Software Alternatives, Accelerators & Startups
Register | Login

AWS Greengrass VS MongoDB

Compare AWS Greengrass VS MongoDB and see what are their differences

Draxlr
Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

AWS Greengrass logo AWS Greengrass

Local compute, messaging, data caching, and synch capabilities for connected devices

MongoDB logo MongoDB

MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
  • AWS Greengrass Landing page
    Landing page //
    2023-03-28
  • MongoDB Landing page
    Landing page //
    2023-10-21

AWS Greengrass features and specs

  • Edge Computing
    AWS Greengrass allows devices to process data locally without relying on cloud resources, reducing latency and ensuring continued operation even with intermittent connectivity.
  • Seamless AWS Integration
    Seamlessly integrates with a variety of AWS services such as AWS Lambda, AWS IoT Core, and Amazon S3, allowing for enhanced functionality and simplified data exchange between edge devices and the cloud.
  • Security Features
    Offers robust security features, including data encryption for both in-transit and at-rest data, ensuring secure communication and data storage.
  • OTA Updates
    Provides over-the-air software updates, allowing developers to deploy new updates and patches to edge devices securely and efficiently.
  • Machine Learning at the Edge
    Supports ML inference capabilities, enabling machine learning models to run locally on devices, which is essential for real-time data processing and decision-making.

Possible disadvantages of AWS Greengrass

  • Complexity
    The integration of Greengrass in IoT solutions can add complexity, requiring a good understanding of both AWS services and edge computing.
  • Cost Considerations
    While processing data locally can reduce cloud costs, there may be additional expenses related to maintaining the hardware and ensuring compatibility with Greengrass, as well as costs associated with AWS usage.
  • Device Compatibility
    Not all devices may be compatible with AWS Greengrass, which may limit its use cases or require specific hardware configurations.
  • Dependency on AWS Ecosystem
    Being heavily integrated with the AWS ecosystem means that changes or outages in AWS services can potentially impact Greengrass deployments.
  • Learning Curve
    There may be a steep learning curve for developers who are new to AWS Greengrass, especially when it comes to deploying and managing complex IoT applications.

MongoDB features and specs

  • Scalability
    MongoDB offers horizontal scaling through sharding, allowing it to handle large volumes of data and enabling distributed computing.
  • Flexible Schema
    It allows for a flexible schema design using BSON (Binary JSON), making it easier to iterate and change application data models.
  • High Performance
    MongoDB is optimized for read and write throughput, making it suitable for real-time applications.
  • Rich Query Language
    Supports a rich and expressive query language that allows for efficient querying and analytics.
  • Built-in Replication
    Provides robust replication mechanisms for high availability and redundancy.
  • Geospatial Indexing
    Offers powerful geospatial indexing capabilities, useful for location-based applications.
  • Aggregation Framework
    Enables complex data manipulations and transformations using the aggregation pipeline framework.
  • Cross-Platform
    Works on multiple operating systems, enhancing its versatility and deployment options.

Possible disadvantages of MongoDB

  • Memory Usage
    MongoDB can consume a large amount of memory due to its use of memory-mapped files, which may be a concern for some applications.
  • Complex Transactions
    While MongoDB supports ACID transactions, they can be more complex to implement and less efficient compared to traditional relational databases.
  • Data Redundancy
    The flexible schema design can lead to data redundancy and increased storage costs if not managed carefully.
  • Limited Joins
    Joins are supported but can be less efficient and more limited compared to relational databases, affecting complex relational data querying.
  • Indexing Overhead
    Extensive indexing can introduce overhead and impact performance, especially during write operations.
  • Learning Curve
    Requires a different mindset and understanding compared to traditional relational databases, which can present a learning curve for new users.
  • Lacks Mature Analytical Tools
    The ecosystem for analytical tools around MongoDB is not as mature as those for traditional relational databases, which might limit advanced analytics capabilities.
  • Cost
    The cost of using MongoDB's cloud services (MongoDB Atlas) can be high, especially for large-scale deployments.

AWS Greengrass videos

+ Add

Run ML Models at the Edge with AWS Greengrass ML

MongoDB videos

+ Add

MySQL vs MongoDB

More videos:

  • Review - The Good and Bad of MongoDB
  • Review - what is mongoDB

Category Popularity

0-100% (relative to AWS Greengrass and MongoDB)

AWS Greengrass

MongoDB

Data Dashboard
100 100%
Data Dashboard
0% 0
Databases
0 0%
Databases
100% 100
IoT Platform
100 100%
IoT Platform
0% 0
NoSQL Databases
0 0%
NoSQL Databases
100% 100

User comments

Share your experience with using AWS Greengrass and MongoDB. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare AWS Greengrass and MongoDB

