MongoDB World 2016 is a wrap! Presentations now available.

Meghan Gill
July 15, 2016 | Updated: January 20, 2017
#World 2016#Company

Thank you to the 2000+ in-person attendees, 2,000+ live stream viewers, and 30+ sponsors who attended MongoDB World. For those of you who were unable to attend, we’ve gathered our favorite highlights into our Extend the Experience microsite.

Visit Extend the Experience

MongoDB Atlas

Leading up to MongoDB World, we told you something giant was coming. We were excited to announce and demo MongoDB Atlas, our new database as a service. MongoDB Atlas makes it easy to set up, operate, and scale your MongoDB deployments in the cloud.

Learn more about Atlas

Spark Connector

We also introduced the general availability of our new, native MongoDB Connector for Apache Spark. With the combination of MongoDB and Spark, developers can seamlessly build data driven applications. You can learn more by reading our announcement or join our webinar at the end of the month.


2016 Innovation Award Winners Honored

The annual MongoDB Innovation Awards recognizes those organizations and individuals that took a giant idea and made a tangible impact in the world. This year’s winners included Barclay’s, Comcast, UPS, and many more. A full list of the winners and a brief description of their projects is outlined in our blog post.

Startup Showcase Participants Dream Big

This year we introduced the Startup Showcase on the Giant Ideas stage, where several startups powered by MongoDB presented. We heard from companies like hiQ, Kinevez, and Meshfire on how they are using MongoDB to bring their giant idea to life.

#MDBW16

Thanks to our great attendees and speakers we were trending on Twitter. Here are some of our favorite conference tweets:

@helton_souza: #MDBW16 is like Hogwarts but for tech. MongoDB Atlas is the upgrade on RDS #justsaying

@CeeDeeBee2901: #MDBW16 omg graphs in MongoDB

@mcnamarabrian: Great product announcement - MongoDB Atlas. Good explanation of where it fits in the product lineup, too.#MDBW16


You can relive all of the fun on our Extend the Experience site!

Extend the Experience


*About the Author - Meghan Gill*

Meghan is Director of Community and Demand Generation at MongoDB. She was the 8th employee and first non-engineering hire at MongoDB, helping to build the developer community behind the fastest-growing big data ecosystem. In 2014 she was recognized by AlleyWatch as a Rising Star in Enterprise Technology.

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MongoDB and Apache Spark at China Eastern Airlines: Delivering 100x Performance Improvements

New MongoDB Connector for Apache Spark Enables New Fare Calculation Engine, Supporting 180m Fares and 1.6 billion Queries per Day, Migrated off Oracle. As one of the world’s largest airlines, China Eastern constantly explores emerging technologies to identify new ways of improving customer experience and reducing cost. Earlier this year, I interviewed its engineering leaders to learn more about the migration of China Eastern’s flight search platform from Oracle to MongoDB . As a result of the migration, China Eastern achieved orders-of-magnitude performance improvements, and enabled the delivery of application features that had never before been possible. The next stage of China Eastern’s initiative to transform user experience has been to address airline fare calculations, and serve them reliably, at scale, to the migrated search application. At this year’s MongoDB World user conference, Chong Huang, Lead Architect at China Eastern Airlines, presented details on their new fare calculation engine built with Apache Spark and MongoDB to New MongoDB Connector for Apache Spark Enables New Fare Calculation Engine, Supporting 180m Fares and 1.6 billion Queries per Day, Migrated off Oracle support more than 1.6 billion queries per day. The Challenge Based on averages used across the airline industry, 12,000 searches are needed to generate a single ticket sale. China Eastern currently sells 260,000 airline seats every day, and is targeting 50% of those to come from its online web and online channels. Selling 130,000 seats equates to 1.6 billion searches and fare requests per day, or just under 20,000 searches a second. However, the current fare calculation system built on the Oracle database supports only 200 searches per second. China Eastern’s engineers realized they needed to radically re-architect their fare engine to meet the required 100x growth in search traffic. The Solution Rather than calculate fares for each search in real time, China Eastern decided to take a different approach. In the new system, they pre-compute fares every night, and then load those results to the database for access by the search application. The flight inventory data is loaded into an Apache Spark cluster, which calculates fares by applying business rules stored in MongoDB. These rules compute fares across multiple permutations, including cabin class, route, direct or connecting flight, date, passenger status, and more. In total, 180 million prices are calculated every night! The results are then loaded into MongoDB where they are accessed by the search application. Why Apache Spark? The fare engine demands complex computations against large volumes of flight inventory data. Apache Spark provides an in-memory data processing engine that can be distributed and parallelized across multiple nodes to accelerate processing. Testing confirmed linear scalability as CPU cores were added to the cluster. As a Java shop, Apache Spark’s Java API also provides development simplicity for the engineering team. Why MongoDB? China Eastern had already implemented a successful project with MongoDB. They knew the database would scale to meet the throughput and latency needs of the fare engine, and they could rely on expert support from MongoDB engineers. The database’s flexible data model would allow multiple fares to be stored for each route in a single document, accessed in one round trip to the database, rather than having to JOIN many tables to serve fares from their Oracle database. Figure 1: Fare Engine Architecture Why the MongoDB Connector for Apache Spark? Just as the project was starting, MongoDB announced the new Databricks-certified MongoDB Connector for Apache Spark . Using the Connector, Spark can directly read data from the MongoDB collection and turn it into a Spark Resilient Distributed Dataset (RDD), against which transformations and actions can be performed. Internally the Connector uses the splitVector command to create chunk splits, and each chunk can then be assigned to one Spark worker for processing. Data locality awareness in the Connector ensures RDDs are co-located with the associated MongoDB shard, thereby minimizing data movement across the network and reducing latency. Once the transformations and actions have been completed on the RDD, the results can be written back to MongoDB. The Implementation Based on performance benchmarking, a cluster of less than 20 Red Hat Linux servers (comprising app servers, and the Spark and MongoDB cluster) are required to meet the demands of 180 million fares and 1.6 billion daily searches. China Eastern is using Apache Spark 1.6 against the latest MongoDB 3.2 release, provisioned with Ops Manager for operational automation. Testing has shown each node in the cluster scaling linearly, and delivering 15x higher performance at 10x lower latency than the previous Oracle based system. Next Steps In his MongoDB World presentation, Mr. Huang provides more insight into the platform built with Apache Spark and MongoDB. He shares detailed steps and code samples that show how to download and setup the Spark cluster, how to configure the MongoDB Connector for Apache Spark, the process for submitting a job, and lessons learned along the way to optimize performance. View the slides from MongoDB World Download our new whitepaper for examples and guidance on turning analytics into real-time action with Apache Spark and MongoDB. About the Author - Mat Keep Mat is a director within the MongoDB product marketing team, responsible for building the vision, positioning and content for MongoDB’s products and services, including the analysis of market trends and customer requirements. Prior to MongoDB, Mat was director of product management at Oracle Corp. with responsibility for the MySQL database in web, telecoms, cloud and big data workloads. This followed a series of sales, business development and analyst / programmer positions with both technology vendors and end-user companies.

