MongoDB introduces Cloud platform

“Developers today are expected to leverage a myriad of technologies, data models, APIs and languages across disparate systems in order to support the transactional, search and analytical features that users demand in modern applications. And while cloud computing has revolutionized the tech industry, providing a low cost of entry and unlimited scale among other proven benefits, most cloud migrations have merely replicated the complexities and drawbacks of the traditional datacenter,” said Dev Ittycheria, President & CEO of MongoDB. “With MongoDB Cloud, developers can finally leave the burden of data silos and sprawl behind and truly unlock the value of data through a unified development experience.”

The Leading Modern Database Designed For Any Workload With MongoDB’s document data model, developers can structure data any way the application requires – from rich, hierarchical objects to simple key-value pairs and tables to connected graphs – and then query it with a single API. This gives developers a consistent and highly productive experience across the broadest set of workloads. Business critical transactional and analytical applications run on MongoDB at leading organizations in every industry: financial services, healthcare, telecoms, insurance, gaming and more. 

The announcement of MongoDB 4.4 furthers the company’s objective of continuing to provide developers with the leading modern general purpose database. MongoDB 4.4 delivers the features and enhancements most demanded by the MongoDB community. The result is a database designed to enable users to build transactional, operational and analytical applications faster and more efficiently than any other database. MongoDB 4.4 allows developers to scale applications globally, with the flexibility to define and refine the distribution of data at any time as requirements evolve while delivering the most sophisticated latency, resilience and security controls anywhere in the cloud.

Notable new features include:
● Union: Empowers users with richer and faster analytics to make better decisions while reducing dependencies on fragile ETL processes and expensive data warehouses 

● Refinable shard keys: Allows for easier scale-out of MongoDB, with the ability to modify the locations of data at any time as applications and business requirements evolve

● Hedged reads: Delivers consistent and predictable performance – even when some nodes might not be working optimally –  by submitting read requests to multiple replicas and returning results to the client as soon as the quickest node responds

A Consistent Experience with Search and Analytics
The addition of Atlas Data Lake and Atlas Search to the MongoDB Cloud platform simplifies modern data infrastructure, extends applications with rich search experiences and unlocks the power of analytics for data archived in a data lake. Using the same MongoDB Query Language (MQL) and data model that has driven MongoDB’s popularity with developers, with Atlas Data Lake a user can run a query and have the data brought back to them: whether it is real-time transactional data in the global Atlas global cloud database or a relevance-based search query with Atlas Search or a long-running analytical query on data in object storage. Using MongoDB Cloud, developers no longer need to deal with the cognitive burden of flipping back and forth between multiple technologies, query languages and data models.  “As the number and type of databases and data sources incorporated into modern applications has exploded over the last decade, the challenge for developers has been scaling their understanding of the multitude of interfaces” said Stephen O’Grady, Principal Analyst with RedMonk. “In response to this, organizations that value speed have increasingly been looking for abstractions that can serve as a single interface capable of traversing multiple back-ends. This is the opportunity that MongoDB is built for.”
 
Atlas Data Lake allows users to conveniently connect to their existing S3 storage buckets with a few clicks from the MongoDB Atlas UI in order to run queries and explore their data using the power of MQL. Atlas Data Lake is completely serverless, so there is no infrastructure to set up, manage or optimize, and customers pay only for the queries they run when actively working with the data. 

With Atlas Data Lake users have access to:

● Atlas Online Archive: Data is tiered across fully managed databases and cloud object storage, with the ability to query the data seamlessly via a single query. By automatically archiving historical data, customers save on transactional database storage costs while still being able to easily query that data 

● Federated queries: Eliminates the cost and complexity of moving and transforming data by enabling users to run a single query across Atlas and historical data on Amazon S3, returning a single result

● Persist Aggregations to Amazon S3 & Atlas: Provides users greater flexibility to persist results of complex aggregations to their preferred storage tier, exposing new data-driven insights for real-time applications in a cost-effective manner
 
Search has become a table-stakes feature for every application, but significant developer and operational challenges remain. Atlas Search is deeply integrated with the Atlas cloud database with a consistent API so users do not need to spin up a separate search engine and synchronize data movement between different data silos. Once indexes have been created using either the Atlas UI or API, developers can run sophisticated search queries using MQL, saving significant effort, time and money.

“It’s almost unthinkable to build a modern application user experience without a relevancebased search capability. Unfortunately, this is still a complex task that requires developers to spin up a search database, maintain data synchronization, and scale it independently from their core database of record,” said Sahir Azam, Chief Product Officer, MongoDB. “Atlas Search eliminates all of these headaches by offering developers a search engine that uses the same language and data model of the core database. MongoDB believes search is an extension of the foundational layer upon which modern applications are built, so we’ve made it that easy for developers. Just query data and let us worry about the rest.”

The Best Mobile Database Syncs Up with Atlas Users expect modern mobile apps to be highly responsive, reliable, work offline or with inconsistent network connectivity, and immediately synchronize data as changes happen on the mobile client or the backend. Moreover, users expect applications not to drain battery life, crash, or require excessive network data. As a result, the amount of effort and time required to develop great mobile apps is not inconsequential. 

In 2019, MongoDB announced its acquisition of the popular open source mobile database and synchronization platform, Realm.io, to help developers build rich, mobile applications more quickly. MongoDB Realm, now generally available, integrates with MongoDB’s serverless platform to give developers a uniform and easier way to work with data all the way through the application lifecycle – from the front to the backend.

An example of the power of MongoDB Realm is the new feature, Realm Sync, which enables bidirectional data synchronization between Realm’s mobile client on the front end and Atlas on the backend. This allows for data to be seamlessly shared between devices and with the backing database without complex conflict resolution and integration code.

Using MongoDB Realm, 7-Eleven built an inventory management system that takes advantage of Realm Sync. “What we’ve created is really innovative. Since rolling the application built on MongoDB Realm out to all 8,500 stores in North America, we’ve been able to sync data across more than 20,000 devices on a nearly real-time basis,” said Srikanth Gandra, Director of Digital Technology, 7-Eleven. “We’ve heard good feedback from store managers. They can start using devices immediately, rather than waiting minutes to download the data on initial startup, like they used to. Data accuracy, especially around inventory when sales happen or shipments arrive, has really improved.