Resources

How to Integrate DevOps Practices with MongoDB

This white paper showcases how Atlas can be leveraged within a DevOps environment to accelerate ops and automate workflows.

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TPC-C Benchmarking

Abstract: Presented at the 2019 VLDB Conference, the paper discusses how we adapted TPC-C for a document database, and performance results we measured

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MongoDB Architecture Guide

Get an overview of MongoDB’s data platform architecture. You will learn more about its document model, distributed systems architecture, and on-demand deployment options.

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MongoDB Atlas Best Practices

MongoDB Atlas is a database as a service from MongoDB, providing all of the features of the database, without the operational heavy lifting required for any new application. This guide describes the best practices to help you get the most out of the MongoDB Atlas service, including: schema design, capacity planning, security, and performance optimization.

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Quantifying Business Advantage: The Value of Database Selection

Can the choice of database really dictate business outcomes? Consider: A leading insurance company delivering a new application in just 3 months, after struggling for 8 years with a legacy database. A global telecoms operators accelerating time to market by 4x and improving customer experience by 10x. A Tier 1 investment bank estimating savings of $40m. What do they all have in common? They all selected MongoDB to deliver applications never previously possible. Download the white paper to learn more

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MongoDB Operations Best Practices

This document provides guidance on best practices for deploying and managing MongoDB clusters. It assumes familiarity with the architecture of MongoDB and a basic understanding of concepts related to enterprise software deployment. Because MongoDB is designed to be simple to administer and to deploy, most operations professionals find they can become competent with MongoDB in a short period of time and with minimal training.

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Apache Spark and MongoDB – Turning Analytics into Real-Time Action

Apache Spark is one of the fastest growing big data projects in the history of the Apache Software Foundation. With its memory-oriented architecture, flexible processing libraries and ease-of-use, Spark has emerged as a leading distributed computing framework for real-time analytics.

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Unlocking Operational Intelligence from the Data Lake

Hadoop-based data lakes are enabling organizations to efficiently capture unprecedented volumes of data generated by new classes of highly connected applications. But without being able to expose that data to operational applications, users are struggling to maximize returns on their Hadoop investments. Read this white paper to learn how to create an operational data lake.

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Top 5 Considerations When Evaluating NoSQL Databases

Relational databases have a long-standing position in most organizations, and for good reason. But companies are increasingly considering alternatives to legacy relational infrastructure, such as non-tabular “NoSQL” databases. As companies evaluate these products, they should consider 5 critical dimensions to make the right choice for their applications and their businesses. In this paper, we describe these dimensions and show why MongoDB is the most widely used non-tabular database in the market.

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Data Streaming with Apache Kafka & MongoDB

A new generation of technologies is needed to consume and exploit today's real time, fast moving data sources. Apache Kafka, originally developed at LinkedIn, has emerged as one of these key new technologies. This paper explores the use-cases and architecture for Kafka, and how it integrates with MongoDB to build sophisticated data-driven applications that exploit new sources of data.

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MongoDB Security Architecture

The frequency and severity of data breaches continues to escalate year on year, with researchers estimating attacks increasing nearly 50% year on year. Databases store an organization’s most important information assets, so securing them is top of mind for administrators.

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RDBMS to MongoDB Migration Guide

This guide explains how to migrate from a relational database to MongoDB and the relevant technical considerations, such as differences between the tabular and document data models, and the implications for schema design. The guide also covers indexing, queries, transactions, application integration and data migration.

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