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

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.

Come and hear more about our new full-text search operator for MongoDB Atlas. This is a significant enhancement to MongoDB search features and is the easiest and most powerful full-text search solution for databases on MongoDB Atlas. This talk is important for anyone who has implemented search or is considering a search feature in their MongoDB application. You will see a demo of $searchBeta, learn about how it works, discover specific features to help you deliver relevant search results, and learn how you can start using full-text search in your application today.

MongoDB has been conceived for the cloud age. Making sure that MongoDB is compatible and performant around cloud providers is mandatory to achieve complete integration with platforms and systems. Azure is one of biggest IaaS platforms available and very popular amongst developers that work on Microsoft Stack.

This presentation discusses migrating data from other data stores to MongoDB Atlas. It begins by explaining why MongoDB and Atlas are good choices for data management. Several preparation steps are covered, including sizing the target Atlas cluster, increasing the source oplog, and testing connectivity. Live migration, mongomirror, and dump/restore options are presented for migrating between replicasets or sharded clusters. Post-migration steps like monitoring and backups are also discussed. Finally, migrating from other data stores like AWS DocumentDB, Azure CosmosDB, DynamoDB, and relational databases are briefly covered.

These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.

Venez en apprendre davantage sur notre nouvel opérateur de recherche en texte intégral pour MongoDB Atlas. Il s'agit d'une amélioration significative des fonctionnalités de recherches de MongoDB et c'est également la solution de recherche en texte intégral la plus simple et la plus puissante pour les bases de données MongoDB Atlas. Cette présentation est importante pour quiconque a mis en place ou en visage de mettre en place une fonctionnalité de recherche dans son application MongoDB. Vous assisterez à une démo de $searchBeta, apprendrez comment cela fonctionne, découvrirez des fonctionnalités spécifiques vous permettant d'obtenir des résultats de recherche pertinents et apprendrez comment vous pouvez commencer à utiliser la recherche en texte intégral dans votre application dès aujourd'hui.

This document provides a high-level summary of MongoDB and its features. It begins with an overview of MongoDB, including its employees, customers, offices, and public status. It then discusses MongoDB's document model and how it allows for flexible, schema-less structures. It also covers MongoDB's rich query language and secondary indexing capabilities. Other sections summarize MongoDB's availability and workload isolation with replica sets, its scalability features including sharding and data locality, its security features, and management tools like Ops Manager and Compass. The document also briefly discusses MongoDB's integration with BI tools and running MongoDB in the cloud with MongoDB Atlas.

The document provides an overview of a presentation on schema design patterns for MongoDB databases. It introduces several common patterns including Attribute, Subset, Computed, Approximation, and Schema Versioning. For each pattern, it describes the problem it addresses, example use cases, and the general solution or approach. It also includes examples of how the patterns could address issues like large documents, working set size, CPU usage, write volume, and changing schemas. The presentation aims to provide a common methodology and vocabulary for designing MongoDB schemas.

You have made a successful Proof of Concept by using Pandas for data manipulation and analysis. So, how are you going to productionize it? Come to learn how to transform your POC to a scalable product with MongoDB. Learn about pitfalls and drawbacks of Pandas and benefits of using MongoDB in the early stages.

aux Core Data, appréciée par des centaines de milliers de développeurs. Apprenez ce qui rend Realm spécial et comment il peut être utilisé pour créer de meilleures applications plus rapidement.

Blazing Fast Analytics with MongoDB & Spark Muthu Chinnasamy, Senior Solutions Architect, MongoDB MongoDB Evenings Pasadena CrossCampus Pasadena September 13, 2016

Learn how MongoDB Atlas has enabled Ticketek to grow rapidly across geographical boundaries and seamlessly support the adoption of new business initiatives. Tane Oakes, TEG Enterprise Architect, will do a deep dive on how MongoDB Atlas supports Ticketek's strategic multi-cloud initiative and how Ticketek uses MongoDB Stitch to establish a scalable and common API used by customers and partners. Tane will also explain how using MongoDB Atlas and MongoDB Stitch has helped reduce technical debt.

