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.
4 Ways MongoDB Enhances Your Google BigQuery Experience
MongoDB and Google Cloud continue to build on their partnership, with MongoDB enhancing Google Cloud with pay-as-you-go abilities, unified billing, and integrations with multiple different GC features, including BigQuery . And, when it comes to data architecture, BigQuery and MongoDB are two products that are better together. Google BigQuery and MongoDB are better together Google’s serverless data warehouse, BigQuery, was launched in 2011 with an aim to enhance business agility as their cloud-native data warehouse. BigQuery allows for fast queries that can uncover insights using familiar SQL. When MongoDB is added to the database technology stack as a complementary technology, it enhances the breadth of capabilities for the developer across a variety of use cases, including the following four examples. Combined impact of the Enterprise Data Warehouse and the Operational Data Store BigQuery is best suited as an Enterprise Data Warehouse (EDW), meaning it is designed to optimize long-running analytics. MongoDB Atlas , on the other hand, is best suited as an Operational Data Store (ODS), designed to optimally support high throughput and highly concurrent real-time operational applications that demand random access to an entity’s data in native JSON. This combination means that BigQuery and MongoDB are complementary technologies that can jointly deliver more value — each delivering on their strongest qualities. BigQuery excels at long-running queries, while Atlas handles the real-time operational application needs with thousands of concurrent sessions and millisecond response times. Enriched end-customer experiences BigQuery enables data scientists and analysts with machine learning (ML) models and BI tools for structured and semi-structured data at scale. For roles that need results with a turnaround time of a day or more, BigQuery is a strong tool for big data queries. With MongoDB Atlas, engineers and development teams can build applications faster and handle highly diverse schema, query, and update patterns, adapting to demanding user needs and competition. Atlas can also deliver the real-time or less than 24-hour queries that are necessary to keep your business operational. Additionally, data can easily move back and forth between the two platforms, creating a prime combination for running analytics on operational data. Being able to unlock the full potential of your data across your organization means that everyone has the insight into the business metrics they need, when they need it. This allows quicker decision making, as well as stronger and more accurate reporting. Extensibility to MongoDB Atlas features On top of the value and synergy that can be realized by a BigQuery+Atlas combination, other Atlas features can help enhance the usefulness and sophistication of a data architecture, such as: Atlas Charts can be leveraged to create rich visualizations of any data stored within Atlas. Atlas Triggers and Alerts can apply database logic in response to events or on a predefined schedule. Atlas Search brings full-text search at scale to all data across MongoDB and BigQuery alike. Atlas Data Federation enables aggregating data across multiple data sources, such as Atlas clusters and HTTPS endpoints, and transforming it into analytical formats (e.g., Parquet). This means you can not only access data in real-time, but you can also analyze it in a visual, user-friendly way. This functionality makes your data more actionable, allowing you not only to answer questions about your business data but also make better predictions and future adjustments based on it. Furthermore, being alerted to certain data-based events and triggering new actions based on that information means you can have your data working more efficiently for you, freeing up time to innovate and focus on core business competencies. Lastly, this approach simplifies your data lifecycle, so JSON data from various applications and endpoints can easily be transformed and consumed for rich analytics. Deeper understanding of your customer Businesses can use fully managed MongoDB Atlas to store customer 360 profiles. A 360-degree view of a customer allows businesses to track an individual customer’s journey across multiple channels, devices, purchases, and interactions, and improves customer satisfaction. With the combination of Atlas and BigQuery, businesses can also use compiled data — such as, transactional data, behavioral data, user profile and segmentations, and business analytics — to match user profiles with products and services using Artificial Intelligence (AI). Vertex AI , a managed machine learning platform, provides all the Google cloud services in one place to deploy and maintain AI models. Being able to easily access a 360 view for each customer and have automation around their customer journey helps with customer engagement and loyalty by improving customer satisfaction and retention through personalization and targeted marketing communications. It also enables retailers to aggregate customer interactions across all channels and identify valuable new customers. Google BigQuery and MongoDB Atlas in the real world Current , a leading U.S. challenger bank, uses innovative approaches, services, and technologies to serve people overlooked by traditional banks, regardless of age or income level, to help improve their financial outcomes. To help create customer experiences that cannot exist in traditional systems, Current chose to leverage Google Cloud, including BigQuery, with MongoDB layering the platform to achieve their goals. Read Full Current Story Are you a Google BiqQuery customer that is curious about how MongoDB Atlas can amplify your existing data warehouse or data lake architecture? Try MongoDB Atlas for free today and spin up your first workload in minutes. Try pay-as-you-go Atlas on GC Marketplace