High-end retailer in Germany delivers omni-channel shopping experience on MongoDB Atlas for thousands of daily online users
The importance of delivering an optimized customer experience cannot be overstated, especially if your business is high-end retail. For Breuninger, the customer-first approach has been in their DNA for more than 130 years.
When the top German retailer set out to build a new e-commerce platform, they wanted the online experience to match that of walking in to one of Breuninger’s premium department stores. Accomplishing this goal required a feature-rich, high-performance, and reliable database capable of supporting complex data sets across multiple categories.
“Today, our development teams have a lot of independence. We only have a handful of rules about how they design and build applications within their respective business units,” says Benedikt Stemmildt, Lead Software Architect of E. Breuninger GmbH & Co. “It’s not quite a rule that you have to use MongoDB, but you do have to explain yourself if you don’t.”
However, it wasn’t always this way. Breuninger’s previous platform was built on one of the industry-standard product content management (PCM) platforms, which Stemmildt felt was “monolithic and difficult to code for.” Code freezes were common and the underlying architecture was a frequent cause of frustration for an organization striving to adopt more agile processes.
A new development and feature roll-out approach was needed to execute the company’s aggressive omni-channel integration plans, and time to market for new online features became a top priority. Breuninger decided to build a technology group in response, going from 10 to 30 in-house developers in just a year.
“We broke down our monolithic architecture and split our application into separate microservices that reflect how our customers shop in the physical stores,” Stemmildt says. “It’s the customer journey — they search, discover, evaluate, and buy not just individual products, but complete outfits.”
“To reflect this architectural change, we split our development teams by different steps of the customer journey and kept dependencies to an absolute minimum,” Stemmildt continues. “One key to making this work is a high-performance database capable of working easily with data in lots of different ways. The document model of MongoDB means we can deliver data with the quality and detail that reflects our products and shopping experience.”
The result? Much faster time to market. Breuninger was able to build their omni-channel platform in months rather than years by enabling teams to decide on important architectural components for their own sections, without having to ask the permission of other teams.
As a seven-year veteran of MongoDB, Stemmildt was confident in recommending the database to his organization. “There are a lot of good databases,” he says. “However, many of them require developers to have a deep knowledge about how they work before getting any benefit. MongoDB is not like that. It’s very quick to learn and start getting results. Our teams are able to deliver features straight away. Once users do expand their use of the database, it’s so feature-rich that you never get a sense of having to push it beyond what it was designed for.”
And agile wouldn’t be agile without automation. “Everything we deploy is automated, and with MongoDB Atlas on AWS, the deployment and management of our databases fit neatly into our processes. After a period of operating MongoDB ourselves on EC2, it’s great not having to worry about the details and not having to spend time setting up, configuring, and managing database[s]. You free up a lot of opportunities to add value to your service by not running things yourself.”
AWS offers a healthy mix of other tools for the teams at Breuninger to leverage, such as a managed Kubernetes service and serverless Lambda functions. MongoDB Atlas and AWS also help Breuninger stay on the right side of the regulators. “We need to comply with GDPR so we keep everything running within our borders. MongoDB Atlas’s built-in security features have helped us satisfy these requirements.”
The finished platform might look different to someone who is used to traditional architectures, but to Stemmildt, not being restrained by legacy approaches makes a lot of sense. “Each of our teams owns one or more sections of the customer journey. The search team updates its own database, pulling data in from the product data producer via a feed and re-populating its own database as needed. We don’t have to ripple refreshes out across the system as they happen. That means each team is free to add new features without changing some core database component and affecting other teams. Self-contained systems are an important design rule.”
And although there are some 25 different and largely independent systems, the customers see just one website. A front-end proxy uses server-side includes to marshal data as required from a mix of micro-frontends before delivering the final composite to the shopper. Product data, product availability, outfit data, price information, navigation metadata — these are all woven together from separate MongoDB databases as the customer goes through the shopping experience online.
