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Simplifying Data Science with Iguazio and MongoDB: Modernization with Machine Learning

For the most innovative, forward-thinking companies, “data” has become synonymous with “big data” — and “big data” has become synonymous with “machine learning and AI.” The amount of data you have is raw knowledge. The ability to connect the dots in a cohesive picture that lets you see major projections, personalizations, security breaches, etc., in real time — that’s wisdom. Or, as we like to call it, data science. MongoDB Cloud is the leading cloud data platform for modern applications. Iguazio , initially inspired by the powerful Iguazu Falls in South America, is the leading data science platform built for production and real-time use cases. We’re both disrupting and leading various industries through innovation and highly forethought intelligence. It makes perfect sense for us to work together to create a powerful, data-driven solution. Iguazio Data Science & MLOps platform optimizes data science for your use cases Iguazio enables enterprises to develop, deploy, and manage their AI applications, drastically shortening the time required to create real business value with AI. Using Iguazio, organizations can build and run AI models at scale and in real time, deploy them anywhere (multi-cloud, on-prem or edge), and bring to life their most ambitious AI-driven strategies. Enterprises spanning a wide range of verticals use Iguazio to solve the complexities of machine learning operations ( MLOps ), and accelerate the machine learning workflow by automating the following, end-to-end processes: Data collection — ingested from any diverse source, whether structured, unstructured, raw or real-time Data preparation — through exploration and manipulation at scale (go big!) Continuous model training — through acceleration and automation Rapid model and API deployment Monitoring and management of AI applications in production As a serverless, cloud-native data science platform, Iguazio reduces the overhead and complexity of developing, deploying, and monitoring AI models, guarantees consistent and reproducible results, and allows mobilizing, scaling, and duplicating functions to multiple enforcement points. MongoDB delivers unprecedented flexibility for real-time data science integration With its scalable, adaptable data processing model, ability to build rich data pipelines, and capacity to scale out while doing both in parallel, MongoDB is a foundational persistence layer for data science. It allows you to use your data intelligently in complex analytics, drawing new conclusions and identifying actions to take. Data science and data analytics go hand-in-hand, fueled by big data. The MongoDB data platform handles data analytics by: Enabling scalability and distributed processing — processing data with a query framework that reduces data movement by knowing where that data is and optimizing in-place computation Accelerating insights — delivering real-time insight and actions Supporting a full data lifecycle — intelligent data tiering from ingestion to transactions to retirement Leveraging a rich ecosystem of tools and machine learning for data science Here's a look at how Iguazio and MongoDB partner to synthesize a seamless production environment: MongoDB and Iguazio: from research to production in weeks Iguazio fuses with MongoDB to allow intelligent, complex data compilations that lead to real-world ML/AI results like streaming and analytics, IoT applications, conversational interfaces, and image recognition. Data science is opening opportunities for businesses in all areas, from financial services to retail, marketing, telco, and IoT, and those opportunities create demands on data that continue to grow. Iguazio swiftly reduces the development and deployment of data science projects from months to weeks, transforming how businesses, developers, and product owners use and imagine new use cases for their data. Together, MongoDB and Iguazio establish a joint hybrid/multi-cloud data science platform. MongoDB’s unique features create the perfect seeding ground for Iguazio’s data science platform. They include: MongoDB’s high-performing, highly ranked data platform experience No data duplication Optimization for real-time, an essential factor for data science An elastic, flexible model that adjusts to ever-changing load requirements Production that’s ready in minutes Meanwhile, Iguazio’s powerful ML pipeline automation simplifies the complex data science layer by creating a production-ready environment with an end-to-end MLOps solution, including: A feature store for managing features (online and offline) that resides in MongoDB Data exploration and training at scale, using built-in distribution engines such as Dask and Horovod Real-time feature engineering using Iguazio’s Nuclio-supported serverless functions Model management and model monitoring, including drift detection Open and integrated Python environment, including built-in libraries and Jupyter Notebook-as-a-Service Data and data science in the real world When we think of data, stagnant databases may come to mind. But data in action is live, quick, and moves in real time. Data science is no different — and it has quickly incorporated itself in every sector of virtually every industry: Fraud prevention — distinguishing legitimate from fraudulent behavior and learning to prevent new tactics over time Predictive maintenance — finding patterns to predict and prevent failures Real-time recommendation engines — processing consumer data for immediate feedback Process optimization — minimizing costs and improving processes and targets Remote monitoring — quickly detecting anomalies, threats, or failures Autonomous vehicles — continuously learning new processes and landscapes to optimize safety, performance, and maintenance Smart scheduling — increasing coordination among nearly infinite variables Smart mobility systems — using predictive optimization to maintain efficiency, safety, and accuracy IoT & IIoT — generating insights to identify patterns and predict behavior Data science today MongoDB enables a more intuitive process for data management and exploration by simplifying and enriching data. Iguazio helps turn data into smarter insights by simplifying organizations’ modernization into machine learning, AI, and the ongoing spectrum of data science — and we’ve only just scratched the surface. To learn more about how, together, Iguazio and MongoDB can transform your data processes into intelligent data science, check out our joint webinar discussing multiple client use cases. MongoDB and modernization To learn more about MongoDB’s overall modernization strategy for moving from legacy RDBMS to MongoDB Atlas, read here .

