GIANT Stories at MongoDB

New 0.7 Release of the MongoDB Enterprise Operator for Kubernetes Beta

New 0.7 Release of the MongoDB Enterprise Operator for Kubernetes Beta

The MongoDB Enterprise Operator for Kubernetes enables you to automate your deployment of MongoDB in Kubernetes clusters. The operator leverages the power of MongoDB Ops Manager and MongoDB Cloud Manager providing you a simple single Kubernetes interface for MongoDB deployment and management.

In this release 0.7, we spent a significant amount of time in the plumbing dealing with state reconciliation. Our Operator can now manage state synchronization between Kubernetes and MongoDB making it easy to handle state changes and provide a more reliable operational experience. For a complete description of the changes see the release notes.

Release Notes:
MongoDB Enterprise Operator for Kubernetes 0.7 Release Notes

For download and installation instructions:
GitHub Repository for MongoDB Enterprise Operator for Kubernetes

Five Minute MongoDB - Change Streams and MongoDB 4.x

Change Streams are a powerful tool in MongoDB for monitoring changes in a collection's documents. They got even more powerful in MongoDB 4.0 enabling you to act on changes to any document in any collection in any database in your MongoDB deployment. Read this Five Minute MongoDB to find out how.

Want Ops Manager updates faster? Introducing Rapid Releases!

We're announcing an additional new Ops Manager release schedule: the Ops Manager Rapid Release, designed to let you test the latest updates to Ops Manager. Customers will be able to download and try out a new build of Ops Manager around every three weeks.

Building with Patterns: The Bucket Pattern

In this edition of the Building with Patterns series, we're going to cover the Bucket Pattern. This pattern is particularly effective when working with Internet of Things (IoT), Real-Time Analytics, or Time-Series data in general. By bucketing data together we make it easier to organize specific groups of data, increasing the ability to discover historical trends or provide future forecasting and optimize our use of storage.

Five Minute MongoDB: Why Documents?

The document is the natural representation of data. We only broke data up into rows and columns back in the 70s as a way to optimize data access. Back then, storage and compute power was expensive and so it made sense to use developer time to reduce the data set into a schema of rows and column, interlinked by relationships and then normalized between tables to reduce duplication. This process was cost-effective then and so it came to dominate database thinking.

That domination means that many people accept the burden of defining rows and columns as an essential part of using databases. In many ways though, relational databases are still expecting the designer and developer to pre-chew the data for easier processing by the database.

The Document Alternative

MongoDB Q&A: What's the deal with data integrity in relational databases vs MongoDB?

Previously in MongoDB Q&A, we looked at agile development and MongoDB. This time, it's all about data integrity...

I've been doing a lot of reading lately on relational vs non-relational databases, investigating the typical reasons why you might pick one over the other. A quick Google search of "relational vs non-relational databases" returns over 18 million results. Digging into that massive pile of results brought up a few key themes around why you would select a non-relational database: horizontal scaling, performance, managing unstructured and polymorphic data, and minimal upfront planning.

Building with Patterns: The Attribute Pattern

In this edition of the Building with Patterns series, we explore the Attribute Schema Design Pattern. We can use this pattern when we have queries that are targeting many similar fields in a document. The Attribute Pattern also provides easy document indexing options.

Documents Are Everywhere

Over the past decade, following MongoDB’s lead, a raft of new document databases have been introduced and legacy databases have added document capabilities. In 2017, Microsoft layered an API for MongoDB on top of Cosmos DB (at the time called “DocumentDB”, but no longer), and recently Amazon released DocumentDB, which presents a subset of the MongoDB query language atop their Aurora technology. The document model, and the MongoDB API in particular, is flourishing.

The Future Will Be Documented

MongoDB was born out of frustration from using relational databases not designed for today's modern applications. After 40 years of no real alternatives, we pioneered a new way to work with data -- the MongoDB document model and the associated query language.

Building an AI-powered workplace ticketing system on MongoDB Atlas at Spoke

At every company, workplace teams are flooded with hundreds of questions on a daily basis. Support ticket platform, Spoke, aims to drastically simplify managing and responding to these time consuming queries. But Spoke goes beyond traditional ticketing with their friendly, AI-powered chatbot that gives workplace teams hours of time back as it automatically responds to questions on Slack, email, SMS, and web. And the more employees ask, the more Spoke learns.