Encryption at rest is a new feature that is available for MongoDB Enterprise when using the WiredTiger Storage Engine. Although you can evaluate MongoDB Enterprise using the evaluation agreement, you would need a MongoDB Enterprise subscription for each server to use it in production.
With a MongoDB Enterprise Advanced subscription, you can enable encryption at rest via Cloud Manager (using Automation). You would need to pass the encryption parameters, as mentioned in Configure Encryption through the The Cloud Manager Advanced Options.
Please note that as the system key is external to the server (i.e. kept separate from the data and the database keys), and therefore still requires external management. Please refer to Key Management for more details on this. If you are interested in MongoDB Enterprise Advanced subscription, please contact a MongoDB Account Executive.
Getting Started with MongoDB Compass
MongoDB’s flexible schema and rich document structure allow developers to quickly build applications with rich data structures. However, this flexibility can also make it difficult to understand the structure of the data in an existing database. Until now, if you wanted to understand the structure of your data, you would have to use the MongoDB shell to issue queries and view data at the command line. There has to be a better way -- enter MongoDB Compass . What is MongoDB Compass? MongoDB 3.2 introduces MongoDB Compass -- a graphical tool that allows you to easily analyse and understand your database schema, as well as allowing you to visually construct queries, all without having to know MongoDB’s query syntax: MongoDB Compass was built to address 3 main goals: Schema discovery Data discovery Visual construction of queries Schema Discovery Compass displays the data types of fields in a collection’s schema. The example below is taken from a mock dataset that I use when test driving Compass. It reports that there are documents in the collection that contain a field last_login with the type date: Compass also displays a percentage breakdown for fields with varying data types across documents. In this example, 81% of documents store phone_no as a string, and the remaining 19% store it as a number: For sparse fields, where some documents omit a value, Compass displays the percentage of missing values as “undefined.” Here, the age field is missing in 40% of the sampled documents. This is exceptionally useful to understand whether your application is storing data the way that you expect it to. Imagine the case where you have a field showing a mix of strings and numbers - perhaps there is an application bug somewhere that has crept in and is storing data with a different type than it should be? Data Discovery Compass is able to show histograms to represent the data frequency and distribution within a collection. For example, here is a data set containing the age of users. We can see the minimum age is 16, the maximum age is 56 and the most popular age is late 30’s (the exact value is shown by hovering over the bar itself). Here’s another example using a field that stores names. Compass will display a random selection of string values for the field: Visual Construction of Queries Do you want an easier way to type out a MongoDB query? Charts in Compass are fully interactive. Clicking on a chart value or bar will automatically build a MongoDB query that matches the selected range in the interface. In the example below, clicking on the “JFK” bar builds a query matching all documents whose departureAirportFsCode field matches “JFK”: Clicking on other field values adds the field and range to the selection, creating a more complex query. Continuing with our example, we can select a particular flightId in addition to departures from JFK Airport. Once you hit the Apply button, Compass will execute the query and bring back the results! It’s as easy as it sounds. You can be building queries with a few clicks of a button in no time at all. One final thing to mention - we didn’t forget about the JSON. Documents can be examined in the document viewing pane. This can be expanded by clicking on the Document Viewer icon on the right-hand side of the page: I know you must be wondering - where can I get this thing?! Well, MongoDB Compass is available in the download center on mongodb.com . It comes included for production use with our subscriptions, both MongoDB Professional and MongoDB Enterprise Advanced. MongoDB Compass can also be used for free in a development environment. This is only version 1.0 of Compass - there is lots of great functionality to come. I’m super excited to be part of the Compass team and I can’t wait for the next set of releases. Give MongoDB Compass a try today. Download MongoDB Compass About the author - Sam Weaver Sam Weaver is the Product Manager for Developer Experience at MongoDB based in New York. Prior to MongoDB, he worked at Red Hat doing technical presales on Linux, Virtualisation, and Middleware. Originally from Cheltenham, England, he received his Bachelors in Computer Science from Cardiff University. Sam has also cycled from London to Paris, competed in several extreme sports tournaments such as ToughMudder, and swam with great white sharks.
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.