December 2, 2020

MongoDB Atlas Online Archive for Data Tiering is now GA

We’re thrilled to announce that MongoDB Atlas Online Archive is now Generally Available. With Online Archive, you can seamlessly tier your data across Atlas clusters and fully managed cloud object stores, gaining the flexibility to set the perfect price to performance ratio across your data. Eliminate the need to manually migrate or delete valuable data. Simply set a rule on your Atlas cluster to automate data archival while retaining easy access to query all your data using a single connection string. With this capability, you can bring new and previously cost-prohibitive use cases onto MongoDB Atlas , our first-class managed offering, and manage your entire data lifecycle without replicating or migrating it across multiple systems. What is Atlas Online Archive? Online Archive is a fully managed data tiering solution that allows you to tier data across your "hot" database storage layer and "colder" cloud object storage to maintain queryability while optimizing on cost and performance. Online Archive is a good fit for many different use cases, including: Insert heavy workloads, where data is immutable and has lower performance requirements as it ages Historical log keeping and time-series datasets Storing valuable data that would have otherwise been deleted using TTL indexes We’ve received amazing feedback from the community over the past few months while the feature was in beta and we’re now confident in supporting your production workloads. Our users have put the feature through a variety of use cases in production and development workloads which has enabled us to make a wide range of improvements. Online Archive gives me the flexibility to store all of my data without incurring high costs, and feel safe that I won't lose it. It's the perfect solution. Ran Landau, CTO, Splitit Autonomous Archival Management It's easy to get started with Online Archive and it requires no ongoing maintenance once it’s been set up. In order to activate the feature, you can follow these simple steps: Navigate to the “Online Archive” tab on your cluster card and begin the setup flow. Set an archiving rule by selecting a date field, with dot-notation if it’s nested, or creating a custom filter. Choose commonly queried fields that you want your archival queries to be optimized for, with a few things in mind: Your data will always be “partitioned” by the date field in your archive, but can be partitioned by up to two additional fields as well. The fields that you most commonly query should be towards the top of the list (date can be moved to the top or bottom). Query fields should be chosen carefully as they cannot be changed after the fact and will have a large impact on query performance. Avoid choosing a field that has unique values as it will have negative performance impacts for queries that need to scan lots of data. And you’re done! MongoDB Atlas will automatically move data off of your cluster and into a more cost-effective storage layer that can still be queried with a single connection string that combines cluster and archive data, powered by Atlas Data Lake . What's Next? Along with announcing Online Archive as Generally Available, we’re excited to share a few additional product enhancements which should be available in the coming months: Custom filters for your archival rules using a non-date based field Support for BYO Key Encryption on your archival data A dedicated connection string for archive-only queries Support for additional time formats Improved performance and stability Try Atlas Online Archive Online Archive allows you to right-size your Atlas clusters by storing hot data that is regularly accessed in live storage and moving colder data to a cheaper storage tier. Billing for this feature will include the cost to store data in our fully managed cloud object storage and usage based pricing for querying archive data. We can’t wait to see what new workloads you’ll bring onto MongoDB Atlas with the new flexibility provided by Online Archive! To get started, sign up for an Atlas account and deploy any dedicated cluster (M10 or higher). Have questions? Check out the documentation or head over to our community forums to get answers from fellow developers. And if we’re missing a feature you’d like to see, please let us know ! Safe Harbor Statement The development, release, and timing of any features or functionality described for MongoDB products remains at MongoDB's sole discretion. This information is merely intended to outline our general product direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver any material, code, or functionality. Except as required by law, we undertake no obligation to update any forward-looking statements to reflect events or circumstances after the date of such statements.

