MongoDB .local Chicago 2019: MongoDB Atlas Data Lake Technical Deep Dive

Daimler will present the Mercedes Telediagnosis use case where MongoDB was chosen as the storage document database. You will learn some basics about Telediagnosis, the requirements which drove us to MongoDB, the advantages we achieved, and our experiences on our MongoDB journey.

Speaker: Gheni Abla, Analytics Software Technical Architect, CoreLogic Level: 200 (Intermediate) Track: Data Analytics CoreLogic is a leading global property information, analytics and solutions provider. The company provides a range of analytic solutions for automated property valuation and appraisals. This presentation will cover a recent project at CoreLogic that utilized MongoDB for storing property and ownership data for over 150 million properties. MongoDB provided powerful support for storing and searching location-based property data. The MongoDB-Spark connector facilitated seamless integration between data access and the Spark-based distributed analytics processing and MongoDB’s replication capability provided high-availability across data centers. This session will cover CoreLogic’s software architecture and real-world development experiences with geospatial data and MongoDB-Spark connector. What You Will Learn: - How CoreLogic manages and stores data for over 150 million real estate properties in MongoDB, and utilizes MongoDB's geospatial data support. - How to distribute large-scale analytics process using Spark and improve data access efficiency using the MongoDB-Spark connector. - How to utilize MongoDB replication for implementing high-availability between two geographically dispersed data centers.

The document discusses the evolution of databases from relational databases to modern databases like MongoDB. Some key points discussed are: - The volume, variety and velocity of data being collected is increasing rapidly each year, creating challenges for relational databases. - Modern databases like MongoDB are designed to handle massive amounts of data from multiple sources more efficiently through improved speed, capacity and accuracy. - Case studies show that companies like MetLife, Shutterfly and Telefonica were able to build applications faster, reduce costs and improve performance by switching to MongoDB from their relational database implementations.

Find out more about our journey of migrating to MongoDB after using Oracle for our hotel search database for over ten years. - How did we solve the synchronization problem with the Master Database? - How to get fast search results (even with massive write operations)? - How other issues were solved

Whether you are running MongoDB on-premise, self-managing in the cloud, or using MongoDB Atlas, it's critical that you have dependable backups of your data for when things go sideways. This takes infrastructure, storage, and coordination, which can be complex and costly. In MongoDB 4.2, we are changing how backup is architected, helping you reduce the required storage footprint and remove architectural complexities to increase performance and decrease costs. Come to this session to see how we're accomplishing this.

MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business. This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.

MongoDB Kubernetes operator is ready for prime-time. Learn about how MongoDB can be used with most popular orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications.

This talk will provide a brief update on Microsoft’s recent history in Open Source with specific emphasis on Azure Databricks, a fast, easy and collaborative Apache Spark-based analytics service. Attendees will learn how to integrate MongoDB Atlas with Azure Databricks using the MongoDB Connector for Spark. This integration allows users to process data in MongoDB with the massive parallelism of Spark, its machine learning libraries, and streaming API.

To successfully implement our clients' unique use cases and data patterns, it is mandatory that we unlearn many relational concepts while designing and rapidly developing efficient applications in NoSQL. In this session, we will talk about some of our client use cases and the strategies we adopted using features of MongoDB.

This document discusses running MongoDB and Kubernetes together to enable lean and agile development. It proposes using Docker containers to package applications and leverage tools like Kubernetes for deployment, management and scaling. Specifically, it recommends: 1) Using Docker to containerize applications and define deployment configurations. 2) Deploying to Kubernetes where services and replication controllers ensure high availability and scalability. 3) Treating databases specially by running them as "naked pods" assigned to labeled nodes with appropriate resources. 4) Demonstrating deployment of a sample MEAN stack application on Kubernetes with MongoDB and discussing future work around experimentation and blue/green deployments.

The document provides an agenda for a MongoDB presentation, including an introduction to MongoDB's document model and how it differs from relational databases, how MongoDB brings value to clients with flexibility, performance, versatility and ease of use. It then demonstrates these qualities through MongoDB's features like rich queries, data models, and deployability anywhere. The presentation promotes MongoDB's cloud database as a service Atlas and tools like Compass. It outlines MongoDB's evolution and roadmap. It concludes by providing contact details for the presenter.

