September 7, 2022
MongoDB is pleased to announce the release of the new "Learn MongoDB'' course, created together with Codecademy. Hosted on the Codecademy platform, the course teaches students the basics of MongoDB and how to perform CRUD operations, query and analyze data, and create and use indexes.
With interactive tutorials and quizzes throughout, developers in the “Learn MongoDB” course can educate themselves on the breadth of MongoDB’s developer data platform and learn best practices for building applications on top of MongoDB. And by completing additional coursework on the programming language of their choice on Codecademy, early-career developers can learn how to code across the full application stack. This is a free, self-paced course that takes approximately eight hours to complete.
For developers building new applications, identifying the right solution for their data layer is critical. But equally important is learning how to use their solution of choice for maximum reliability and scalability. In addition to documentation and how-to guides, educational courses like “Learn MongoDB” are invaluable for helping developers harness new solutions to their full potential. MongoDB offers MongoDB University, which helps developers advance their careers with MongoDB courses and certifications; we also partner with leading third-party providers of developer educational experiences like Codecademy.
If you’re a developer new to working with MongoDB, Codecademy’s “Learn MongoDB” course is a great way to get started. Sign up today!
Free your data with the MongoDB Relational Migrator
Nothing is more frustrating than data that is just out of reach. Imagine wanting to combine customer behavior data from your CRM and usage data from your legacy product to trigger tailored promotions in your new mobile app, but not being able to locate the required data in the sea of tables in your relational database. As MongoDB CTO Mark Porter explains in his MongoDB World keynote , the data that can make a difference might be locked up “somewhere that you can’t use.” Relying on his own hard-earned experience with data, Porter adds that this information can be trapped “in a schema with hundreds or thousands of tables that have built up over decades.” “Schema is a huge part of this problem,” MongoDB product manager Tom Hollander explains during a presentation on MongoDB Relational Migrator at MongoDB World 2022. “So we’ve spent a lot of time building out the tools to enable you to map your tabular relational schema into a document schema and make use of the full power of the MongoDB document model.” To see MongoDB Relational Migrator in action, check out this introduction and demo from MongoDB World 2022, featuring MongoDB product manager Tom Hollander. What is MongoDB Relational Migrator? MongoDB Relational Migrator streamlines migrations from legacy data infrastructure to MongoDB by helping developers analyze relational database schemas, convert them into MongoDB schemas, and then migrate data from the source database to MongoDB. Currently, Relational Migrator is compatible with four of the most common relational databases: Oracle, SQL Server, MySQL, and PostgreSQL. Migrator not only moves data from your relational database to MongoDB, but it also transforms it according to your new schema. As Hollander and MongoDB product marketing director Eric Holzhauer point out , developers often use a mix of software and tools (e.g., extract-transform-load pipelines, change data capture (CDC), message queues, and streaming) to execute migrations, which can be complicated, risky, and error-prone. Relational Migrator provides a single tool that can streamline the process while simultaneously ensuring that your data lands in an organized, logical manner. By simplifying schema translations — one of the most complex, difficult parts of any relational migration — Relational Migrator grants developers and other technical teams a greater degree of control over (and increased visibility into) their new MongoDB schema. The result is to make data more accessible for analysis and decision making. “Now I can get at the data in my program without going through a translation layer,” Porter explains. A visual representation of how Migrator maps relational schema to document schema. Migration mode: Snapshot or ongoing? Migrator provides two modes of data transfer: a one-time snapshot or a continuous sync (which will be available later this year). To help decide which mode you should use, consider whether you can move over to MongoDB and immediately decommission your previous database or whether you need to keep your existing relational database up and running. Organizations may wish to keep their relational database for various reasons, such as testing the effectiveness of your proposed document schema, running out the contract or licensing agreement to avoid expensive fees, or keeping old databases available for audits. In this situation, you can keep your relational database running so that Relational Migrator will continue to push data from your source to your new MongoDB clusters. The limits of Relational Migrator As Hollander points out, Relational Migrator is only a tool — one intended to facilitate schema mapping, providing many abilities and options for effective schema design. “It’s not a silver bullet that will immediately modernize your application portfolio,” Hollander says. “It’s not going to do everything for you. You still have to do the planning.” Furthermore, because database schema is a tricky topic even for seasoned experts, Hollander recommends that developers would benefit from working with architects, consultants, and partners — especially if they’re not familiar with MongoDB or schema design best practices. Relational Migrator does not yet support continuous replication, which would enable your relational database and MongoDB clusters to coexist for an extended period of time. However, Hollander says that work on this feature is ongoing and it will be available in the future, along with additional capabilities like schema recommendations, an integration for the MongoDB Atlas developer data platform, and more. MongoDB Relational Migrator is currently in early access, for use on non-production workloads with assistance from our Product and Field Engineering teams. To learn more, get in touch with your MongoDB rep or contact us via our Migrator page to discuss your workload and next steps.