November 30, 2020

Splitit & MongoDB Atlas: Racing to Capture a Global Opportunity

Splitit is a global payment solution that allows businesses to offer installment plans for their customers. Unlike with other buy now, pay later (BNPL) solutions, Splitit shoppers can split their online purchases into monthly installments by using their existing credit, without the need for registration, application, or approval. “We have a very different proposition than others in this space,” says Splitit’s CTO, Ran Landau. “We’re not a financing company. We utilize the customer’s existing credit card arrangement, which allows us to accommodate smaller average deal values and a broader range of installment schedules.” Splitit works with online retailers across all market sectors and diverse price points, and recently raised $71.5 million in investment to fund global expansion. Following its IPO in January 2019, the business had seen strong growth as more consumers moved from brick and mortar to ecommerce. Then COVID-19 hit, and online shopping boomed. Landau recognized that the company needed to quickly scale its infrastructure in order to capture this large opportunity. The Need for Speed Landau joined Splitit in May 2019 and worked to modernize the company’s infrastructure. At the time, the team was using a traditional relational database. “As tech leaders, we need to make the right decision,” he says. “When I came to Splitit, I knew I needed a powerful NoSQL server so that my developers could develop faster and so that we could scale – both things that our relational databases were failing to deliver.” In the interest of getting up and running quickly, Ran’s team thought that they could move faster using a cloud-provider database that mimicked MongoDB functionality. He had used MongoDB before and saw that this solution offered the same drivers he was familiar with and claimed compatibility with MongoDB 3.6. Initially, the new solution seemed fine. But as the team started to migrate more data into the database, however, Landau noticed a few missing features. Scripts for moving documents from one collection to another were failing, and overall performance was deteriorating. The application became slow and unresponsive even though the load on the database was normal. “We were having issues with small things, like renaming collections. I couldn’t search or navigate through documents easily,” recalls Landau. Offline Database: A Breaking Point Then one day, the application was unable to communicate with the database for 20 minutes, and when the database finally came back online, something wasn’t right. Landau contacted support, but the experience was not very helpful. “We were not pleased with the response from the database vendor,” he explains. “They insisted that the issue was on our side. It wasn’t so collaborative.” Fortunately, he had taken a snapshot of the data so Splitit was able to revert back to an earlier point in time. But the incident was troubling. Other teams also had been complaining about how difficult it was to debug problems and connect to the database successfully. Landau knew he needed to find a better solution as soon as possible. MongoDB Atlas: A Reliable, Scalable Solution Landau believed that MongoDB was still the right choice for Splitit, and investigated whether the company offered a cloud solution. He discovered MongoDB Atlas and decided to give it a try. “The migration to MongoDB Atlas was so simple. I exported whatever data I had, then imported it into the new cluster. I changed the connection strings and set up VPC peering in all of my environments,” says Landau. “It was incredibly easy.” Not only was MongoDB Atlas built on actual MongoDB database software, but it was also secure, easy to use, and offered valuable features such as Performance Advisor . “It can tell you which indexes need to be built to increase speed. It’s such a powerful tool — you don’t need to think; it analyzes everything for you,” explains Landau. Another great feature was auto-scaling. “My biggest concern as I scale is that things keep working. I don’t have to stop, evaluate, and maintain the components in my system,” says Landau. “If we go back to doing database operations, we can’t build new features to grow the business.” Auto-archival Made Easy with Online Archive As a business in the financial services industry, Splitit needs to comply with various regulations, including PCI DSS . A key requirement is logging every transaction and storing it for auditing purposes. For Splitit, that adds up to millions of logs per day. Landau knew that storing this data in the operational database was not a cost-effective, long-term solution, so he initially used an AWS Lambda function to move batches of logs older than 30 days from one collection to another periodically. A few months ago, he discovered Online Archive , a new feature released at in June 2020. With it, Landau was able to define a simple rule for archiving data from a cluster into a more cost-effective storage layer and let Atlas automatically handle the data movement. “The gem of our transition to Atlas was finding Online Archive,” says Landau. “There’s no scripting involved and I don’t have to worry about my aging data. I can store years of logs and know that it’s always available if I need it.” Online Archive gives me the flexibility to store all of my data without incurring high costs, and feel safe that I won't lose it. It's the perfect solution. Ran Landau, CTO, Splitit With federated queries, the team can also easily analyze the data stored in both the cluster and the Online Archive for a variety of use cases. Ready for Hypergrowth and Beyond Looking back, Landau admits that he learned his lesson. In trying to move quickly, he selected a solution that appeared to work like MongoDB, but ultimately paid the price in reliability, features, and scalability. You wouldn't buy a fake shirt. You wouldn't buy fake shoes. Why buy a fake database? MongoDB Atlas is the real thing. Ran Landau, CTO, Splitit Landau is confident that his investment in MongoDB puts in place a core building block for the business’ continued success. With a fully managed solution, his team can focus on building features that differentiate Splitit from competitors to capture more of the market. “We saw our growth triple in March due to COVID-19, but the sector as a whole is expanding,” he says. “Our technology is patent protected. Everything we build moving forward will be on MongoDB. As a company that’s scaling rapidly, the most important thing is not having to worry about my scaling. MongoDB Atlas takes care of everything.”