Les données de séries chronologiques sont de plus en plus au cœur des applications modernes: pensez à l'IoT, aux transactions sur actions, aux flux de clics, aux médias sociaux, etc. Avec le passage des systèmes batch aux systèmes temps réel, la capture et l'analyse efficaces des données de séries chronologiques peuvent permettre aux entreprises de mieux détecter et réagir aux événements en avance sur leurs concurrents ou d'améliorer l'efficacité opérationnelle pour réduire les coûts et les risques. Travailler avec des données de séries chronologiques est souvent différent des données d’application classiques et vous devez observer les meilleures pratiques. Cette conférence couvre: Composants communs d'une solution IoT Les défis liés à la gestion de données chronologiques dans les applications IoT Différentes conceptions de schéma et leur incidence sur l'utilisation de la mémoire et du disque sont deux facteurs déterminants dans les performances des applications. Comment interroger, analyser et présenter les données de séries chronologiques IoT à l'aide de MongoDB Compass et MongoDB Charts À la fin de la session, vous aurez une meilleure compréhension des meilleures pratiques clés en matière de gestion des données de séries chronologiques de l'IoT avec MongoDB.

Agile Software Development is becoming the defacto way of building software these days. More and more enterprises, from large fortune 500 to small shop start-ups, are adopting agile development methodologies. But Agile Software development is more than just a methodology or a practice. It's also a combined set of tools and platforms that today are at our disposal to allows to iterate faster, get-to-market sooner and also fail faster. These set of tools augment our development cycles by a few orders of magnitude and allow developers to be much more productive.

Jane Uyvova Senior Solutions Architect, MongoDB March 21, 2017 MongoDB Evenings San Francisco Learn how easy it is to set up, operate, and scale your MongoDB deployments in the cloud with MongoDB Atlas.

Presented by Claudius Li, Solutions Architect at MongoDB, at MongoDB Evenings New England 2017. MongoDB Atlas is the premier database as a service offering. Find out how MongoDB Atlas can help your team to deploy more easily, develop faster and easily manage deployment, maintenance, upgrades and expansions. We will also demonstrate some of the key features and tools that come with MongoDB Atlas.

A Free New World: Atlas Free Tier and How It Was Born Speaker: Louisa Berger, Senior Software Engineer Speaker: Vincent Do, Fullstack Engineer, MongoDB Level: 200 (Intermediate) Track: How We Build MongoDB Last year, MongoDB released Atlas – a new Database as as Service product that takes handles running, monitoring, and maintaining your MongoDB deployment in the Cloud. This winter, we added a new Free Tier option to the product, which allows users to try out Atlas with their own real data for free. Lead Automation engineer Louisa Berger and Atlas engineer Vincent Do will talk about how it works behind the scenes, and why you might want to try out Atlas. This talk is intended for developers, and will take you through the technical details of the architecture, and show you the techniques and challenges in building a multi-tenant MongoDB. What You Will Learn: - Insights on how/why you should use the Atlas free tier - How the Atlas free tier was designed and implemented - Best practices for building a multi-tenant MongoDB application

Watch this webinar to learn about our new Backend as a Service (BaaS) – MongoDB Stitch. MongoDB Stitch lets developers focus on building applications rather than on managing data manipulation code, service integration, or backend infrastructure. Whether you’re just starting up and want a fully managed backend as a service, or you’re part of an enterprise and want to expose existing MongoDB data to new applications, Stitch lets you focus on building the app users want, not on writing boilerplate backend logic. This webinar will cover the what, why, and how of MongoDB Stitch. We’ll cover everything from the features it provides to the architecture that makes it possible. By the end of the session, you should understand how Stitch can kickstart your new project or take your existing application to the next level. Attendees will learn: - The basics of MongoDB Stitch and how to use it for new projects or to expose existing data to new applications - How to control what data and services individual users can access - How to integrate your favorite services with your MongoDB application without writing extra code

MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business. This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented.

MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business. This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.

This document summarizes MongoDB Atlas Data Lake, a new service that allows customers to access, query, and analyze long-term data stored in AWS S3 buckets. It implements MongoDB's query language and security model to provide a familiar interface for working with structured data in object storage. The service is read-only, distributed, and optimized to handle queries over vast amounts of data efficiently using MongoDB's aggregation engine. Customers maintain full control over their data and how it is configured and accessed.

BigQuery Overview Typical Uses Project Hierarchy Access Control and Security Datasets and Tables Tools Demos

This document discusses MongoDB and the needs of Rivera Group, an IT services company. It notes that Rivera Group has been using MongoDB since 2012 to store large, multi-dimensional datasets with heavy read/write and audit requirements. The document outlines some of the challenges Rivera Group faces around indexing, aggregation, and flexibility in querying datasets.

Eagle6 is a product that use system artifacts to create a replica model that represents a near real-time view of system architecture. Eagle6 was built to collect system data (log files, application source code, etc.) and to link system behaviors in such a way that the user is able to quickly identify risks associated with unknown or unwanted behavioral events that may result in unknown impacts to seemingly unrelated down-stream systems. This session is designed to present the capabilities of the Eagle6 modeling product and how we are using MongoDB to support near-real-time analysis of large disparate datasets.

BM Cloudant is a NoSQL Database-as-a-Service. Discover how you can outsource the data layer of your mobile or web application to Cloudant to provide high availability, scalability and tools to take you to the next level.