MongoDB and AXA France: Creating a Connected Homes Project

Mat Keep
August 22, 2016 | Updated: December 11, 2017
#Customer Stories#Business

The Internet of Things (IoT) is a transformative technology, touching all of our lives – in the workplace, in the gym, on the road, and in so many other places and contexts. But it’s in the home where the IoT is making some of its largest strides.

The concept of the smart, connected home is revolutionizing how we protect our families and most valuable personal assets – whether it’s from intruders, from fires and floods, through to environmental controls. Insurance companies have been quick to identify the opportunity smart homes present to them, and to their customers. By providing an “IoT Hub”, insurers can aggregate sensor data from multiple devices in the home to allow their customers complete control to defend, protect and monitor their families and property from a single user interface.

At this year’s MongoDB World customer conference, AXA France developers Guillaume Chervet and Vincent Gillot presented their connected homes project, built on MongoDB, node.js and the cloud. In their presentation, they discuss: - How they optimized schema design through multiple iterations to most efficiently represent customers, sensor data, and notifications - How they consume, process and alert against sensor events emitted within the same millisecond from multiple devices in a single connected home - How they use the MongoDB aggregation pipeline to analyse and report against sensor data. - How the move to MongoDB 3.2 has allowed them to simplify their application code. By taking advantage of new features such as document validation they are able to enforce structure over their IoT events schema; and the $lookup aggregation operator allows the team to JOIN data from multiple collections for richer analytics

Guillaume and Vincent shared how the development team had come from SQL Server and Oracle database backgrounds before working with MongoDB on this project. Selected quotes from the team included:

MongoDB is really easy to install, to configure. Its immediate ease of use impressed me.

Developing new features in agile with sprint modes is easier with a NoSQL database than with a relational database.

Querying system is rich, manipulating data in JSON becomes intuitive, and my algorithms are simpler!

View the slides to learn more about AXA’s connected home project with MongoDB

Download our IoT & Big Data whitepaper, co-authored with Bosch Software Innovations to learn more about the challenges presented by managing IoT data.

Download the white paper


*About the Author - Mat Keep

Mat is a director within the MongoDB product marketing team, responsible for building the vision, positioning and content for MongoDB’s products and services, including the analysis of market trends and customer requirements. Prior to MongoDB, Mat was director of product management at Oracle Corp. with responsibility for the MySQL database in web, telecoms, cloud and big data workloads. This followed a series of sales, business development and analyst / programmer positions with both technology vendors and end-user companies.*

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MongoDB and Stratio: Building an Operational Data Lake for One of Spain’s Largest Insurance Companies

MongoDB, Apache Spark, Zeppelin, Hadoop and Kafka Improve Customer Experience and Optimize Marketing ROI Data lakes are playing an increasingly critical role in modern enterprise data architectures. Providing a centralized repository to aggregate data from multiple internal and external sources, the data lake – often built on Apache Hadoop – provides a foundation for sophisticated analytics that can improve customer insights, deliver higher operational efficiency and reduce costs. Beyond just storing data, a key requirement of the data lake is being able to serve analytic models to real-time, operational applications. And that requires more than just Hadoop. Powerful operational databases are needed to make analytics models accessible and actionable within operational applications. Exposing these models to online applications makes our business processes smarter and more contextually aware – for example, presenting personalized recommendations to users, detecting and preventing fraudulent transactions while they are in flight, or predicting imminent failures in critical systems. Mutua Madrileña, one of the largest insurance companies in Spain, recognized the importance of creating a data lake in its goal to improve customer experience and optimize marketing spend. They worked with the Pure Spark Platform from Stratio to implement an operational data lake, bringing together: Apache Kafka and Apache Flume for data ingestion Apache Hadoop for storing raw data Apache Spark for analytical processing and machine learning, orchestrated by Apache Mesos MongoDB and Postgres for serving analytics models to operational applications and reporting tools R Studio and Apache Zeppelin for business intelligence and analytics *Figure 1: Stratio Pure Spark Platform: creating an operationalized data lake* Alvaro Santos, Big Data Architect at Stratio, presented the data lake built for Mutua Madrileña at this year’s MongoDB World conference. In his session, Alvaro discussed how the data lake is ingesting data from over 25 different sources to power a range of applications, including: The creation of machine learning models to personalize user experience across web and mobile channels, present product recommendations, and classify insurance applicants by risk Mapping of the customer journey through Mutua’s systems to understand user context and identify gaps in business processes Collection and analysis of marketing campaign data to measure impact and improve performance. Alvaro discussed the selection of technology for the data lake. Apache Spark was chosen because of its speed as a distributed processing engine, access to rich machine learning libraries, and developer-friendly APIs. MongoDB was chosen: To take advantage of its flexible data model, allowing rapidly changing, semi-structured data to be stored and processed with ease Rich secondary indexes that push query filtering down to the database. As a result, operational applications can execute complex queries with low latency Automatic sharding to support a doubling of data volumes in the data lake, and native replication for always-on availability View the slides to learn more about Mutua’s data lake journey: Learn more about unlocking operational intelligence from the data lake with our new whitepaper: Download the white paper