Choosing the Right Tool for the Job: Understanding the Analytics Spectrum
Data-driven organizations share a common desire to get more value out of the data they're generating. To maximize that value, many of them are asking the same or similar questions: How long does it take to get analytics and insights from our application data? What would be the business impact if we could make that process faster? What new experiences could we create by having analytics integrated directly within our customer-facing apps? How do our developers access the tools and APIs they need to build sophisticated analytics queries directly into their application code? How do we make sense of voluminous streams of time-series data? We believe the answer to these questions in today's digital economy is application-driven analytics. What is Application-Driven Analytics? Traditionally, there's been a separation at organizations between analytics that run the business and analytics that manage the business. They're built by different teams, they serve different audiences, and the data itself is replicated and stored in different systems. There are benefits to the traditional way of doing things and it's not going away. However, in today's digital economy, where the need to create competitive advantage and reduce costs and risk are paramount, organizations will continue to innovate upon the traditional model. Today, those needs manifest themselves in the demand for smarter applications that drive better customer experiences and surface insights to initiate intelligent actions automatically. This all happens within the flow of the application on live, operational data in real time. Alongside those applications, the business also wants faster insights so it can see what's happening, when it's happening. This is known as business visibility, and the goal of it is to increase efficiency by enabling faster decisions on fresher data. In-app analytics and real-time visibility are enabled by what we call application-driven analytics. Find out why the MongoDB Atlas developer data platform was recently named a Leader in Forrester Wave: Translytical Data Platforms, Q4 2022 You can find examples of application-driven analytics in multiple real-world industry use cases including: Hyper-personalization in retail Fraud prevention in financial services Preventative maintenance in manufacturing Single subscriber view in telecommunications Fitness tracking in healthcare A/B testing in gaming Where Application-Driven Analytics fits in the Analytics Ecosystem Application-driven analytics complements existing analytics processes where data is moved out of operational systems into centralized data warehouses and data lakes. In no way does it replace them. However, a broader spectrum of capabilities are now required to meet more demanding business requirements. Contrasting the two approaches, application-driven analytics is designed to continuously query data in your operational systems. The freshest data comes in from the application serving many concurrent users at very low latency. It involves working on much smaller subsets of data compared to centralized analytics systems. Application-driven analytics is typically working with hundreds to possibly a few thousand records at a time. And it's running less complex queries against that data. At the other end of the spectrum is centralized analytics. These systems are running much more complex queries across massive data sets — hundreds of thousands or maybe millions of records, and maybe at petabyte scale — that have been ingested from many different operational data sources across the organization. Table 1 below identifies the required capabilities across the spectrum of different classes of analytics. These are designed to help MongoDB’s customers match appropriate technologies and skill sets to each business use case they are building for. By mapping required capabilities to use cases, you can see how these different classes of analytics serve different purposes. If, for example, we're dealing with recommendations in an e-commerce platform, the centralized data warehouse or data lake will regularly analyze vast troves of first- and third-party customer data. This analysis is then blended with available inventory to create a set of potential customer offers. These offers are then loaded back into operational systems where application-driven analytics is used to decide which offers are most relevant to the customer based on a set of real-time criteria, such as actual stock availability and which items a shopper might already have in their basket. This real-time decision-making is important because you wouldn't want to serve an offer on a product that can no longer be fulfilled or on an item a customer has already decided to buy. This example demonstrates why it is essential to choose the right tool for the job. Specifically, in order to build a portfolio of potential offers, the centralized data warehouse or data lake is an ideal fit. Such technologies can process hundreds of TBs of customer records and order data in a single query. The same technologies, however, are completely inappropriate when it comes to serving those offers to customers in real time. Centralized analytics systems are not designed to serve thousands of concurrent user sessions. Nor can they access real-time inventory or basket data in order to make low latency decisions in milliseconds. Instead, for these scenarios, application-driven analytics served from an operational system is the right technology fit. As we can see, application-driven analytics is complementary to traditional centralized analytics, and in no way competitive to it. The benefits to organizations of using these complementary classes of analytics include: Maximizing competitive advantage through smarter and more intelligent applications Out-innovating and differentiating in the market Improving customer experience and loyalty Reducing cost by improving business visibility and efficiency Through its design, MongoDB Atlas unifies the essential data services needed to deliver on application-driven analytics. It gives developers the tools, tech, and skills they need to infuse analytics into their apps. At the same time, Atlas provides business analysts, data scientists, and data engineers direct access to live data using their regular tools without impacting the app. For more information about how to implement app-driven analytics and how the MongoDB developer data platform gives you the tools needed to succeed, download our white paper, Application-Driven Analytics: Defining the Next Wave of Modern Apps .