November 23, 2020

Built with MongoDB: Sunsama

My co-founder Travis Meyer and I were both one year into our careers when it hit us that we would spend the next 40 years using tools such as Google Calendar and Microsoft Outlook to map out our time. That felt unacceptable. We wanted to build a productivity tool that’s more thoughtful and intentional so we can live a life that’s more thoughtful and intentional . That’s when we started working on Sunsama. Ashutosh Priyadarshy, Co-Founder, Sunsama Ashutosh Priyadarshy , founder of Sunsama , and I first met in 2017. At that time, he was building a meeting documentation tool. He soon saw significant churn and realized that users didn’t love the product, so Sunsama pivoted. Today, Sunsama is a Y Combinator-backed, invite-only daily task manager for busy professionals. Ashutosh says that in conversations with thousands of users, Sunsama found that people know what’s on their calendar, but also want to know how to think about their workday—how to answer the key question “what am I going to do today?” Sunsama gives users the ability to combine all tasks, meetings, to-dos, and JIRA tickets in a prioritized calendar, enabling professionals to be more mindful of how they spend their time. In this issue of #BuiltWithMongoDB, Ashutosh and I discuss his journey building Sunsama (including the pivots and successful fundraising) and how his team uses MongoDB to operate more efficiently. Siya Raj Purohit: How big is Sunsama now? Ashutosh Priyadarshy: We’re a team of five serving 2,000 active users. We graduated from YC in March 2019 and have since raised $2.4 million in seed money, were featured in TechCrunch and Cheddar, and were voted “Hot Product of the Month” on ProductHunt (read: Sunsama: If Trello & Google Calendar Had a Baby)” ). We’re growing 10 to 15% per month but generally limiting growth via invitations to ensure optimal user experience. SRP: Why is Sunsama #BuiltWithMongoDB? AP: We first decided to use MongoDB because a framework we wanted to use (Meteor) was tightly coupled with MongoDB. Our product doesn’t require all of the complexity of other database solutions, and MongoDB felt like a simple way to get started. Initially, we deployed MongoDB on AWS ourselves. But we’re a tiny team of five people, and it was time-consuming to manage DevOps. Upgrading to MongoDB Atlas was an easy decision because we would rather spend slightly more money and have zero headaches so we can spend all of our time building for customers instead of building for internal tooling and DevOps. As a young team, you just can’t afford to dedicate resources to second-order issues, so outsourcing to MongoDB Atlas made complete sense. Two or three years ago, we noticed that the MongoDB Atlas ecosystem was maturing beyond other tools out there: MongoDB added search, monitoring, and security features that were easy to set up. The timing was great: we were going through an audit with Google for getting our team integration approved, and Google looked at the entire surface area of the security app. Being able to plug and play—setting up the right type of encryption by just pressing a button—was really nice. We grew into MongoDB’s suite of products, and once we committed, we didn’t want to leave. SRP: What MongoDB services do you use? AP: We previously managed our own MongoDB deployment on Amazon EC2, and it was so much overhead. Our customers just wanted the product to work; they didn’t care about how our MongoDB instance was deployed. Thanks to MongoDB Atlas, we can focus on building things customers care about instead of maintaining our database. MongoDB is also moving in a direction we’re excited about that will provide direct value to our customers, especially with MongoDB 4.2 and the ability to run Elasticsearch queries directly in MongoDB. This means we can further simplify our stack and remove expensive Elasticsearch deployments that we’ve got in AWS and use the simple and clean alternatives MongoDB provides. Want to learn more about Sunsama? Request access here. Building something cool with MongoDB? Check out our developer resources , and let us know if you want your startup to be featured in our #BuiltWithMongoDB series.

November 18, 2020

New Ways to Customize Your Charts

When it comes to building charts, we know that details matter. Small differences in layout, styling or composition can make a big difference in how well your chart communicates the story behind your data. That’s why we’ve just released a whole bunch of new capabilities in MongoDB Charts , giving you more control than ever. Here’s what’s new: Secondary Y Axis: Charts can be a great way to show correlation between two different datasets, but when their scales differ greatly it can be hard to see the correlation. By choosing to plot one more series on a secondary Y Axis, you can allow them to make the most of the available space and highlight any interesting relationships. Secondary Y Axis can be enabled on Grouped Column, Discrete Line, Continuous Line and Continuous Area charts. Legend Position: Chart legends can now be moved to the top, right or bottom of your chart, or hidden altogether. “All Others” Group: Charts has long allowed you to limit a chart to show, say, just the top 10 values. The new “All Others” option allows you to add an additional bar or donut segment that shows the value of all other categories not included in the limit. “Count by Value” aggregation: Building multi-series charts is now easier than ever, with the new “Count by Value” aggregation option. This will automatically create series from each distinct value found in a field. String binning with Regular Expressions: Last month we introduced binning of string values, allowing you to choose the exact values to go into each bin. This month we’ve extended this further by allowing you to use Regular Expressions to assign values to a bin based on powerful patterns. Scatter Mark formatting: We’ve ramped up the customization options available on Scatter charts, allowing you to control the size, border thickness and opacity of each plotted mark. Line Dash Styles: A new option on Discrete and Continuous Line charts results in a different dash style for each series, making it easier to differentiate the series and improve the accessibility of your charts. Here’s one example of a chart that shows off the secondary Y axis, custom legend position and line dash styles: And here’s another, showing the effect you can get by customizing your scatter chart’s mark style: We hope you enjoy these new charting capabilities, but we’re not done yet! Over the next couple of months, we’ll be moving our focus to Table charts, adding options like conditional formatting, text wrapping and column pinning. If you have any other ideas for new customization features, please let us know using the MongoDB Feedback Engine . If you haven’t tried Charts yet, you can get started for free by signing up for MongoDB Atlas and deploying a free tier cluster.

