Slides and videos are now up from the largest ever MongoNYC, the one day conference in New York City dedicated to MongoDB. Based on feedback from attendees, here are the Top 5 Videos from MongoNYC, which range from a series of use cases, to best practices for using MongoDB in production.
- Growing Up MongoDB By Kiril Salvino, CTO and Founder, Gamechanger
- The right and wrong ways to implement MongoDB, Richard Kreuter, Consulting Manager, 10gen
- Managing a Maturing MongoDB Ecosystem, Charity Majors, Systems Engineer, Parse
- Real time integration between MongoDB and SQL Databases, Eugene Dvorkin, WebMD
- How to Keep Your Data Safe in MongoDB, Eliot Horowitz, CTO and Co-founder, 10gen
The Big Data Hoax That Wasn't
Welcome to the Age of Big Data. Or perhaps it’s the Age of Big Data Agnosticism. In a Newtonian twist, what started as a wave of hype for data’s transformational potential on organizations everywhere has turned into an equal and opposite backlash of big data naysaying. It is an understandable reaction to the great over-selling of big data as a kind of enterprise cure-all. Of course, in some companies, big data pilots have produced nothing but big piles of unfulfilled expectations. But the problem likely is not big data. Big data remains potentially the most powerful engine for business transformation to gain currency in the 21st century. The problem is that so much of what is sold as big data isn’t. It’s typically just lots of data. “Big data, that’s just data mining with a fancy new name.” How often have you heard that? It’s flatly false. The size or volume of the data does not matter in genuine big data analytics. Instead, savvy organizations already understand that big data is really about working with a mix of data types - structured and unstructured, from inside the organization and outside. It is CRM forms, but it also is Tweets, Facebook posts, TripAdvisor rants, Gmails, Outlook entries, even voicemail. In most organizations this does not add up to petabytes of data, as I’ve written before . Terabytes is the usual quantity even though that seems small by many measures. The complexity arises in the diversity of data. And that raises a problem. Not many databases have the flexibility to handle that many forms of data. And fewer databases have the agility to permit modifications on the fly - “Shouldn’t we add SMS data in here, too?” The right answer is, done. A database that cannot - with little fuss -- add a new row is too rigid for use in true big data analysis because the exciting - maybe maddening? - bit about big data today is that always there is new input that may enhance the overall result. Then there are the other questions: why are you collecting big data in the first place? What do you want from your analysis of it and this question is key because without targeted analytics, big data is just hoarding. As an insightful story in The Guardian recently posited, “Companies need to focus on big answers not big data. Instead of focusing upon the concept of big data, organizations should concentrate on the intelligence data can offer.” In other words, it’s not about the data: it’s about what intelligence can be drawn from it. The Guardian author calls himself a “big data sceptic” but, really, he isn’t. He just shares the frustration over the many mislabeled big data projects - that never were about big data - and also about the data hoarding that some companies do when they say they are committing to big data. Such projects rarely end well. Real big data - unstructured, from multiple sources - coupled with real analytics is a game changer that gives forward-thinking organizations insight that before was merely guesswork. One Texas city ran analyses to determine exactly what happened in parts of the city that experienced higher than anticipated growth and a resulting increase in value. This was true big data. In the mix were police reports, zoning violations, construction permits, parking tickets, you name it. If the data existed, it was fed into the analysis and the city began to see what it did - and didn’t do - to spur growth. Where could it get out of the way? Where could it proactively spur growth? It was real big data in action. And it’s why big data remains a big deal, despite the hype.
Control Your Colours in MongoDB Charts
Colours are integral to the story you want to convey with any sort of data visualisation. With the latest release of MongoDB Charts , we have added more control to how you can assign colours to your charts! Previously, colour assignment of a series were always based on the series order within that chart. However, we may instead want to colour the chart based on the series value. Some basic scenarios where these different strategies prove useful include: Colouring the top 3 series with the colours gold, silver and bronze. Colouring the series "Summer" and "Winter" with "Red" and "Blue" respectively, to symbolise the season. If the above examples did not give it away enough, we will create some beautiful charts using an Olympics dataset to fully understand the capabilities of the new features. Single-series charts We will start off with a basic single-series chart. These charts usually have a single field encoded to the x and y axes and will display a single colour for the chart. In these charts, we now show a single colour swatch for you to edit. Simple, right? Multi-series charts For more complicated charts with multiple series, we may want to colour the series based on the encoded field itself. These charts are created when multiple fields are encoded to an aggregation channel where the field key is used to build the multi-series chart. In the above chart, I have a medal tally of the top 10 countries based on medal count. The chart itself is fine, but we could improve this chart with some useful colouring! A notable colour scheme we could apply to this chart is assigning each series to the colour of the medal. Inside the Color Palette customisation option, you will see that each encoded field is now listed based on the order that they were encoded in. With the colour scheme set to the medal colour, the chart will be a lot easier to convey the original information. Colours assigned to these channels will always have the same colour assigned and will ignore the ordering of these fields. Assigning chart colours to string data The final chart that we want to create, involves a chart where the data itself is a String type. With these chart types, the Color Palette will provide options to toggle between the two different colour assignment strategies where: 'By Order' will allow you to assign colours by the ordering of the series 'By Series' lets you customise the colour for a specific series value To help streamline the process of assigning colours in the above chart, in the ‘By Order’ menu, I can choose to assign colours based on the value order of the Discipline that appears in the chart. This may be useful if we don't care what the colours are that represent each Discipline. Alternatively, we could assign colours using 'By Series' so that we can be assured that I can represent the Disciplines with an associated colour. Now that we have created all of our charts using the different ways we can assign colours, we can be confident that the colours in our data visualisations are consistent throughout our dashboard. Want to start colouring your charts today? You can start now for free by signing up for MongoDB Atlas , deploying a free tier cluster and activating Charts. Have an idea on how we can make MongoDB Charts better? Feel free to leave an idea at the MongoDB Feedback Engine .