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Intern Spotlight: Jason Hu
This year, MongoDB welcomed 33 university students to our intern program in Engineering, Marketing, and Education. In this series, we'll introduce you to the talented students who are helping us transform development and operations for how we run applications today. I had the chance to sit down with Jason Hu who spent the summer in our Palo Alto office working with the CAP team! Where do you go to school, what is your major, and what year are you in? I'm a rising senior at Brown University, where I study computer science. What is your role at MongoDB? I’m a software engineering intern on the CAP team. How did you find out about the internship program at MongoDB? Why did you choose to come to MongoDB? I actually started computer science a year late, so I didn’t really know about tech companies, let alone MongoDB until late in college. As I did more CS I learned from upperclassmen and recent graduates about the company, and it seemed like a cool mix of technical challenges that thought big. What’s your hometown? I actually grew up in the Silicon Valley—I’m from Los Altos, California. My friends and I would walk by Facebook when it was still a small startup in Palo Alto. Did you have previous experience using MongoDB before you arrived? If so, how are things different now that you work at MongoDB? If not, how did you learn MongoDB and how was the education process? I had no experience whatsoever. To be honest, databases had been a huge, intimidating block of computer science and software engineering that I haven’t really touched before. I figured throwing myself in the deep end would be the best way to learn, and after one week of orientation I definitely learned more than an entire semester. All the on-boarding staff and mentors were terrific—they should consider becoming professors, after they retire. Working on actual projects has also taught me loads about databases—not just how to use them, but how to think about them in the larger picture of an enterprise or business. Bike or public transportation to work? I drive, actually. One of the perks of being at home, I suppose, is having my old car. What’s a typical day (or week) for you? Well, it’s really hard to say since every week I’ve had has been so different. Some days will be very focused, where I can put on some ear-buds and crank out code. Other days will be back-to-back meetings or presentations, where I’ll be learning about MongoDB’s history or theoretical concepts about databases or road mapping my project for the next week. And I can’t forget the fun! A typical week always has a bunch of great events, from smaller game nights to larger company outings, such as to indoor skydiving, Giants games, or go-karting. The intern coordinators really out-do themselves. What do you love most about MongoDB? Definitely how much I’ve learned. I can say without exaggeration that I learned more about databases during onboarding than an entire semester of the class. But it’s more than just coding chops: Watching the ins-and-outs of a startup has been fascinating. For me, it’s easy at school to be stuck in a narrow path of problem solving—improving runtimes, cleaning code, etc.—but the challenges outside of code are usually not so neat and controlled. Hearing about the problems of marketing, finance, design, and HR has been incredibly illuminating for so much work we take for granted. What’s the most challenging aspect of your job? Adapting and learning on the fly is definitely something I need to work on more. In school, we have a general sense that a right answer exists, and what it should look like. In engineering in the real world, however, there aren’t necessarily the right answers in the back of the book. Now as an engineering intern, my main challenge isn’t finding the right answer, but rather figuring out whether a problem is feasible, what the solution looks like, and whether it’s worth my time. And this isn’t an easy process. During the course of the summer, as I learned and tested different languages and programs, my project had to change and sometimes backtrack as I adapted. What’s do you hope to accomplish while you’re here? The cool thing about Cluster Bingo was that I got the start it from scratch—decisions for structuring the code, as well as library and language decisions, were all mine. So my goal wasn’t necessarily to rush out a fully functional version. Instead I wanted to think about what are its long-term goals, and how to design it in such away that enabled the growth and continuation of the project after I leave. What’s your favorite Seamless lunch order? Definitely any sandwich from the Ace of Sandwiches. Name one secret skill you have, unrelated to work. I’m a really mean baker. Literally. Apparently I get really bossy, but my cakes also are fantastic. Where do you want to be in five years? I picked this question because, well, as a rising college senior it’s kind of on my mind a lot. The honest answer is “I don’t know,” but I can say that one way or another I know I’ll be programming. Which isn’t necessarily to say I’ll be a software engineer: The past few months, I’ve realized that I can use code within any of my interests—advocating social and economic justice, product design and user interfaces, and even my original major of biology and bioengineering. Given how much my interests and skills have changed in the past five years, it’s impossible for me to really know where I’ll be in the upcoming five. All I hope is to somehow find a way to combine some or all of my interests through coding, whether that’s through a software company, start-up, or NGO. Kindle or book? What’s your favorite book? I haven’t read anything on Kindle yet, so I’m going to have to say books. However, the moment Amazon figures out how to give Kindles that old-book smell, I might have to give it a shot. It’s really hard for me to pick a favorite book, but the one that I always find myself rereading is the graphic novel Blankets by Craig Thompson. His illustrations and paneling are gorgeously done, and he has a great ability of capturing little, human interactions in his characters. Every time I read it I find something new. Describe your perfect weekend. Wake up Saturday morning and start off with hiking or the gym. Then I like going up to a museum in San Francisco: The Academy of Sciences in Golden Gate Park never gets old. Afterward, catching up with friends over dinner, before going out for the night or hanging out at their apartments. The next morning usually entails people watching in the park, and finally settling into a book on the train ride home.
