How does the UPS i-parcel service give online shoppers a native checkout experience in more than 100 countries, and with 70 different currencies? In our webinar Yursil Kidwai, Vice President of Technology at UPS i-parcel, explains how an infrastructure of MongoDB powered microservices helps answer that question.
UPS i-parcel is a global e-commerce and operations service that enables retailers with operations in the United States and the United Kingdom to sell across borders to online shoppers in their native language and currency. UPS i-parcel dynamically provides shoppers with a summary of all landed costs, including duties and taxes, in the retailer’s shopping cart so there are no surprises when the order arrives. It is part of a broad range of UPS ecommerce solutions that results in increased shopping cart conversions and consumer loyalty.
Retailers that use UPS i-parcel report increased customer satisfaction, lower return rates, and even the elimination of fraudulent orders. Both sellers and shoppers benefit from reduced time in transit, tracking visibility and simplified transactions. However, in order to deliver that experience, UPS i-parcel needs to process a massive variety and volume of data in near real time.
Earlier this year, at MongoDB World 2016, Yursil explained why UPS i-parcel needed to migrate away from traditional relational databases to process that wide variety and high volume of data. He also revealed a new open source tool his team developed internally that allows developers to easily take the model–view–controller (MVC) programming pattern promoted within ASP.NET and expand it to scale.
MongoDB based microservices have become another important part of UPS i-parcel’s strategy. The application is implemented as a set of microservices distributed across the cloud to ensure there’s not a single point of failure. If you want to find out more about UPS i-parcel’s journey to microservices and MongoDB, watch the Enabling Microservices from Startups to the Enterprise Webinar.
MongoDB named a leader in The Forrester Wave™: Document Stores, Q3 2016
Today, Forrester released The Forrester Wave™: Document Stores, Q3 2016, recognizing MongoDB as a Leader based on our current offering, strategy, and market presence. The report said that "MongoDB is one of the most popular document stores." As you may recall, a few weeks ago Forrester published another important piece of research on databases, The Forrester Wave™: Big Data NoSQL, Q3 2016. In that report we were also acknowledged as a leader, with a “5 out of 5” score in 19 of the 26 criteria. In this latest report, Forrester evaluates the document database capabilities of a range of database technologies, from MongoDB to traditional relational databases. The very existence of such a report is remarkable, but beyond our position as a Leader, I see in this report evidence that consensus has come to endorse our vision of what the world needs in a datastore. The first release of MongoDB was just over 7 years ago. One of our underlying beliefs was that the document data model is the right way to model data, for a number of reasons. Documents are more flexible and inherently more agile than the relational model; they map to the objects of modern programming models; they are easier and more natural for developers to reason about; and, it turns out, large volumes of documents are much easier to scale to meet the needs of cloud infrastructure and modern workloads. Back in 2009, traditional relational vendors did not hold the same convictions of the importance of the document model. But now, just a few years later, virtually every mainstream database supports the document data model. The reason is clear - the market has embraced the document model, and vendors have either joined the document revolution, or they’re getting left behind. The world is ready for a document database to be its default. 61% of the enterprises surveyed by Forrester for the Big Data NoSQL Wave are using, planning to use, expanding or upgrading to NoSQL over the next 12 months, and we are confident that MongoDB will continue to be the most popular choice. I believe that Forrester’s research makes a critical point - not all document databases are created equal. We developed MongoDB with a broad range of use cases in mind, which is why it excels at so many workloads. Our document model is a superset of other data models, including key-value, graph, object, and relational, and we natively support complex manipulations on these data with operators like $lookup and our new graph operators in 3.4. But it’s not just the data model that makes MongoDB unique. Modern applications require flexible approaches to “always on” global deployments, and easy ways to meet demanding SLAs. Our replication and sharding architecture, pluggable storage engine framework, and tunable consistency mean that an entire spectrum of data semantics can be achieved through configuration, rather than by mixing and matching from a grab-bag of different database products. Another central aspect of our vision is that embracing the flexibility of the document data model does not require sacrificing the ability to safeguard data integrity. While this may be true with most document stores, including relational databases, with MongoDB this is not the case at all. MongoDB’s document validation features allow you to be incredibly strict in how you enforce your schema, from just a few fields, to every field in your model, to no validation at all. Best of all, we don’t require you learn a new language to express schema; instead, we rely on the find() syntax that every MongoDB developer and DBA knows today, which also means we can take advantage of Boolean, geospatial, data typing, wildcard expressions and more - it’s incredibly powerful. Our tools and integrations for MongoDB meet the needs of a broad range of enterprise users. From our beautiful GUI for the database, MongoDB Compass , to our powerful Connector for BI which provides SQL access for analysis, to our management tools like Ops Manager and Cloud Manager , which provide a comprehensive suite of monitoring, automation, and backup and point-in-time recovery capabilities - we’ve got you covered. We're also innovating in the next generation of analytics, machine learning, and streaming with our new MongoDB Connector for Apache Spark . To summarize, our vision for the modern datastore incorporates the flexibility and power of the document model, handles high availability and scale out as core features, retains the ability to safeguard data integrity, and affords enterprises the ability to leverage an ecosystem of analytical tools, and one last thing... it is a first-class citizen of the cloud. This is why we created our database as a service, MongoDB Atlas: the simplest, most robust, and most cost effective way to run MongoDB in the cloud. Using MongoDB Atlas , enterprises can spin up a fully managed, monitored, and backed up cluster with the click of a button, in just a few minutes. Now, regardless of what type of infrastructure an enterprise wants to run, they have the flexibility to deploy and manage MongoDB with ease. Learn how a database can make your organization faster, better, leaner About the Author, Eliot Horowitz Eliot is CTO and Co-Founder of MongoDB. He is one of the core MongoDB kernel committers. Previously, he was Co-Founder and CTO of ShopWiki. Eliot developed the crawling and data extraction algorithm that is the core of its innovative technology. He has quickly become one of Silicon Alley's up and coming entrepreneurs and was selected as one of BusinessWeek's Top 25 Entrepreneurs Under Age 25 nationwide in 2006. Earlier, Eliot was a software developer in the R&D group at DoubleClick (acquired by Google for $3.1 billion). Eliot received a BS in Computer Science from Brown University.
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.