Comparing a microservices architecture to a monolithic one revealed to Breuninger that some metrics don’t matter as much as they once did, while others matter more. “With multiple teams developing things so rapidly, I don’t know exactly how much total data is in play. But we are a very metrics-driven company, not just in the technical infrastructure but across the business. We know when a component is and is not working well from both a technical and business perspective, if it needs optimizing for performance, or whether it is delivering value to the business or we need to revisit that aspect of the system architecture.”
While Stemmildt couldn’t comment too much on future plans, he’s enthusiastic about MongoDB’s part in whatever they may be. “We wanted high performance, but most importantly we wanted to be able to add more features. We’re not using MongoDB’s graph database feature yet, but we may be by the end of the year. There are a lot of things we could do with text search, too.”
Other new features — such as multi-document transaction support in MongoDB 4.0 — may also be useful, but in unorthodox ways. “I don’t actually think transactions are needed anymore for our platform,” he laughs, “But there are some teams, like the customer data team, who don’t agree with me yet and won’t use MongoDB because of that. So the release of MongoDB 4.0 will help me to help them make the transition.”
While customers won’t see the nuts and bolts of Breuninger’s transformation to a data-driven enterprise, they will benefit from the company’s newly integrated omni-channel platform, which delivers an improved customer experience and more ways to get inspired.
And to anyone thinking about using MongoDB on their next project, Stemmildt has just one piece of advice: “Use it. Get a MongoDB Atlas account, create a cluster, and play with it. The way we see it, after the majority of our teams have naturally adopted MongoDB, if you can’t say why you should use another database, then you should just use MongoDB.”
New to MongoDB Atlas — Global Clusters Enable Low-Latency Reads and Writes from Anywhere
The ability to replicate data across any number of cloud regions was introduced to MongoDB Atlas, the fully managed service for the database, last fall. This granted Atlas customers two key benefits. For those with geographically distributed applications, this functionality allowed them to leverage local replicas of their data to reduce read latency and provide a fast, responsive customer experience on a global scale. It also meant that an Atlas cluster could be easily configured to failover to another region during cloud infrastructure outages, providing customers with the ability to provision multi-region fault tolerance in just a few clicks.
But what about improving write latency and addressing increasingly demanding regulations, many of which have data residency requirements? In the past, users could address these challenges in a couple of ways. If they wanted to continue using a fully managed MongoDB service, they could deploy separate databases in each region. Unfortunately, this often resulted in added operational and application complexity. They could also build and manage a geographically distributed database deployment themselves and satisfy these requirements using MongoDB’s zone sharding capabilities.
Today we’re excited to introduce Global Clusters to MongoDB Atlas. This new feature makes it possible for anyone to effortlessly deploy and manage a single database that addresses all the aforementioned requirements. Global Clusters allow organizations with distributed applications to geographically partition a fully managed deployment in a few clicks, and control the distribution and placement of their data with sophisticated policies that can be easily generated and changed.
Improving app performance by reducing read and write latency
With Global Clusters, geographically distributed applications can write to (and of course, read from) local partitions of an Atlas deployment called zones. This new Global Writes capability allows you to associate and direct data to a specific zone, keeping it in close proximity to nearby application instances and end users. In its simplest configuration, an Atlas zone contains a 3-node replica set distributed across the availability zones of its preferred cloud region. This configuration can be adjusted depending on your requirements. For example, you can turn the 3-node replica set into multiple shards to address increases in local write throughput. You can also distribute the secondaries within a zone into other cloud regions to enable fast, responsive read access to that data from anywhere.
The illustration above represents a simple Global Cluster in Atlas with two zones. For simplicity’s sake, we’ve labeled them blue and red. The blue zone uses a cloud region in Virginia as the preferred region, while the red zone uses one in London. Local application instances will write to and read from the MongoDB primaries located in the respective cloud regions, ensuring low latency read and write access. Each zone also features a read-only replica of its data located in the cloud region of the other one. This ensures that users in North America will have fast, responsive read access to data generated in Europe, and vice versa.
