GIANT Stories at MongoDB

Build something fun in the MongoDB World Hackathon

Launch your editors! Fire up your soldering irons! Go and git init your repos! MongoDB is hosting a 10-week long competition for the world's hackers, designers, and makers. The top 3 teams will win a trip to New York City where they will present their projects on stage at our annual conference, MongoDB World 2019. There's a bunch of prizes to be won, including $10,000 for first place! So get ready to team up, learn about new technology, and bring your ideas to life in the MongoDB World Hackathon from February 20 to April 30.

Building with Patterns: The Computed Pattern

We've looked at various ways of optimally storing data in the Building with Patterns series. Now, we're going to look at a different aspect of schema design. Just storing data and having it available isn't, typically, all that useful. The usefulness of data becomes much more apparent when we can compute values from it. What's the total sales revenue of the latest Amazon Alexa? How many viewers watched the latest blockbuster movie? These types of questions can be answered from data stored in a database but must be computed.

Mobile Sync for iOS Now In Beta

We know you've been waiting for it and now here it is, the beta release for MongoDB Mobile Sync for iOS is now available. Ever since the Android version of Mobile Sync entered beta, we've been asked when would the iOS beta appear and now, it's here and ready for you to start developing your next, great mobile app.

Building With Patterns: The Outlier Pattern

So far in this Building with Patterns series, we've looked at the Polymorphic, Attribute, and Bucket patterns. While the document schema in these patterns has slight variations, from an application and query standpoint, the document structures are fairly consistent. What happens, however, when this isn't the case? What happens when there is data that falls outside the "normal" pattern? What if there's an outlier?

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.

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.

Building with Patterns: The Polymorphic Pattern

Over the course of this blog post series, we’ll take a look at twelve common Schema Design Patterns that work well in MongoDB. We hope this series will establish a common methodology and vocabulary you can use when designing schemas. Leveraging these patterns allows for the use of “building blocks” in schema planning, resulting in more methodology being used than art.