November 18, 2020

Meet the MongoDB Sharding Team’s New Barcelona Division

I sat down with Kaloian Manassiev (Kal), Lead Engineer on MongoDB’s Barcelona-based Sharding team, to better understand what the team does and how they plan to grow. The Sharding team started in our New York City headquarters and expanded to Barcelona in the summer of 2019. Here, we explore who they are recruiting in the growing Spanish market and why someone would be excited to join their team. Ashley Perez: First, can you give a quick overview of what the Sharding team does? Kal Manassiev: The Sharding team builds frameworks and tools that abstract away difficult distributed systems problems for database users. This frees developers to focus on working with the data itself and not have to worry about where it resides, whether there is some network problem, or if a data center catches fire. As a result of this, the projects delivered by the Sharding team are highly visible and are predominantly flagship features for each major MongoDB release. AP: Let’s dive in a little more. What projects has your team taken on? KM: In the past, we’ve delivered projects such as Distributed Transactions and Retryable Writes . Retryable Writes, for example, makes it much easier to implement scenarios so that if your browser crashes when you click the Pay button, it will not charge you two times when you try again. Just recently, we completed a project to assign vector or scalar clocks to all the distributed objects we manage, so that our system is easier to reason about and can be proven correct via theoretical proof models and correctness checkers such as TLA+. This project also makes it easier to add more distributed systems features and be confident in their correctness. AP: Very interesting! What are some projects on the horizon? KM: The biggest upcoming effort is to make sharding even more transparent (invisible) to developers so they can focus on working with data. Behind the scenes, we will analyze their workload patterns and apply balancing techniques to relocate data in order to squeeze the maximum performance out of the hardware and offer the best possible throughput and latency to users. There are a myriad of technical challenges we will need to solve. For example: how to decide the best placement for workloads that might change dynamically, how to ensure consistency while we are reshuffling in the background, and how to minimize the impact on the customers’ workloads so they are not aware of what is going on behind the scenes. AP: I’m sure our customers will be excited when you roll this out. Now, let’s talk a little bit about you. Why did you join MongoDB? KM: Before joining MongoDB, I worked in Seattle at Microsoft SQL Server and at AWS, where I was thrown in the deep end, working on a service running on thousands of nodes across the globe. One day, while I was on vacation, a recruiter from MongoDB randomly reached out to me. After learning about the new Document Model and how MongoDB is essentially taking the best things from the good old relational databases and making them more scalable and available, I was convinced that this was “the future.” So, I made the jump and moved to New York City. I have been at MongoDB for more than seven years because I still believe the direction we are going is the future. In addition, I love the company culture with respect to giving responsibility to engineers to provide input into the roadmap of their teams, and also with tasking them with doing features of critical importance to the business. AP: You went from Seattle to New York City, and now Spain. Can you tell me more about your move and how that sparked a new Sharding team in Barcelona? KM: After living in the United States for roughly 15 years, I decided to move to Europe. It had always been my dream to live in Barcelona because of the Mediterranean climate and lifestyle, which are very well paired with a good education and technology environment. For example, Universitat Politècnica de Catalunya is a well-known school here that hosts the Barcelona Supercomputing Centre and the Mare Nostrum supercomputer. They conduct research very closely related to what my team does, and a good portion of my team had some tenure there at some point in their careers. Because I was very familiar with the company culture and could mentor this team on our technological base, company values, and processes, MongoDB gave me the opportunity to build a small team in Barcelona to see how things would work out. Initially, we started with just two people. After the first eight months (which included the COVID-19 lockdown), it was obvious the team was very strong and that there is very good talent in Barcelona. Therefore, we decided to scale it up and now we have eight people. AP: I hear you’re planning to hire a few more to the Sharding team in Barcelona. What are the career opportunities for your team? KM: That’s correct. Our team is growing. Since sharding is at the forefront of the company’s products, there are many interesting projects to choose from that solve difficult distributed systems problems. With respect to career growth in general, it’s not much different from our North American teams. Our career growth guidelines are universal. Currently, there are two career paths; individual contributor (IC) and manager. On the Barcelona Sharding team, we have career growth opportunities mostly on the IC path. However, we have discovered that it is best to promote leads from within the team, because they already have established rapport with the team members and can work well with them. So while we are growing initially and we definitely lean on the IC path more, there are lead opportunities too. AP: How do you mentor individual contributors so they can move up on the team? KM: It’s a cliche, but the best way to build skills for new engineers is to “throw them in the deep end” and let them figure out how to swim. When people join, we generally let them ease into the team’s processes for a few weeks and train them on how to use MongoDB as a customer. Then, they spend the next month or so fixing small bugs, investigating failures, and so on. After that, they typically join an ongoing project, and little by little will become responsible for some aspect of the project. Mentorship comes as a byproduct of working together with engineers who have been on the team a long time already, and consists of providing feedback and explaining internals of the system and why things work the way they work historically. I also encourage people to read papers, see what other products are doing, and so forth. AP: What’s your proudest moment leading this team? KM: Realizing about five to six months after the first two engineers started and after we hired our third engineer that we have become a proper team and not that little group of people working out of Europe. Our team members were participating in discussions with the bigger team in New York, defending their ideas and proposing new ones. I believe this helped MongoDB see the value in our team and why we’re able to continue to add more hires in Barcelona. Interested in pursuing a career at MongoDB? We have several open roles on our teams across the globe, and would love for you to build your career with us!