AWS Greengrass Reviews

We have no reviews of AWS Greengrass yet.
Be the first one to post

MongoDB Reviews

10 Top Firebase Alternatives to Ignite Your Development in 2024
MongoDB’s superpower lies in its flexibility. Its document-based model lets you store data in a free-form, schema-less way, making it adaptable to evolving application needs. Need to add a new field or change the structure of your data? No problem, MongoDB handles it with ease.
Source: genezio.com
Top 7 Firebase Alternatives for App Development in 2024
MongoDB Realm provides a robust alternative to Firebase, especially for apps requiring a flexible data model. Key features include:
Source: signoz.io
Announcing FerretDB 1.0 GA - a truly Open Source MongoDB alternative
MongoDB is no longer open source. We want to bring MongoDB database workloads back to its open source roots. We are enabling PostgreSQL and other database backends to run MongoDB workloads, retaining the opportunities provided by the existing ecosystem around MongoDB.
Source: blog.ferretdb.io
16 Top Big Data Analytics Tools You Should Know About
The database added a new feature to its list of attributes called MongoDB Atlas. It is a global cloud database technology that allows to deploy a fully managed MongoDB across AWS, Google Cloud, and Azure with its built-in automation for resource, workload optimization and to reduce the time required to handle the database.
Source: www.analytixlabs.co.in
9 Best MongoDB alternatives in 2019
MongoDB is an open source NoSQL DBMS which uses a document-oriented database model. It supports various forms of data. However, in MongoDB data consumption is high due to de-normalization.
Source: www.guru99.com

Social recommendations and mentions

Based on our record, MongoDB should be more popular than AWS Greengrass. It has been mentiond 18 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

AWS Greengrass mentions (5)

  • Orchestrating Application Workloads in Distributed Embedded Systems: Setting up a Nomad Cluster with AWS IoT Greengrass - Part 1
    In this blog post series, we will demonstrate how to use AWS IoT Greengrass and Hashicorp Nomad to seamlessly interface with multiple interconnected devices and orchestrate service deployments on them. Greengrass will allow us to view the cluster as a single "Thing" from the cloud perspective, while Nomad will serve as the primary cluster orchestration tool. - Source: dev.to / about 2 years ago
  • AWS Summit 2022 Australia and New Zealand - Day 2, AI/ML Edition
    AWS IoT Greengrass allows one to manage their IOT Edge devices, download ML models locally, so that inference can then be also be done locally. - Source: dev.to / about 3 years ago
  • Applying DevOps Principles to Robotics
    To assist in deployment and management of workloads in your fleet, it's worth taking advantage of a fleet or device management tool such as AWS GreenGrass, Formant or Rocos. - Source: dev.to / over 3 years ago
  • Machine Learning Best Practices for Public Sector Organizations
    In some cases, such as with edge devices, inferencing needs to occur even when there is limited or no connectivity to the cloud. Mining fields are an example of this type of use case. AWS IoT Greengrass enables ML inference locally using models that are created, trained, and optimized in the cloud using Amazon SageMaker, AWS Deep Learning AMI, or AWS Deep Learning Containers, and deployed on the edge devices. - Source: dev.to / over 3 years ago
  • Looking for a good IoT overview + a simple tutorial
    Take a look at Greengrass https://aws.amazon.com/greengrass/ Enables OTA updates and fleet management. Source: almost 4 years ago

MongoDB mentions (18)

  • Creating AI Memories using Rig & MongoDB
    In this article, we’ll build a CLI tool using the Rig AI framework and MongoDB for retrieval-augmented generation (RAG). This tool will store summarized conversations in a database and retrieve them when needed, enabling the AI to maintain context over time. - Source: dev.to / 2 months ago
  • The Adventures of Blink S2e2: Database, Contained
    Have a Mongo database holding the various phrases we're going to use and potentially configuration data for the frontend as well. - Source: dev.to / 9 months ago
  • Introducing Perseid: The Product-oriented JS framework
    It's also worth mentioning that Perseid provides out-of-the-box support for React, VueJS, Svelte, MongoDB, MySQL, PostgreSQL, Express and Fastify. - Source: dev.to / 8 months ago
  • DocumentDB Elastic Cluster Pricing
    Does anyone know if the most basic Elastic Cluster instance of DocumentDB carries any monthly fixed cost or is it just on-demand cost? Another words if I run like 10,000 queries against the DB per month, what kind of bill would I expect? This is for a super small app. I am currently using mongodb free tier , but want to migrate everything to AWS. Can't seem to find a straight answer to the pricing question. Source: over 2 years ago
  • I wrote some scripts for converting the UTZOO Usenet archive to a Mongo Database
    You can use either MongoDB.com's dashboard (if you host a remote database) or Mongo Compass to run queries on the data or you can modify the express middleware with your own queries. I'm still working on the API, so it's not very robust yet. I will update this when it is. Source: over 2 years ago
View more

What are some alternatives?

When comparing AWS Greengrass and MongoDB, you can also consider the following products

Particle.io - Particle is an IoT platform enabling businesses to build, connect and manage their connected solutions.

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

AWS IoT - Easily and securely connect devices to the cloud.

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

Azure IoT Hub - Manage billions of IoT devices with Azure IoT Hub, a cloud platform that lets you easily connect, monitor, provision, and configure IoT devices.

MySQL - The world's most popular open source database

Loading...