Satisfying data residency for regulatory requirements
By allowing developers to easily direct the movement of data at the document level, Global Clusters provide a foundational building block that helps organizations achieve compliance with regulations containing data residency requirements. Data is associated with a zone and pinned to that zone unless otherwise configured.
The illustration below represents an Atlas Global Cluster with 3 zones — blue, red, and orange. The configuration of the blue and red zones are very similar to what we already covered. Local application instances read and write to nearby primaries located in the preferred regions — Virginia and London — and each zone includes a read-only replica in the preferred cloud region of every other zone for serving fast, global reads. What’s different is the orange zone, which serves Germany. Unlike data generated in North America and the UK, data generated in and around Germany is not replicated globally; instead, it remains pinned to the preferred cloud region located in Frankfurt.
Deploying your first Global Cluster
Now let’s walk through how easy it is to set up a Global Cluster with MongoDB Atlas.
In the Atlas UI, when you go to create a cluster, you’ll notice a new accordion labelled Global Cluster Configuration. If you click into this and enable “Global Writes”, you’ll find two easy-to-use and customizable templates. Global Performance provides reasonable read and write latency to the majority of the global population and Excellent Global Performance provides excellent read and write latency to majority of the global population. Both options are available across AWS, Google Cloud Platform, and Microsoft Azure.
You can also configure your own zones. Let’s walk through the setup of a Global Cluster using the Global Performance template on AWS. After selecting the Global Performance template, you’ll see that the Americas are mapped to the North Virginia region, EMEA is mapped to Frankfurt, and APAC is mapped to Singapore.
As your business requirements change over time, you are able to switch to the Excellent Global Performance template or fully customize your existing template.
Customizing your Global Cluster
Say you wanted to move your EMEA zone from Frankfurt to London. You can do so in just a few clicks. If you scroll down in the Create Cluster Dialog, you’ll see the Zone configuration component (pictured below). Select the zone you want to edit and simply update the preferred cloud region.
Once you’re happy with the configuration, you can verify your changes in the latency map and then proceed to deploy the cluster.
After your Global Cluster has been deployed, you’ll find that it looks just like any other Atlas cluster. If you click into the connect experience to find your connection string, you’ll find a simple and concise connection string that you can use in all of your geographically distributed application instances.
Configuring data for a Global Cluster
Now that your Global Cluster is deployed, let's have a look at the Atlas Data Explorer, where you can create a new database and collection. Atlas will walk you through this process, including the creation of an appropriate compound shard key — the mechanism used to determine how documents are mapped to different zones.
This shard key must contain the
location field. The second field should be a well-distributed identifier, such as
userId. Full details on key selection can be found in the MongoDB Atlas docs.
To help show what documents might look like in your database, we’ve added a few sample documents to a collection in the Data Explorer. As you can see above, we’ve included a field called
location containing a ISO-3166-1 alpha 2 country code ("US", "DE", "IN") or a supported ISO-3166-2 subdivision code ("US-DC", "DE-BE", "IN-DL"), as well as a field called
userId, which acts as our well-distributed identifier. This ensures that location affinity is baked into each document.
In the background, MongoDB Atlas will have automatically placed each of these documents in their respective zones. The document corresponding to Anna Bell will live in North Virginia and the document corresponding to John Doe will live in Singapore. Assuming we have application instances deployed in Singapore and North Virginia, both will use the same MongoDB connection string to connect to the cluster. When Anna Bell connects to our application from the US, she will automatically be working with data kept in close proximity to her. Similarly, when John Doe connects from Australia, he will be writing to the Singapore region.
Adding a zone to your Global Cluster
Now let’s say that you start to see massive adoption of your application in India and you want to improve the performance for local users. At anytime, you can return to your cluster configuration, click “Add a Zone”, and select Mumbai as the preferred cloud region for the new zone.