November 17, 2020

Built with MongoDB: Shipright

User research is fundamental to guide great design and define a company’s brand and viability. For this edition of #BuiltWithMongoDB, we are featuring Shipright , a Netherlands-based Techstars company from the Netherlands that enables software businesses to seamlessly track customer feedback and make better-informed product decisions. Shipright provides a central place for user feedback, collating suggestions that customer support receives and sharing it across the company. The tool allows product managers to connect with customers who have left feedback, and ask for more details or provide personalized updates. Merging customer feedback ―traditionally spread around the company and left forgotten inside support conversations―into the product development process enables product people to deeper understand, and act upon, user requests. We spoke with co-founder Steven Aanen about Shipright’s journey to centralize product feedback and how they are growing with MongoDB. How did the idea for Shipright come about? As a team of four college friends, we had collaborated on software products for several years and repeatedly saw how difficult it is for companies to understand what their users really need. The result is that product teams are too-often building based on the input of the loudest person in the room, typically one angry customer or a product manager’s gut feel. With many new products hitting the market, and the cost of switching between them being so low, companies that fail to build something users love won’t survive for long. The good thing is that software companies actually get lots of feedback through customer support, live chat, and online reviews. The problem is that this feedback lives in many places and across different teams. There wasn’t any way to get a clear picture of, and act upon, a user’s perspective. That’s why we created Shipright. Shipright allows companies to easily collect feedback in one centralized location so that product teams get a clear sense of what matters. All feedback is transparent, from real users with associated identities, making it approachable and possible to open a dialogue. Teams that use Shipright collaborate with users to build with them instead of for them. What were some of the early challenges in building Shipright? Product management is inherently a very complex and messy process. There are many factors that come into play when defining product strategy. The challenge was to help streamline the process without creating a solution that is too complex or creates unnecessary overhead. We wanted to make it possible for any team to create amazing products. We pivoted a few times, from focusing on user research to incorporating user perspective deeply into product roadmaps. Another challenge is dealing with the variety of systems in which feedback and user data lives. Our solution is to offer a browser plugin that consolidates feedback across several channels and integrations. What technologies are you using to help build Shipright? Shipright is built in Vue.js, Typescript, NodeJS, and, of course, MongoDB. We run the platform on Kubernetes, where we also deployed a bunch of microservices and a message queue to process asynchronous jobs. A fun fact is that every customer has their own database, which is something we started with to ensure data protection. Why did you decide to have Shipright #BuiltWithMongoDB? MongoDB allows us to be flexible as we learn to support our customers in the best possible way. Functionality changes regularly, and it’s super important for us to be able to adapt our course as needed. We initially picked MongoDB for quick prototyping of the MVP that would later become Shipright, and it stuck. At first, we hosted the product ourselves, which quickly became very challenging. We regularly had to pause our work to fix things in production. With help of our friend David Asabina, we switched to a new hosting infrastructure, including MongoDB Cloud. That really helped us to get our focus back on product development instead of managing servers. Now that we have worked with MongoDB for nearly four years, we deeply understand the API and leverage our compounded knowledge to ship updates even faster. Which MongoDB products do you use to power Shipright? Our hosting infrastructure is defined using Terraform. We have a staging cluster and production cluster in MongoDB Atlas, and if necessary, we can spawn up a new cluster that looks exactly the same. On every signup, one of our microservices creates and prepares a new database specifically for that customer. In the near future, we’d like to try out Atlas Search as well, as we rely heavily on search engines to power the product. We’re fans of the MongoDB ecosystem and are excited to grow with the platform. Want to learn more about Shipright ? Check out the product and their newsletter: Build with Users . Creating something cool with MongoDB? Check out our developer resources , and let us know if you want your startup to be featured in our #BuiltWithMongoDB series.

