Since welcoming the first Diversity Scholarship recipients in 2014, each year I look forward to meeting the new class of Scholars at MongoDB World.
As the conference grows, so does the scholarship award. This year, I’m excited to share that scholarship recipients located outside of the tristate area will also receive a complimentary 3-night hotel stay at the NY Hilton Midtown. We recognize the cost of a hotel room in New York City can be significant, and hope this removes another barrier that may prevent some applicants from attending.
In addition to a complimentary hotel stay, scholarship recipients get*:
- Complimentary admission to MongoDB World
- Complimentary admission to a workshop
- Invitation to a lunch session with other Scholars
- Speed mentoring with MongoDB speakers at the event
- A MongoDB certification voucher applicable for both developer and DBA certification exams
- Six-month access to on-demand MongoDB University courses
- Lifelong membership in the online MongoDB Diversity Scholars community
- A feature in a blog post
Members of underrepresented groups in tech** qualify to apply for a scholarship. Apply today. Hurry, the deadline is May 4.
We look forward to seeing your submissions and to meeting you at MongoDB World!
*Please note that travel is not included in the Diversity Scholarship.
** MongoDB’s Scholarship program seeks to support to members of groups who are underrepresented in the technology industry. This includes, but is not limited to, Black, LatinX, women, low-income, and LGBTQ.
BookMyShow Continues to Lead Online Entertainment Ticketing in India and Scales to 25 Million Users with MongoDB
India's twin passions for cinema and tech make it a natural fit for automated ticketing. But if ever a market needs scalable solutions, this 1.4 billion-strong nation is it. That’s a lesson Viraj Patel, VP Technology for BigTree Entertainment , learned the hard way. "We started out in ticketing distribution in 1999 using telephones," he says, "before mobile platforms and internet access were on the scene. It just didn't work. The investors pulled the plug in 2002.” Undeterred, the company successfully pivoted to selling software to cinema chains. By 2006, Viraj and team were ready to aim for the big prize again. They just needed the right tools. With the internet and mobile data fitting into place, a trial project in online ticket aggregation looked promising enough for investors to fund the launch of BookMyShow in 2007. “We launched with a 100 percent Microsoft stack,” says Viraj, “but soon realized that scaling with Microsoft was not an easy job.” It wasn’t the Windows platform or the developer tools that were the problem, he recalls: “It was the SQL Server database. That was the first bottleneck as we got more and more traffic, and it soaked up more and more resources and money. It wasn’t the right solution. It couldn’t scale with us.” Spoiler: By 2018, BookMyShow, each month, sells more than 10 million tickets for all manner of movies and events and serves three billion pages a month across the web and its 50 million plus installed apps. Scaling happened. The plot changed for the better in 2010 with the discovery of MongoDB. “We were looking around for alternatives, and it was the new kid on the block.” (In fact, MongoDB 1.0 had launched just the year before, and MongoDB India was yet to come.) “We tested it internally as a straight distributed database for monolithic SQL database swap. Every web and mobile application we built needed a database that had performance and scalability, and MongoDB blew us away on both.” MongoDB really won its spurs when the company added Facebook Connect to its registration process. “The registration database was the first thing we built, and it was running on SQL Server. Which was OK, until Facebook Connect came along and we added that as a registration option. Then the database really struggled. We switched to MongoDB and it was night and day. Tremendous gains. Not only did we get the ability to represent customers directly as JSON documents in the database, which made our data model much simpler, but we got all our performance back. “We want the flexibility of upgrading the schema for future use cases, and that’s so much easier in MongoDB. The data structures we create are clear and easy to read, and it’s so much simpler to understand and extend,” Viraj adds, about their discovery of the advantages of document-model storage. MongoDB’s second big job was also thoroughly web scale, as it took on the task of giving each of those millions of users their own bespoke, personalized view of the service. This time, the engineering team knew where to start. “About five years ago, we built our personalization engine on MongoDB,” says Viraj, “and it continues to scale with us. It stores a lot of customer information and when a customer visits, it pulls it out, personalizes it in real time and delivers it. That really improves the customer experience. We see an 18 percent increase in conversion, personalized versus non-personalized.” Today, MongoDB is the default database for developing ideas and services in BigTree, and Viraj cheerfully admits he has long ago stopped counting how many nodes are in use. “Last time I looked, it was between 100-160,” he says. Future plans include containerization of the databases to smooth out upgrades and ease of deployment with BigTree’s agile DevOps production pipeline and, when the time comes, sharding the customer database. That’s planned for, but not currently necessary. He explains: “We just haven’t reached the point where writes to MongoDB are the limiting factor anywhere in the service. We get a long way with MongoDB replica sets, and are safe in the knowledge that there are no limitations to scaling further when we need to.” Viraj cares deeply about latency – “We’re a performance-sensitive company” – and much of the service is instrumented by monitoring and management platforms such as New Relic. While initial performance gains were superlative, he says, things have only continued to improve as new features and technologies have been added. “We had been using SQL tabular databases for customer booking history,” says Viraj. “We moved this to MongoDB and have seen a superb performance boost. What used to take up to 5000 ms on traditional SQL databases went down to 10-20 ms on MongoDB using the MMAP storage engine. When we moved to MongoDB’s default WiredTiger storage engine, it improved five to ten times further, to 2ms. We’re still getting this performance, even though the database now has close to 200 million documents.” There have been other benefits from following MongoDB’s roadmap. “WiredTiger has made things much more cost-effective,” he says. “Security is better as we now encrypt data instead of storing it in plain JSON. Our customer database is five times more compact and our personalization database uses nearly eight times less storage.” In the future, he says, they expect aggregation queries and query caching mechanisms will improve performance still more. As for reliability, “MongoDB auto-heals so well in the event of any failures in our platform we don’t even need to worry about it. That’s highly appreciated, and much better than any of the other databases we have used.” There can be few better stories of early adoption and innovation with MongoDB than the success BigTree Entertainment has enjoyed with BookMyShow. Viraj and his engineers insist on picking the right tools for each part of the job running India’s favourite online ticketing service, their long experience of casting this particular actor in so many roles makes MongoDB a performer they’ve come to rely on. Read more about what others are building with MongoDB .
