Devsharp 2018 was held in Gdansk on September 21st, 2018 in the Stary Maneż cultural center. It’s only 15 min away from the Airport so it’s very easy to go there and of course, all the conference were in English.
This free conference was such a victim of its success that they had to increase the number of places. Initially planned for 250 persons, about 400 passionate developers answered the call.
The conference was sponsored by IHS Markit, automotiveMastermind, Carfax and of Course, MongoDB. Seven talks were planned during the day from Microsoft, 8x8, and JetBrains for example.
I also happen to have a slot to speak about MongoDB Atlas & MongoDB Stitch and I explained how you could benefit from our platforms to accelerate and simplify your interactions with your data.
I shared my presentation here so feel free to have a look but I have made a lot of live demos leveraging MongoDB Compass, MongoDB Charts, MongoDB Atlas and MongoDB Stitch so make sure to come and see me on stage next time :-).
I would definitely recommend this conference, especially if you are a C# developer so please feel free to join us next year.
It was also my largest audience I have spoken to so far. I am really proud and I can’t wait to go again next year :-). Special thanks to the team for your warm welcome!
The MongoDB Summer ‘18 Intern Series: From Learning How a Computer Works to Helping Build MongoDB Stitch
Most interns joining us for the MongoDB summer engineering program are in the process of pursuing a degree in computer science and come looking for a hands-on, impactful work experience. As a sophomore in high school, Julia Ruddy was introduced to computer science in a basic CS class that received so much positive feedback, her school introduced an Advanced Topics in CS course to the curriculum for her senior year. When Julia started her freshman year at Princeton University, she decided to pursue a degree in electrical engineering and joined us this summer as one of three interns on our Stitch team. Andrea Dooley : Why did you decide to declare electrical engineering as your major? Julia Ruddy : The Advanced Topics course in high school went very low level. We started the course working with transistors and proceeded to build up to the level of writing a Tetris game. It was interesting to see how it all fit together from transistors and binary to code you can write a program on. It was at that point I considered electrical engineering because as interested as I was in CS, electrical engineering gives you the opportunity to see what’s under the hood. I wanted to understand how a computer worked from 1s and 0s to building something like MongoDB. AD : So you’ll graduate with a degree in Electrical Engineering. What experience have you had with Computer Science? JR : One of my priorities was to keep up with the computer science schedule, so I took a lot of upper-level CS classes. Last summer during an internship at an early stage startup, my work consisted of half hardware and half software. Through that internship, I learned I didn’t want to work in the hardware space. I liked the hardware aspect of my work but found I enjoyed my days doing software more. Although I love learning about it, I find building hardware from scratch to be very tedious, and it’s hard to debug. Also, I find the software industry as a whole more intriguing. AD : How did you first learn about MongoDB? JR : A good friend of mine from Princeton interned here last summer and encourage me to attend the open house. It seemed like a cool place to work and piqued my interest. When I got deeper into the search I found MongoDB especially attractive because it’s an established company, but smaller in size. I know sometimes at larger organizations your work can get lost, but my friend vouched for the work he did here, and that it impacted the business. AD : As someone not fully immersed in CS as other intern candidates might be, did you find the interview to be particularly difficult? JR : From my experience, software interviews, in general, follow a similar pattern regarding data structure and algorithms. I did a ton of prep in those areas, ensuring I was very familiar with them. During my interviews here, everyone was very approachable and easy to talk to and the conversations flowed naturally. If my interviewers had any hesitations regarding my experience, I didn’t notice them. AD : What team are you working on this summer? JR : I was given my first choice, which was to work on the Stitch team. Stitch is a serverless platform designed to help people focus on the interesting and exciting pieces of their applications, rather than get bogged down with boilerplate and tedious back end code. It’s a newer team for a relatively new product, as well as something people are talking a lot about. I wanted to be on a team that was on the forefront of the upcoming MongoDB release. AD : What project are you working on for Stitch? JR : There is no one concrete project within Stitch. I’ve been picking up tickets, some bigger than others, working on both the front end and back end. Going into my internship, I had zero front end experience and I felt that if I wanted to become a respected full stack engineer, I needed to change that. So, my goal for the summer was to pick up as many UI tickets as I could. I actually really enjoyed front end development and learned a lot. Overall, this summer I’ve been able to work on many different things, which I find to be more similar to what a day in the life of a full-time engineer would be like. I now know what a career in software engineering entails, and it’s fun! AD : What’s one of the bigger tickets you were able to work on? JR : One of the bigger tickets was to create a generic AWS service in the UI, which allows for an extra layer of ease for our users. They used to have to edit code themselves to do more specific actions, but now they can choose from a drop down. I’ve also been working through a series of UI tickets for Stitch usage metrics, which is a real time visual representation for users to see how much data they’ve used, and how many transactions they’ve done, which will help with transparency in billing. AD : Aside from project work, what has been one of the most memorable aspects of your internship? JR : I worked with a group of interns on a project for Skunkworks, MongoDB’s internal hackathon. We built a computer game for people with minimal technical skills, to help them get familiar with MongoDB query language. The user plays a detective, and the goal is to solve the mystery of the missing emerald leaf in the MongoDB museum by querying databases. When the detective is completing one of the tasks to help solve the mystery, there is a prompt to drag and drop the proper argument into the query. We made it to the final round to present the game to the entire office and won the award for “Most Fun.” AD : Is there anything you learned during your time at MongoDB that surprised you? JR : I found the Speaker Series with our CPO [Chief People Officer] Dan Heasman to be really interesting mostly because it’s the side of a company I don’t ever think about. The idea that there is someone dedicated to fostering a great culture, managing how people interact, and maintaining the vibe is new to me, but he was so clear and concise in his approach I learned that there actually is a science to making people feel comfortable and welcome at work. AD : What’s one key takeaway from your experience as a MongoDB intern? JR : After this internship, I can confidently say software engineering is what I want to pursue after I graduate. One of my worries beforehand was that it was an isolated career, where you code all day and don’t have much interaction with other people, but my experience at MongoDB has shown me that there are always people willing to help, and asking for help, and there is a lot of collaboration in between. It’s been really rewarding to be able to write code that fits into a massive code base like MongoDB, as opposed to working on an isolated project as I would perhaps at another internship, or at school.
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.