Chaque entreprise devient une entreprise de logiciels, fournissant des solutions client pour accéder à une variété de services et d'informations. Les entreprises commencent maintenant à valoriser leurs données et à obtenir de meilleures informations pour l'entreprise. Un défi crucial consiste à s'assurer que ces données sont toujours disponibles et sécurisées pour être conformes aux objectifs commerciaux de l'entreprise et aux contraintes réglementaires des pays. MongoDB fournit la couche de sécurité dont vous avez besoin, venez découvrir comment sécuriser vos données avec MongoDB.

Presented by: Saurabh Kashikar Abstract: MongoDB’s basic unit of storage is a document. Documents can represent rich, schema-free data structures, meaning that we have several viable alternatives to the normalized, relational model. If you are new to MongoDB, learn the schema design basics in this introductory session. This session will help you model basic relationships in MongoDB. What You Will Learn: - The fundamentals of the MongoDB document model. - How to model 1-1 and one-to-many (1-N) relationships in MongoDB. - How to model many-to-many (N-N) relationships in MongoDB.

Modern architectures are moving away from a "one size fits all" approach. We are well aware that we need to use the best tools for the job. Given the large selection of options available today, chances are that you will end up managing data in MongoDB for your operational workload and with Spark for your high speed data processing needs. Description: When we model documents or data structures there are some key aspects that need to be examined not only for functional and architectural purposes but also to take into consideration the distribution of data nodes, streaming capabilities, aggregation and queryability options and how we can integrate the different data processing software, like Spark, that can benefit from subtle but substantial model changes. A clear example is when embedding or referencing documents and their implications on high speed processing. Over the course of this talk we will detail the benefits of a good document model for the operational workload. As well as what type of transformations we should incorporate in our document model to adjust for the high speed processing capabilities of Spark. We will look into the different options that we have to connect these two different systems, how to model according to different workloads, what kind of operators we need to be aware of for top performance and what kind of design and architectures we should put in place to make sure that all of these systems work well together. Over the course of the talk we will showcase different libraries that enable the integration between spark and MongoDB, such as MongoDB Hadoop Connector, Stratio Connector and MongoDB Spark Native Connector. By the end of the talk I expect the attendees to have an understanding of: How they connect their MongoDB clusters with Spark Which use cases show a net benefit for connecting these two systems What kind of architecture design should be considered for making the most of Spark + MongoDB How documents can be modeled for better performance and operational process, while processing these data sets stored in MongoDB. The talk is suitable for: Developers that want to understand how to leverage Spark Architects that want to integrate their existing MongoDB cluster and have real time high speed processing needs Data scientists that know about Spark, are playing with Spark and want to integrate with MongoDB for their persistency layer

Join this talk and test session with MongoDB Support where you'll go over the configuration and deployment of an Atlas environment. Setup a service that you can take back in a production-ready state and prepare to unleash your inner genius.

Rapid Development and Performance By Transitioning from RDBMSs to MongoDB Modern day application requirements demand rich & dynamic data structures, fast response times, easy scaling, and low TCO to match the rapidly changing customer & business requirements plus the powerful programming languages used in today's software landscape. Traditional approaches to solutions development with RDBMSs increasingly expose the gap between the modern development languages and the relational data model, and between scaling up vs. scaling horizontally on commodity hardware. Development time is wasted as the bulk of the work has shifted from adding business features to struggling with the RDBMSs. MongoDB, the premier NoSQL database, offers a flexible and scalable solution to focus on quickly adding business value again. In this session, we will provide: - Overview of MongoDB's capabilities - Code-level exploration of the MongoDB programming model and APIs and how they transform the way developers interact with a database - Update of the exciting features in MongoDB 3.0

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.

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 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 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 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 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.

IBM's Cloud-based Data Lake for Analytics and AI presentation covered: 1) IBM's cloud data lake provides serverless architecture, low barriers to entry, and pay-as-you-go pricing for analytics on data stored in cloud object storage. 2) The data lake offers SQL-based data exploration, transformation, and analytics capabilities as well as industry-leading optimizations for time series and geospatial data. 3) Security features include customer-controlled encryption keys and options to hide SQL queries and keys from IBM.