November 11, 2020

How Growth and Leadership Foster Change In The Tech Space

Editor's note: This was originally published on AfroTech’s website . Are you on the hunt for your next role in the tech space? We know firsthand just how difficult it can be to find a company that is truly committed to investing in Black talent, so we’re highlighting a few of our top picks that are currently hiring. First up is MongoDB — with over 2,200 employees, this New York-based tech company provides database software that powers companies like Epic Games (best known for its popular game Fortnite), Shine Text, Coinbase, 7-Eleven, and more. MongoDB prides itself in cultivating an environment for employees to share their ideas about diversity and inclusion in the workplace without judgment. The general purpose database platform focuses on elevating productivity and scalability for both its clients and employees, which sets up a space for everyone to shine. The company offers opportunities for Black tech professionals to thrive as it values different thoughts and perspectives and those who approach tech solutions in a unique way. No matter their gender, race, age or sexual orientation, employees are valued and respected by MongoDB’s leadership. Its open environment encourages employees to perform at their best and elevates the success of the company. Let’s explore one employee’s experience and his unique role at the company. Tosin Ajayi leads MongoDB’s global corporate Solutions Architect team. He is a prime example of how a Black tech leader creates and influences an inclusive company culture for all employees. Ajayi uses his position to promote growth, leadership and foster change. For example, he’s currently building an Associate Solutions Architects team . The team is suited for junior or early-career professionals and provides them a great start to a highly coveted technical presales career. “The presales role is unique as it combines the technical prowess of an engineer, the vision of a product manager, the sales acumen of a sales rep and the design and troubleshooting skills of a consultant,” Ajayi said. “In essence, it bridges several functions within an organization to bring solutions to our customers and revenue to MongoDB,” all while furthering MongoDB’s goal of inclusion as a top priority. While Ajayi believes that practicing inclusion is everyone’s responsibility, he asserts that MongoDB’s tone about inclusiveness is set at the executive level, which helps such a culture thrive. Here are concrete initiatives that MongoDB has implemented to support diversity in the workplace: The company holds all-hands meetings where the executive team takes open questions. For a company this size, it’s quite impressive considering the diversity of thoughts and opinions in a large employee base. And yes, the questions often reflect that diversity in thoughts and opinions. MongoDB signed the ParityPledge to ensure that at least one qualified woman candidate is interviewed for all VP and higher positions. There is a company-wide Decoding Inclusion series that addresses a variety of topics like race, the LGBTQ+ community, and mental health. MongoDB is really big on feedback. Surveys are consistently run in order to seek to understand employees’ visceral feelings towards their work, their team, team makeup, leaders, workplace, and work conditions. MongoDB has a dedicated D&I team. In fact, this interview is happening as a result of the great work Cindy Class and Danielle James are doing. Companies across the country are tackling current events such as the disproportionate rate of COVID-19 affecting Black people and the violence at the hands of police. These events require open dialogue for employees to express their thoughts and feelings and MongoDB has done just that. Ajayi agrees that it’s important to have these conversations in order to “disambiguate the stance on human decency issues and promote inclusiveness”. “Employees aren’t a monolith,” Ajayi said. “Yes, we’re a collection of driven and talented professionals — but well above that, we’re human beings. Employees want to feel heard, they want to know that their feelings and opinions matter, they want a company whose philosophy they can align with. Talking about current events is a display of awareness, it shows a sense of connectedness to the outside events that can and often affect employees. More importantly, it shows empathy and support for employees.” These candid conversations help Ajayi as a leader, allowing him to “address the historically taboo topic of race and racial injustice.” When topics such as these are addressed it impacts the success of the company positively and creates a “psychologically safe environment” at work. “Another point here is that employees are only as good as they feel,” Ajayi said. “I find that people give more of themselves when they’re in a space where they feel psychologically safe.” MongoDB continues to promote their mission of inclusivity and diversity through various initiatives like scholarships to their MongoDB World conference, an Intern Mentorship program, affinity groups like The Underrepresented People of Color Network (TUPOC), Queeries, MDBWomen, Underrepresented Genders in Tech, Veterans, and the Green Team, and the company’s Decoding Inclusion series that was launched last year by the Diversity and Inclusion team. “The [Decoding Inclusion Series] is an opportunity to educate and sometimes challenge preconceived ideas about D&I,” Ajayi said. “[These sessions] are sponsored by MongoDB executives. We feature employees and bring guest speakers to talk about a variety of topics including race, gender, mental health issues, and other topics that pertain to D&I.” Ajayi revealed that he found sessions like the most recent Decoding Inclusion conversation on race very informative, resulting in his own self-evaluation about his understanding of community and societal differences. He is proud to see these types of programs not only deconstruct the taboo topic of racism in the workplace, but make changes as a result of it. “I encourage all organizations to embrace the humanity of their employees, not just the workers in them, and promote an environment where people can talk, like my company has done for us,” Ajayi added. MongoDB is dedicated to creating opportunities to impact change, not only at the company but throughout the community. Are you a tech-minded dreamer, who is passionate about innovation? Grow your career at MongoDB, view open roles here and make sure to indicate that you learned about the role through AfroTech when applying. Join MongoDB in supporting organizations fighting for racial justice and equal opportunity. Donate to our fund by December 31, 2020 and MongoDB will match the donation up to a maximum aggregate amount of $250,000. Learn more here.