The global latency map will update, showing us the new zone and an updated view of the countries that map to it. When we deploy the changes, the documents that are tagged with relevant ISO country codes will gracefully be transferred across to the new zone, without downtime.
Scaling write throughput in a single zone
As we mentioned earlier in this post, it’s possible to scale out a single zone to address increases in local write throughput. Simply scroll to the “Zone Configuration”, click on “Additional Options” and increase the number of shards. By adding a second shard to a zone, you are able to double your write throughput.
Low-latency reads of data originating from other zones
We also referenced the ability to distribute read-only replicas of data from a zone into the preferred cloud regions of other zones, providing users with low-latency read access to data originating from other regions. This is easy to configure in MongoDB Atlas. In “Zone Configuration”, select “Add secondary and read-only regions”. Under “Deploy read-only replicas”, select “Add a node” and choose the region where you’d like your read-only replica to live.
For global clusters, Atlas provides a shortcut to creating read-only replicas of each zone in every other zone. Under “Zone configuration summary”, simply select the “Configure local reads in every zone” button.
MongoDB Atlas Global Clusters are very powerful, making it possible for practically any developer or organization to easily deploy, manage, and scale a distributed database layer optimized for low-latency reads and writes anywhere in the world. We're very excited to see what you build with this new functionality.
Global clusters are available today on Amazon Web Services, Google Cloud Platform, and Microsoft Azure for clusters M30 and larger.
Introducing Free Cloud Monitoring for MongoDB
With the release of MongoDB 4.0, we’re excited to announce the availability of free cloud monitoring, the easiest way to monitor and visualize the status of your MongoDB deployments.
Let’s walk through how it works.
After you’ve installed MongoDB 4.0, connect to your instance(s) using the
MongoDB shell version v4.0.0
connecting to: mongodb://127.0.0.1:27017
MongoDB server version: 4.0.0
Enable MongoDB's free cloud-based monitoring service to collect and display
metrics about your deployment (disk utilization, CPU, operation statistics,
The monitoring data will be available on a MongoDB website with a unique
URL created for you. Anyone you share the URL with will also be able to
view this page. MongoDB may use this information to make product
improvements and to suggest MongoDB products and deployment options to you.
To enable free monitoring, run the following command:
When you run the command, you should see something similar to what’s shown below.
"state" : "enabled",
"message" : "To see your monitoring data, navigate to the unique URL below. Anyone you share the URL with will also be able to view this page. You can disable monitoring at any time by running db.disableFreeMonitoring().",
"url" : "https://cloud.mongodb.com/freemonitoring/cluster/22E5ZH35UZ77JY3UHS3VYYTI7BKBIHWF",
"userReminder" : "",
"ok" : 1
Simply copy and paste your unique URL into a browser to access your monitoring dashboard. Free cloud monitoring tracks key performance indicators such as operation execution times, disk utilization, memory, network input/out, and more in interactive charts.
Mousing over chart lines reveal precise metrics.
You can also zoom in to 1 minute granularity.
Free cloud monitoring supports standalone instances and replica sets of MongoDB 4.0+. Of course, only monitoring metadata is accessed, never the contents of your databases. You can disable monitoring and your unique URL at any time by running the
For more information, visit our documentation.
New to MongoDB Atlas — Free Fully Managed Databases on Google Cloud Platform
Today we’re excited to announce that the MongoDB Atlas free tier — which provides access to a fully managed M0 cluster with 512 MB of storage at no cost — is now available on Google Cloud Platform (GCP).
We launched the MongoDB Atlas database as a service on GCP one year ago at MongoDB World 2017. Since then, we’ve made significant product enhancements, culminating in the most powerful MongoDB service for developers building their applications on Google’s expanding ecosystem of cloud services. For example, we launched Atlas into 13 Google cloud regions earlier this year, and added the ability for customers to replicate their data to any number of regions for fast, responsive read access and multi-region fault tolerance.