How DataSwitch And MongoDB Atlas Can Help Modernize Your Legacy Workloads
Data modernization is here to stay, and DataSwitch and MongoDB are leading the way forward. Research strongly indicates that the future of the Database Management System (DBMS) market is in the cloud, and the ideal way to shift from an outdated, legacy DBMS to a modern, cloud-friendly data warehouse is through data modernization. There are a few key factors driving this shift. Increasingly, companies need to store and manage unstructured data in a cloud-enabled system, as opposed to a legacy DBMS which is only designed for structured data. Moreover, the amount of data generated by a business is increasing at a rate of 55% to 65% every year and the majority of it is unstructured. A modernized database that can improve data quality and availability provides tremendous benefits in performance, scalability, and cost optimization. It also provides a foundation for improving business value through informed decision-making. Additionally, cloud-enabled databases support greater agility so you can upgrade current applications and build new ones faster to meet customer demand. Gartner predicts that by 2022, 75% of all databases will be on the cloud – either by direct deployment or through data migration and modernization. But research shows that over 40% of migration projects fail. This is due to challenges such as: Inadequate knowledge of legacy applications and their data design Complexity of code and design from different legacy applications Lack of automation tools for transforming from legacy data processing to cloud-friendly data and processes It is essential to harness a strategic approach and choose the right partner for your data modernization journey. We’re here to help you do just that. Why MongoDB? MongoDB is built for modern application developers and for the cloud era. As a general purpose, document-based, distributed database, it facilitates high productivity and can handle huge volumes of data. The document database stores data in JSON-like documents and is built on a scale-out architecture that is optimal for any kind of developer who builds scalable applications through agile methodologies. Ultimately, MongoDB fosters business agility, scalability and innovation. Key MongoDB advantages include: Rich JSON Documents Powerful query language Multi-cloud data distribution Security of sensitive data Quick storage and retrieval of data Capacity for huge volumes of data and traffic Design supports greater developer productivity Extremely reliable for mission-critical workloads Architected for optimal performance and efficiency Key advantages of MongoDB Atlas , MongoDB’s hosted database as a service, include: Multi-cloud data distribution Secure for sensitive data Designed for developer productivity Reliable for mission critical workloads Built for optimal performance Managed for operational efficiency To be clear, JSON documents are the most productive way to work with data as they support nested objects and arrays as values. They also support schemas that are flexible and dynamic. MongoDB’s powerful query language enables sorting and filtering of any field, regardless of how nested it is in a document. Moreover, it provides support for aggregations as well as modern use cases including graph search, geo-based search and text search. Queries are in JSON and are easy to compose. MongoDB provides support for joins in queries. MongoDB supports two types of relationships with the ability to reference and embed. It has all the power of a relational database and much, much more. Companies of all sizes can use MongoDB as it successfully operates on a large and mature platform ecosystem. Developers enjoy a great user experience with the ability to provision MongoDB Atlas clusters and commence coding instantly. A global community of developers and consultants makes it easy to get the help you need, if and when you need it. In addition, MongoDB supports all major languages and provides enterprise-grade support. Why DataSwitch as a partner for MongoDB? Automated schema re-design, data migration & code conversion DataSwitch is a trusted partner for cost-effective, accelerated solutions for digital data transformation, migration and modernization through a modern database platform. Our no-code and low-code solutions along with cloud data expertise and unique, automated schema generation