November 10, 2020

Client-Side Field Level Encryption is now on Azure and Google Cloud

We’re excited to announce expanded key management support for Client-Side Field Level Encryption (FLE). Initially released last year with Amazon’s Key Management Service (KMS), native support for Azure Key Vault and Google Cloud KMS is now available in beta with support for our C#/.Net, Java, and Python drivers. More drivers will be added in the coming months. Client-Side FLE provides amongst the strongest levels of data privacy available today. By expanding our native KMS support, it is even easier for organizations to further enhance the privacy and security of sensitive and regulated workloads with multi-cloud support across ~80 geographic regions. My databases are already encrypted. What can I do with Client-Side Field Level Encryption? What makes Client-Side FLE different from other database encryption approaches is that the process is totally separated from the database server. Encryption and decryption is instead handled exclusively within the MongoDB drivers in the client, before sensitive data leaves the application and hits the network. As a result, all encrypted fields sent to the MongoDB server – whether they are resident in memory, in system logs, at-rest in storage, and in backups – are rendered as ciphertext. Neither the server nor any administrators managing the database or cloud infrastructure staff have access to the encryption keys. Unless the attacker has a compromised DBA password, privileged network access, AND a stolen client encryption key, the data remains protected, securing it against sophisticated exploits. MongoDB’s Client-Side FLE complements existing network and storage encryption to protect the most highly classified, sensitive fields of your records without: Developers needing to write additional, highly complex encryption logic application-side Compromising your ability to query encrypted data Significantly impacting database performance By securing data with Client-Side FLE you can move to managed services in the cloud with greater confidence. This is because the database only works with encrypted fields, and you control the encryption keys, rather than having the database provider manage the keys for you. This additional layer of security enforces an even finer-grained separation of duties between those who use the database and those who administer and manage the database. You can also more easily comply with “right to erasure” mandates in modern privacy legislation such as the GDPR and the CCPA . When a user invokes their right to erasure, you simply destroy the associated field encryption key and the user’s Personally Identifiable Information (PII) is rendered unreadable and irrecoverable to anyone. Client-Side FLE Implementation Client-Side FLE is highly flexible. You can selectively encrypt individual fields within a document, multiple fields within the document, or the entire document. Each field can be optionally secured with its own key and decrypted seamlessly on the client. To check-out how Client-Side FLE works, take a look at this handy animation. Client-Side FLE uses standard NIST FIPS-certified encryption primitives including AES at the 256-bit security level, in authenticated CBC mode: AEAD AES-256-CBC encryption algorithm with HMAC-SHA-512 MAC. Data encryption keys are protected by strong symmetric encryption with standard wrapping Key Encryption Keys, which can be natively integrated with external key management services backed by FIPS 140-2 validated Hardware Security Modules (HSMs). Initially this was with Amazon’s KMS, and now with Azure Key Vault and Google Cloud KMS in beta. Alternatively, you can use remote secure web services to consume an external key or a secrets manager such as Hashicorp Vault. Getting Started To learn more, download our Guide to Client-Side FLE . The Guide will provide you an overview of how Client-Side FLE is implemented, use-cases for it, and how it complements existing encryption mechanisms to protect your most sensitive data. Review the Client-Side FLE key management documentation for more details on how to configure your chosen KMS. Safe Harbor The development, release, and timing of any features or functionality described for our products remains at our sole discretion. This information is merely intended to outline our general product direction and it should not be relied on in making a purchasing decision nor is this a commitment, promise or legal obligation to deliver any material, code, or functionality.

November 9, 2020

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