Companies like Longbow Advantage, a supply chain partner to Del Monte Foods and Subaru of America, are using MongoDB Atlas on GCP to accelerate innovation and stay agile and efficient in their development life cycles.
We wanted to ensure that our team could remain focused on the application and not have to worry about the underlying infrastructure. Atlas allowed us to do just that.Alex Wakefield, Chief Commercial Officer, Longbow Advantage
The availability of the Atlas free tier on GCP will make it easier than ever for developers using the cloud platform to experiment in an optimized environment for MongoDB, with no barrier to entry. The M0 cluster is ideal for learning MongoDB, prototyping, or early development, and has built-in security, availability, and fully managed upgrades.
The Atlas free tier on GCP is available in 3 regions:
- Iowa (us-central1)
- Belgium (europe-west1)
- Singapore (asia-southeast1)
MongoDB Atlas is available in 13 GCP regions
Getting started is simple. When building a new cluster in MongoDB Atlas, select GCP as the cloud provider and then select the region closest to your application server(s) with the "Free tier available" label.
Then, in the Cluster Tier tab, select the M0 cluster size.
Finally, name your cluster.
That's it. We're excited to see what you build with MongoDB Atlas and GCP!
New to MongoDB Atlas — Fully Managed Connector for Business Intelligence
Driven by emerging requirements for self-service analytics, faster discovery, predictions based on real-time operational data, and the need to integrate rich and streaming data sets, business intelligence (BI) and analytics platforms are one of the fastest growing software markets.
Today, it’s easier than ever for MongoDB Atlas customers to make use of the MongoDB Connector for BI. The new BI Connector for Atlas is a fully managed, turnkey service that allows you to use your automated cloud databases as data sources for popular SQL-based BI platforms, giving you faster time to insight on rich, multi-structured data.
The BI Connector for Atlas removes the need for additional BI middleware and custom ETL jobs, and relies on the underlying Atlas platform to automate potentially time-consuming administration tasks such as setup, authentication, maintaining availability, and ongoing management.
Customers can use the BI Connector for Atlas along with the recently released MongoDB ODBC Driver to provide a SQL interface to fully managed MongoDB databases. This allows data scientists and business analysts responsible for analytics and business reporting on MongoDB data to easily connect to and use popular visualization and dashboarding tools such as Excel, Tableau, MicroStrategy, Microsoft Power BI, and Qlik.
When deploying the BI Connector, Atlas designates a secondary in your managed cluster as the data source for analysis, minimizing the likelihood an analytical workload could impact performance on your operational data store. The BI Connector for Atlas also utilizes MongoDB’s aggregation pipeline to push more work to the database and reduce the amount of data that needs to be moved and computed in the BI layer, helping deliver insights faster.
The BI Connector for Atlas is currently available for M10 Atlas clusters and higher.
New to MongoDB Atlas — Full CRUD Support in Data Explorer
As a fully managed database service, MongoDB Atlas makes life simpler for anyone interacting with MongoDB, whether you’re deploying a cluster on demand, restoring a snapshot, evaluating real-time performance metrics, or inspecting data.
Today, we’re taking it one step further by allowing developers to manipulate their data right from within the Atlas UI. The embedded Data Explorer, which has historically allowed you to run queries, view metadata regarding your deployments, and retrieve information such as index usage statistics, now supports full CRUD functionality.
To support these capabilities, new Project-level roles with different permission levels have been added.
You can assign users these new roles in the Users and Teams settings.
In addition, all Data Explorer operations are tracked and presented in the Atlas Activity Feed (found in the Alerts menu for each Project), allowing you to see who did what, and when.
When you click into the Data Explorer in Atlas, you should see new controls for interacting with your documents, collections, databases, and indexes. For example, modify existing documents using the intuitive visual editor, or insert new documents and clone or delete existing ones in just a few clicks. A comprehensive list of available Data Explorer operations can be found in the Atlas documentation.