August 19, 2016
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Congratulations to the 2023 EMEA MongoDB Innovation Award Winners

Throughout 2023, MongoDB has recognized the exceptional successes our APAC and North America customers realize when they build with MongoDB Atlas. Today, we are pleased to announce the recipients of the 2023 EMEA MongoDB Innovation Awards. These customers join a premiere group of organizations that have been recognized for boldly exploring the power of data and harnessing it to build the applications of tomorrow. The professionals behind these innovative projects span industries, use cases, and geographies. However, what remains constant is each organization's unfailing commitment to delivering deeper insights, powerful features, and capabilities, as well as seamless experiences, to their customers. We congratulate each of the EMEA MongoDB Innovation Award winners for delivering big results and invite you to find inspiration in their work below. Building the Next Big Thing Since being originally founded in Finland as a food delivery service, Wolt expanded their offering into groceries, gifts, and other items, and makes it possible for people living in hundreds of cities across 25 countries to get whatever they need, delivered quickly and reliably to their front door. Due to the rapidly increasing scale in their business and new partnerships with enterprise retailers, Wolt needed to rethink their existing tooling that is used by their tens of thousands of merchant partners to manage their offering inside Wolt’s delivery platform. To provide their merchants with multi-venue management capabilities, increased automation, and no limitations on amounts of menu items, Wolt chose fully-managed MongoDB Atlas due to its flexible data document model, support for Kafka and event-driven architectures, high performance, and limitless scalability. Industry Transformation Gong ’s Revenue Intelligence Platform uses proprietary AI technology to enable revenue teams to utilize customer interactions to increase business efficiency, improve decision-making, and accelerate revenue growth. Gong chose MongoDB Atlas as a high-performing transactional database due to its ability to handle Gong’s complex data structures and algorithms, and indeed MongoDB Atlas was able to support 30-40 million daily interactions and mission-critical search queries. Through the adoption of MongoDB Atlas, Gong’s queries take only 20-30 milliseconds, and with Atlas’ sharding capabilities, Gong future-proofed its environment for ongoing growth and gained greater control over its customer data. Innovative Leadership in Diversity, Equity, and Inclusion As part of its "Inclusion for All" strategy, Vodafone is committed to ensuring our digital society is accessible to all, and that women feel empowered to participate in it. In addition, Vodafone’s mission is to have 40% of women in management roles by 2030. Earlier this year, Vodafone UK teamed up with MongoDB to host interactive sessions and networking for “Digital & IT Women in Tech Week.” With a shared goal of reducing the gender gap in technology and inspiring young women to build their careers by pursuing leadership roles, the week’s events brought together more than 200 Vodafone employees to discuss breaking workplace biases and enhancing gender equality in the industry. In addition, Vodafone spoke at the 2023 MongoDB.local London event in the IDEA Lounge. Inspiring Innovation Ernst & Young LLP (EY) manages high volumes of transactional data and its clients and internal teams work under strict timelines to file taxes and meet regulatory deadlines. Their cloud-based Global VAT Reporting Tool (GVRT) platform automates and digitizes the preparation of 242 different types of returns across 79 countries. With support from MongoDB Professional Services, EY planned and executed a data migration from their previous database solution to MongoDB Atlas so that the GVRT tool could fully benefit from MongoDB’s developer data platform. Since migrating to Atlas, EY experienced a significant performance boost, they reduced costs by as much as 50%, and they are able to scale without limitations to handle increased data volumes, transactional loads, and concurrent user requests during peak periods. Find out Who Uses MongoDB and read more about our customers’ MongoDB Atlas successes.

September 27, 2023