking to run a highly available MongoDB deployment across multiple Kubernetes clusters in GCP? This step-by-step tutorial walks you through using the Percona Operator for MongoDB to deploy in two GKE clusters, then connect them with Multi-Cluster Services (MCS) for smooth cross-cluster discovery and communication. https://hubs.ly/Q03JhzhD0

How do we query records in MongoDB from CSharp? If you're interested in learning the basics of filtering in MongoDB, check out this article! I've included examples of how to use the MongoDB FilterDefinitionBuilder to build different kinds of filters -- starting off nice and easy. You can follow the examples in this article to build more complex filters for your own needs! Check out the article: https://lnkd.in/gHTCyevm #MongoDB #Databases #CSharp #DotNet

When MongoDB Storage Hits ~83% Recently, in one of our production-grade MongoDB clusters (with post-archival strategy enabled), we noticed the storage utilization creeping up to ~83% full. On digging deeper, it wasn’t just active data — the real issue was storage bloat. To address this, we carefully planned and executed a resync/compact activity. 💡 The outcome was eye-opening: - Nearly 40% disk space freed - Significant boost in throughput - Balanced IOPS across the cluster - Faster and smaller backups - Reduced risk of hitting the max storage limit, and future cost savings 👉 Key takeaway: activities like resync/compact can make a huge difference, but they require planning and validation before running on production workloads. Sharing this experience so others in the community can benefit while managing MongoDB at scale. #MongoDB #DatabaseEngineering #CloudArchitecture #DatabaseOptimization #StorageManagement #DevOps #EngineeringExcellence #Scalability

Proud to showcase my MongoDB Indexing Design Fundamentals from MongoDB. This badge validates my expertise in designing efficient indexes to speed up queries and reduce resource consumption in MongoDB. Having struggled with large index sizes in the past, this course highlighted some excellent strategies. I wanted to share my key takeaways on how to efficiently manage and optimize MongoDB indexes. 1. Embrace Partial Indexes: This is a game-changer. Instead of indexing all documents in a collection, partial indexes only index documents that meet a specific filter. This drastically reduces the index size, saving disk space and memory, without sacrificing performance for your most critical queries. 2. Regularly Review Your Indexes: A large index size can be a sign of a bloated system. I learned the importance of reviewing existing indexes at least once a quarter. This helps you identify and remove redundant or unused indexes that are consuming valuable resources for no good reason. 3. Test with Hidden Indexes: Dropping an index can be risky. That's where hidden indexes come in. This feature allows you to temporarily remove an index from the query planner's consideration. You can safely test the impact on your queries before committing to a permanent deletion. 4. The ESR Strategy for Compound Indexes: When building compound indexes, the Equality, Sort, Range (ESR) strategy is a must. It dictates the optimal field order: place fields for exact matches first, followed by fields for sorting, and finally, fields for range queries. This ensures the index is used as efficiently as possible. 5. Understand Low Cardinality Fields: A query on a low-cardinality field (one with very few unique values) can often return a large number of documents. For this reason, these fields are generally poor candidates for a standalone index. However, they are highly effective when used as the first field in a compound index to quickly narrow down the results for a more selective query. These strategies provide a solid framework for building and maintaining a healthy, high-performing MongoDB database. I'm looking forward to applying these concepts in my work! Another very useful video: Solving the Mystery of Index Use https://lnkd.in/gFy3a3fm #MongoDB #DatabaseOptimization #Indexes #DataManagement #Learnin

🌩 Day 14 of 15 – AWS Cloud Journey Today’s focus was on Databases in AWS — understanding both relational and non-relational systems, and getting hands-on with RDS (MySQL) and DynamoDB. 🚀 📌 Key Topics Covered: 🔹 Relational Databases (SQL) 1. Structured, table-based, use schemas. 2. Good for transactional systems requiring consistency. 🔹 Non-Relational Databases (NoSQL) 1. Schema-less, flexible, and scalable. 2. Ideal for unstructured/semi-structured data and high-velocity workloads. 🔹 Amazon RDS (Relational Database Service) 1. Fully managed relational database service. 2. Features: automated backups, replication, high availability, and easy scaling. 🛠 Hands-On Practical: 1. Set up RDS with MySQL. 2. Connected it from an EC2 instance to simulate a real-world application environment. 🔹 Amazon DynamoDB (NoSQL Database) 1. Fully managed, serverless, key-value and document database. 2. Features: millisecond latency, scalability, backup & restore, streams. 🛠 Hands-On Practical: 1. Created a table in DynamoDB. 2. Inserted items, performed scan and query operations. ✨ Key Takeaway: RDS simplifies running relational workloads without heavy admin overhead. DynamoDB shines in scalability and flexibility for modern, high-performance apps. One day at a time, getting closer to cloud mastery ☁️ For more details on these topics, please see the PDF below. #AWS #CloudComputing #RDS #DynamoDB #MySQL #Database #LearningInPublic #Upskilling

Scaling reads in Aurora PostgreSQL is simple: Add up to 15 read replicas Use the reader endpoint for load-balanced queries Failover ready: replicas can become the writer That’s high availability + performance in one cluster. #AWS #Aurora #PostgreSQL #CloudComputing