The Data Explorer is currently available for M10 Atlas clusters and higher.
New to MongoDB Atlas on AWS — AWS Cloud Provider Snapshots, Free Tier Now Available in Singapore & Mumbai
AWS Cloud Provider Snapshots
MongoDB Atlas is an automated cloud database service designed for agile teams who’d rather spend their time building apps than managing databases, backups, and restores. Today, we’re happy to announce that Cloud Provider Snapshots are now available for MongoDB Atlas replica sets on AWS. As the name suggests, Cloud Provider Snapshots provide fully managed backup storage and recovery using the native snapshot capabilities of the underlying cloud service provider.
Choosing a backup method for a database cluster in MongoDB Atlas
When this feature is enabled, MongoDB Atlas will perform snapshots against the primary in the replica set; snapshots are stored in the same cloud region as the primary, granting you control over where all your data lives. Please visit our documentation for more information on snapshot behavior.
Cloud Provider Snapshots on AWS have built-in incremental backup functionality, meaning that a new snapshot only saves the data that has changed since the previous one. This minimizes the time it takes to create a snapshot and lowers costs by reducing the amount of duplicate data. For example, a cluster with 10 GB of data on disk and 3 snapshots may require less than 30 GB of total snapshot storage, depending on how much of the data changed between snapshots.
Cloud Provider Snapshots are available for M10 clusters or higher in all of the 15 AWS regions where you can deploy MongoDB Atlas clusters.
Free, $9, and $25 MongoDB Atlas clusters now available in Singapore & Mumbai
We’re committed to lowering the barrier to entry to MongoDB Atlas and allowing developers to build without worrying about database deployment or management. Last week, we released a 14% price reduction on all MongoDB Atlas clusters deployed in AWS Mumbai. And today, we’re excited to announce the availability of free and affordable database cluster sizes in South and Southeast Asia on AWS .
Free M0 Atlas clusters, which provide 512 MB of storage for experimentation and early development, can now be deployed in AWS Singapore and AWS Mumbai. If more space is required, M2 and M5 Atlas clusters, which provide 2 GB and 5 GB of storage, respectively, are now also available in these regions for just $9 and $25 per month.
MongoDB Atlas Price Reduction - AWS Mumbai
Developers use MongoDB Atlas, the fully automated cloud service for MongoDB, to quickly and securely create database clusters that scale effortlessly to meet the needs of a new generation of applications.
We recognize that the developer community in India is an incredibly vibrant one, one that is growing rapidly thanks to startups like Darwinbox. The team there built a full suite of HR services online, going from a standing start to a top-four sector brand in the Indian market in just two years.
As part of our ongoing commitment to support the local developer community and lower the barrier to entry to using a MongoDB service that removes the need for time-consuming administration tasks, we are excited to announce a price reduction for MongoDB Atlas. Prices are being reduced by up to 14% on all MongoDB Atlas clusters deployed in AWS Mumbai. With this, we aim to give more developers access to the best way to work with data, automated with built-in best practices.
MongoDB Atlas is available in India on AWS Mumbai and GCP Mumbai. It is also available on Microsoft Azure in Pune, Mumbai and Chennai. Never tried MongoDB Atlas? Click here to learn more.
DarwinBox Evolves HR SaaS Platform and Prepares for 10x Growth with MongoDB Atlas
Evolution favors those that find ways to thrive in changing environments. DarwinBox has done just that, providing a full spectrum of HR services online and going from a standing start to a top-four sector brand in the Indian market in just two years. From 40 enterprise clients in its first year to more than 80 in its second, it now supports over 200,000 employees, and is hungrily eyeing expansion in new territories.
“We’re expecting 10x growth in the next two years,” says Peddi. “That means aggressive scaling for our platform and MongoDB Atlas will play a big role."