accelerates time to market. We provide end-to-end data, schema and process migration with automated replatforming and refactoring, thereby delivering: 50% faster time to market 60% reduction in total cost of delivery Assured quality with built-in best practices, guidelines and accuracy Data modernization: How “DataSwitch Migrate” helps you migrate from RDBMS to MongoDB DataSwitch Migrate (“DS Migrate”) is a no-code and low-code toolkit that leverages advanced automation to provide intuitive, predictive and self-serviceable schema redesign from a traditional RDBMS model to MongoDB’s Document Model with built-in best practices. Based on data volume, performance, and criticality, DS Migrate automatically recommends the appropriate ETTL (Extract, Transfer, Transform & Load) data migration process. DataSwitch delivers data engineering solutions and transformations in half the timeframe of the existing typical data modernization solutions. Consider these key areas: Schema redesign – construct a new framework for data management. DS Migrate provides automated data migration and transformation based on your redesigned schema, as well as no-touch code conversion from legacy data scripts to MongoDB Atlas APIs. Users can simply drag and drop the schema for redesign and the platform converts it to a document-based JSON structure by applying MongoDB modeling best practices. The platform then automatically migrates data to the new, re-designed JSON structure. It also converts the legacy database script for MongoDB. This automated, user-friendly data migration is faster than anything you’ve ever seen. Here’s a look at how the schema designer works. Refactoring – change the data structure to match the new schema. DS Migrate handles this through auto code generation for migrating the data. This is far beyond a mere lift and shift. DataSwitch takes care of refactoring and replatforming (moving from the legacy platform to MongoDB) automatically. It is a game-changing unique capability to perform all these tasks within a single platform. Security – mask and tokenize data while moving the data from on-premise to the cloud. As the data is moving to a potentially public cloud, you must keep it secure. DataSwitch’s tool has the capability to configure and apply security measures automatically while migrating the data. Data Quality – ensure that data is clean, complete, trustworthy, consistent. DataSwitch allows you to configure your own quality rules and automatically apply them during data migration. In summary: first, the DataSwitch tool automatically extracts the data from an existing database, like Oracle. It then exports the data and stores it locally before zipping and transferring it to the cloud. Next, DataSwitch transforms the data by altering the data structure to match the re-designed schema, and applying data security measures during the transform step. Lastly, DS Migrate loads the data and processes it into MongoDB in its entirety. Process Conversion Process conversion, where scripts and process logic are migrated from legacy DBMS to a modern DBMS, is made easier thanks to a high degree of automation. Minimal coding and manual intervention are required and the journey is accelerated. It involves: DML – Data Manipulation Language CRUD – typical application functionality (Create, Read, Update & Delete) Converting to the equivalent of MongoDB Atlas API Degree of automation DataSwitch provides during Migration Schema Migration Activities DS Automation Capabilities Application Data Usage Analysis 70% 3NF to NoSQL Schema Recommendation 60% Schema Re-Design Self Services 50% Predictive Data Mapping 60% Process Migration Activities DS Automation Capabilities CRUD based SQL conversion (Oracle, MySQL, SQLServer, Teradata, DB2) to MongoDB API 70% Data Migration Activities DS Automation Capabilities Migration Script Creation 90% Historical Data Migration 90% 2 Catch Load 90% DataSwitch Legacy Modernization as a Service (LMaas): Our consulting expertise combined with the DS Migrate tool allows us to harness the power of the cloud for data transformation of RDBMS legacy data systems to MongoDB. Our solution delivers legacy transformation in half the time frame through pay-per-usage. Key strengths include: ● Data Architecture Consulting ● Data Modernization Assessment and Migration Strategy ● Specialized Modernization Services DS Migrate Architecture Diagram Contact us to learn more.