Choosing the Right Tool for the Job: Understanding the Analytics Spectrum
Data-driven organizations share a common desire to get more value out of the data they're generating. To maximize that value, many of them are asking the same or similar questions: How long does it take to get analytics and insights from our application data? What would be the business impact if we could make that process faster? What new experiences could we create by having analytics integrated directly within our customer-facing apps? How do our developers access the tools and APIs they need to build sophisticated analytics queries directly into their application code? How do we make sense of voluminous streams of time-series data? We believe the answer to these questions in today's digital economy is application-driven analytics. What is Application-Driven Analytics? Traditionally, there's been a separation at organizations between analytics that run the business and analytics that manage the business. They're built by different teams, they serve different audiences, and the data itself is replicated and stored in different systems. There are benefits to the traditional way of doing things and it's not going away. However, in today's digital economy, where the need to create competitive advantage and reduce costs and risk are paramount, organizations will continue to innovate upon the traditional model. Today, those needs manifest themselves in the demand for smarter applications that drive better customer experiences and surface insights to initiate intelligent actions automatically. This all happens within the flow of the application on live, operational data in real time. Alongside those applications, the business also wants faster insights so it can see what's happening, when it's happening. This is known as business visibility, and the goal of it is to increase efficiency by enabling faster decisions on fresher data. In-app analytics and real-time visibility are enabled by what we call application-driven analytics. Find out why the MongoDB Atlas developer data platform was recently named a Leader in Forrester Wave: Translytical Data Platforms, Q4 2022 You can find examples of application-driven analytics in multiple real-world industry use cases including: Hyper-personalization in retail Fraud prevention in financial services Preventative maintenance in manufacturing Single subscriber view in telecommunications Fitness tracking in healthcare A/B testing in gaming Where Application-Driven Analytics fits in the Analytics Ecosystem Application-driven analytics complements existing analytics processes where data is moved out of operational systems into centralized data warehouses and data lakes. In no way does it replace them. However, a broader spectrum of capabilities are now required to meet more demanding business requirements. Contrasting the two approaches, application-driven analytics is designed to continuously query data in your operational systems. The freshest data comes in from the application serving many concurrent users at very low latency. It involves working on much smaller subsets of data compared to centralized analytics systems. Application-driven analytics is typically working with hundreds to possibly a few thousand records at a time. And it's running less complex queries against that data. At the other end of the spectrum is centralized analytics. These systems are running much more complex queries across massive data sets — hundreds of thousands or maybe millions of records, and maybe at petabyte scale — that have been ingested from many different operational data sources across the organization. Table 1 below identifies the required capabilities across the spectrum of different classes of analytics. These are designed to help MongoDB’s customers match appropriate technologies and skill sets to each business use case they are building for. By mapping required capabilities to use cases, you can see how these different classes of analytics serve different purposes. If, for example, we're dealing with recommendations in an e-commerce platform, the centralized data warehouse or data lake will regularly analyze vast troves of first- and third-party customer data. This analysis is then blended with available inventory to create a set of potential customer offers. These offers are then loaded back into operational systems where application-driven analytics is used to decide which offers are most relevant to the customer based on a set of real-time criteria, such as actual stock availability and which items a shopper might already have in their basket. This real-time decision-making is important because you wouldn't want to serve an offer on a product that can no longer be fulfilled or on an item a customer has already decided to buy. This example demonstrates why it is essential to choose the right tool for the job. Specifically, in order to build a portfolio of potential offers, the centralized data warehouse or data lake is an ideal fit. Such technologies can process hundreds of TBs of customer records and order data in a single query. The same technologies, however, are completely inappropriate when it comes to serving those offers to customers in real time. Centralized analytics systems are not designed to serve thousands of concurrent user sessions. Nor can they access real-time inventory or basket data in order to make low latency decisions in milliseconds. Instead, for these scenarios, application-driven analytics served from an operational system is the right technology fit. As we can see, application-driven analytics is complementary to traditional centralized analytics, and in no way competitive to it. The benefits to organizations of using these complementary classes of analytics include: Maximizing competitive advantage through smarter and more intelligent applications Out-innovating and differentiating in the market Improving customer experience and loyalty Reducing cost by improving business visibility and efficiency Through its design, MongoDB Atlas unifies the essential data services needed to deliver on application-driven analytics. It gives developers the tools, tech, and skills they need to infuse analytics into their apps. At the same time, Atlas provides business analysts, data scientists, and data engineers direct access to live data using their regular tools without impacting the app. For more information about how to implement app-driven analytics and how the MongoDB developer data platform gives you the tools needed to succeed, download our white paper, Application-Driven Analytics: Defining the Next Wave of Modern Apps .