Starting from a blank sheet of paper
The company’s key business insight is that employees have grown accustomed to the user experience of online services they access in their personal lives. However, the same ease of use is simply not found at work, especially in HR solutions that address holiday booking, managing benefits, and appraisals. DarwinBox’s approach is to deliver a unified platform of user-friendly HR services to replace a jumble of disparate offerings, and to do so in a way that supports its own aggressive growth plans. The company aims to support nearly every employee interaction with corporate HR, such as recruitment, employee engagement, expense management, separation, and more.
“We started in 2015 from a blank sheet of paper,” Peddi says. “It became very clear very quickly that for most of our use cases, only a non-relational database would work. Not only did we want to provide an exceptionally broad set of integrated services, but we also had clients with a large number of customization requirements. This meant we needed a very flexible data model. We looked at a lot of options. We wanted an open source technology to avoid lock-in and our developers pushed for MongoDB, which fit all our requirements and was a pleasure to work with. Our databases are now 90 percent MongoDB. We expect that to be at 100 percent soon.”
Reducing costs and future-proofing database management
When DarwinBox launched, it ran its databases in-house, which wasn’t ideal. “We have a team of 40+ developers, QA and testers, and three running infrastructure, and suddenly we’re growing much faster than we expected. It’s a good problem to have, but we couldn’t afford to offer anything less than excellent service.” Peddi emphaszied that of all the things they wanted to do to succeed, becoming database management experts wasn’t high on the list.
This wasn’t the only reason that MongoDB Atlas looked like the next logical step for the company when it became available, says Peddi, “We were rapidly developing our services and our customer base, but our strategies for backing up the databases, for scaling, for high availability, and for monitoring performance weren’t keeping up. In the end, we decided that we’d migrate to Atlas for a few major reasons.”
The first reason was the most obvious. “The costs of managing the databases, infrastructure, and backups were increasing. In addition, it became increasingly difficult to self-manage everything as requirements became more sophisticated and change requests became more frequent. Scaling up and down to match demand and launching new clusters consumed precious man hours. Monitoring performance and issue resolution was taking up more time than we wanted. We had built custom scripts, but they weren’t really up to the task.”
With MongoDB Atlas on AWS, Peddi says, all these issues are greatly reduced. “We’re able to do everything we need with our fully managed database very quickly – scale according to business need at the press of a button, for example. There are other benefits. With MongoDB technical engineers a phone call away, we’re able to fix issues far quicker than we could in the past. MongoDB Compass, the GUI for the database, is proving helpful in letting our teams visually explore our data and tune things accordingly.”
Migrating to Atlas has also helped Darwinbox dramatically reduce costs.
We’ve optimized our database infrastructure and how we manage backups. Not only did we bring down costs by 40%, but by leveraging the queryable snapshot feature, we’re able to restore the data we actually need 80% faster.Chaitanya Peddi, Co-founder and Head of Product, DarwinBox
The increased availability and data resilience from the switch to MongoDB Atlas on AWS eases the responsibility in managing the details of 200,000 employees’ working lives. “Data is the most sensitive part of our business, the number one thing that we care about,” says Peddi, “We can’t lose even 0.00001 percent of our data. We used to take snapshots of the database, but that was costly and difficult to manage. Now, it’s more a live copy process. We can guarantee data retention for over a year, and it only takes a few moments to find what you need with MongoDB Atlas.”
For DarwinBox to achieve its target of 10x growth in two years, it has to – and plans to – go international.
“We had that in mind from the outset. We’ve designed our architecture to cope with a much larger scale, both in total employee numbers and client numbers, and to handle different regulatory regimes.” According to Peddi, that means moving to microservices, developing data analytics, maybe even looking at other cloud providers to host the DarwinBox HR Platform. He added: “If we were to do this on AWS and self-manage the database with our current resources, we would have to invest a significant amount of effort into orchestrating and maintaining a globally distributed database. MongoDB Atlas with its cross-region capabilities makes this all much easier.”