July 14, 2016
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Using MongoDB Skill Scanner to Build Better Training Programs

Technology leaders know that transformation is about more than just adopting modern technologies like MongoDB. The entire organization has to rally behind change — which is no easy task. The skills that modern development teams need are evolving faster than ever, and hiring to fill skills gaps can be too time-consuming and expensive of a process for many organizations. So it’s imperative that we plan for how we want to bring our people with us on our modernization journey, and proactively upskill them on the technologies we’re betting on. Because what happens if you choose MongoDB, but your developers don’t know how to use it? CIOs know that training programs are easier said than done. EY reported that 30% of CIOs acknowledge that their training programs are ineffective, and that they’re struggling to retain talent because of it. These leaders come to us to help them build and execute their MongoDB training programs , and seek advice on two extremely common yet critical challenges: How do we get away from the less effective one-size-fits-all approach? How do we measure the ROI of our training program and connect it to business impact? How we use MongoDB Skill Scanner to overcome training challenges Our Professional Services team uses a tool called MongoDB Skill Scanner to address both of these challenges. This tool helps us provide these three benefits to our customers looking to build a training program: Improve MongoDB proficiency: Teams can use Skill Scanner to quickly and easily assess the MongoDB skill gaps of their team members and gain a comprehensive understanding of their team’s MongoDB skills baseline. Increased productivity and accuracy: When team members have a comprehensive understanding of MongoDB, they are able to work more quickly and accurately on projects, leading to increased productivity and a higher quality of work. Save time and money with targeted Training: Using Skill Scanner, customers can avoid wasting time and money on trial-and-error learning. Instead, they can focus on improving their skills in a more targeted and efficient way with right-sized training plans. By leveraging this data, our customers’ engineers can engage in the right training at the right time, targeted for their job role and specific skill shortages. When a training program is built this way, engineers maximize their knowledge retention and minimize time away from their projects. Skill Scanner includes three role-based assessments, one for developers, database administrators, and DevOps respectively. Through a series of multiple choice questions, Skill Scanner provides customers with a clear understanding of their level of expertise across a set of technical skills that are critical for success in their role. After submitting the assessment, engineers will get results in each skill area outlining if they are beginner, intermediate, or advanced. Why data-driven training programs matter We’ve learned that it’s not enough to just tell teams to go watch training videos or webinars on their own, or to place everyone in the same one-size-fits-all program. Skills gaps vary from team to team, and individual to individual. The one-size-fits-all approach of some programs may not address individual learners' needs, wasting time and making it difficult for them to acquire new skills. By using Skill Scanner, we’re able to interpret this data to help determine which training courses your team should take. But we don’t only capture this data before doing training; we use Skill Scanner again after training programs are completed to see where immediate improvements have been made. This helps technology leaders prove the impact and ROI of their training, and gives them the confidence that their teams are ready to be successful with MongoDB. Developing a Precision Learning Program To go even further, our team can work with you to build a Precision Learning Program, where we use Skill Scanner data to build learning schedules that are unique to each individual. These schedules include a variety of short, blended, learning events such as classes, technical workshops, self-paced exercises, and project coaching. We’ve seen PLP lead to higher knowledge retention and of course, measurable project results. A customer who recently concluded their PLP saw a 43% increase in knowledge retention. Getting started building a personalized training program Skill gaps aren’t a novel problem IT leaders are facing. But with new digital courses, training, and technologies, the resources to close these gaps are at your fingertips. Skill Scanner and Precision Learning Program have been specifically designed to empower teams by offering targeted training that enhances their understanding of MongoDB. These short training events are carefully crafted to close skill gaps without compromising developer productivity. We’ve seen a variety of customers use this tool to help train their team’s individual needs, from needing to upskill new hires on their teams, projects with new MongoDB products, migrating to MongoDB Atlas, and more. It also saves your business the hours developers would've wasted searching for answers (and developers don’t want to spend their time that way, either). “We need help getting from point A to point B and feel MongoDB is uniquely positioned to help” — CTO at large insurance firm If you're interested in trying out MongoDB Skill Scanner or want to explore the MongoDB Precision Learning Program further, you can reach out to your account representative or contact us directly .

June 7, 2023