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.
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?
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.
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.
Meet Guillermo Ulises Lo Coco - MongoDB University’s 1 Millionth Registrant
MongoDB University passed over 1 million registrants. Meet our 1 Millionth registrant Guillermo Ulises Lo Coco from Tenerife, Canary Islands, Spain who signed up for the MongoDB University courses M001: MongoDB Basics and M040: New Features and Tools in MongoDB 4.0.
Amy Berman: Let's start by getting into your tech background. When did you first become interested in tech, and why did you decide to pursue the field?
Guillermo Ulises Lo Coco: I’ve been programming since I had a Commodore in 1990 but started to program seriously in 2002. Having taken 4-5 years off programming, I came back to realize the developer world had changed. At this time, I felt compelled to advance my skills, and I signed up for MongoDB University M001.
AB: That’s a good transition into my next question. Where do you currently work? What is your role, and what do you like about it?
GU: I work at Club Deportivo Tenerife, a Spanish league soccer team, and I am in charge of redesigning old intranet database applications to improve public web areas. I focus on future-proofing my apps and spend a lot of time trying to make the “right choice” to save time in the future.
AB: How did you first discover MongoDB? What projects have you used or are you in the process of using with MongoDB?
GU: Programming in different languages was always difficult for me; it’s not easy to switch. In 2016, I discovered NodeJs and started to invest time researching and learning how to switch to JS backend and I found many examples and posts with Node and JSON databases. I tried different technologies and found MongoDB the most complete.
AB: Why MongoDB?
GU: I tried different technologies and MongoDB was the easiest to design, prototype, and deploy.
- Extremely easy to learn
- Fast like no other
- Excellent documentation
- Well supported in Linux
- Amazing cloud service and desktop app
AB: What did you do in the past year with MongoDB?
GU: Last year, I developed new apps for annual subscription renewal and an appointment management for the Tenerife Soccer team - https://2018.cdtoficial.es.
After learning geospatial indexing in the MongoDB University course, I wrote a Telegram Bot to show how easy it is to implement the geospatial indexing feature. You can interact with the bot via Telegram.
@GasolinaBarataBot Telegram Bot takes a table from Spanish Open Data Portal every 12hs, converts that approximately 10.000 stations data in a JSON object, and saves a copy to Atlas Cloud. You can send any map position inside Spain to the bot, and it will search for the best gas station in Spain according to the selected fuel type.
AB: What advice or encouragement do you have for those considering enrolling in a MongoDB University course to learn to use MongoDB?
GU: Think in real-life database problems, how the data will grow, and how you will use that data. The real-life case studies used in MongoDB University courses give you the ability to really think about how your database will be used, and how it will scale.
"MongoDB University courses provided me with the opportunity to update my database skills and allowed me to quickly and easily build applications. I especially enjoy the many features and benefits of using MongoDB Atlas and Compass."
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