Darwinbox is confident that MongoDB Atlas will help the organization achieve its product plans.
“MongoDB Atlas will be able to support the business needs that we've planned out for the next two years.” says Peddi, “We’re happy to see how rapidly the Atlas product roadmap is evolving.”
Bienvenue à MongoDB Atlas: MongoDB as a Service Now Available in France
MongoDB Atlas, the fully automated cloud database, is now available in France on Amazon Web Services and Microsoft Azure. Located in the Paris area, these newly supported cloud regions will allow organizations using MongoDB Atlas to better serve their customers in and around France. For deployments in AWS EU (Paris), the following instance sizes are supported. MongoDB Atlas deployments in this cloud region will automatically be distributed across three AWS availability zones (AZ), ensuring that the failure of a single AZ will not impact the database’s automated election and failover process. Currently, customers deploying to AWS EU (Paris) can also replicate their data to regions of their choosing (to provide even greater fault tolerance or fast, responsive read access) if they’re using the M80 (low CPU), M200 (low CPU), or M400 (low CPU) instance sizes.
For MongoDB Atlas deployments in Azure France Central, the following instance sizes are supported. Deployments in this cloud region will automatically be distributed across 2 Azure fault domains. Assuming that a customer is deploying a 3-node replica set, 2 of those nodes will be located in 1 fault domain and the last node will live in its own fault domain. While this configuration does have a higher chance of loss of availability in the event that a fault domain goes down, cross-region replication can be configured to withstand fault domain and regional outages and is compatible with any Atlas instance size available in Azure France Central.
MongoDB is certified under the EU-US Privacy Shield, and the MongoDB Cloud Terms of Service now includes GDPR-required data processing terms to help MongoDB Atlas customers prepare for May 25, 2018 when the GDPR becomes enforceable.
MongoDB Atlas, la base de donnée entièrement automatisée dans le cloud, est maintenant disponible en France sur Amazon Web Services et Microsoft Azure. Localisés dans la région Parisienne, ces data centers nouvellement supportés permettront à votre organisation d’utiliser MongoDB Atlas pour répondre au mieux aux besoins de vos clients en France et ses environs. Pour les déploiements Atlas sur AWS EU (Paris), les tailles d’instances suivantes sont supportées. Les déploiements sur Atlas dans cette région du cloud seront automatiquement distribués au travers de trois zones de disponibilités pour assurer qu’une panne dans l’une de ces zones n’impacte pas le système d’élection automatique et le processus de basculement vers un nouveau noeud. Actuellement, les clients d’Atlas qui déploient sur AWS EU (Paris) peuvent aussi répliquer leurs données dans les autres régions de leur choix (pour permettre une encore plus grande résistance à la panne ou pour des accès en lecture plus réactifs et plus rapides) s'ils utilisent les tailles d’instances M80 (CPU faible), M200 (CPU faible), ou M400 (CPU faible).
Pour les déploiements dans “Azure France Central”, les tailles d’instances suivantes sont supportées. Les déploiements Atlas dans cette région du cloud seront automatiquement distribuée dans deux data centers Azure. En supposant qu’un client déploie un replica set de trois noeuds, deux de ces noeuds seront localisés dans un data center et le dernier sera situé dans son propre data center. Bien que cette configuration possède plus de chance de perte de disponibilité dans le cas d’une panne sur un datacenter entier, la réplication au travers de plusieurs régions peut être configurée pour résister à une panne générale d’un datacenter ou à des coupures régionales. Cette réplication inter-régionale est compatible avec n’importe quelle taille d’instance disponible sur Azure France Central.
MongoDB est certifié dans le cadre du Privacy Shield EU-US, et les conditions d'utilisation de MongoDB Cloud incluent désormais les termes de traitement de données requis par GDPR pour aider les clients de MongoDB Atlas à se préparer pour le 25 mai 2018.