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MongoDB VS Keywords Everywhere

Compare MongoDB VS Keywords Everywhere and see what are their differences

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MongoDB logo MongoDB

MongoDB (from "humongous") is a scalable, high-performance NoSQL database.

Keywords Everywhere logo Keywords Everywhere

Free browser add-on for keyword volume, CPC & competition
  • MongoDB Landing page
    Landing page //
    2023-10-21
  • Keywords Everywhere Landing page
    Landing page //
    2023-09-19

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.

Keywords Everywhere features and specs

  • Comprehensive Metrics
    Keywords Everywhere provides detailed metrics such as search volume, CPC, and competition data, helping users make informed decisions for SEO and PPC strategies.
  • Ease of Use
    The browser extension integrates seamlessly with essential tools like Google Search, YouTube, and Google Analytics, making it convenient to access keyword data directly from these platforms.
  • Affordability
    Offers a pay-as-you-go pricing model, which can be more cost-effective for small businesses and individual users compared to subscription-based services.
  • Data Across Platforms
    Provides keyword data for multiple platforms including Google, YouTube, Amazon, and more, which is valuable for diverse digital marketing strategies.
  • Time Saver
    By displaying keyword metrics directly in search engine results and other tools, it significantly reduces the time needed to gather and analyze keyword data.

Possible disadvantages of Keywords Everywhere

  • Limited Free Version
    The free version offers very limited features, driving users to purchase credits for more comprehensive data.
  • Dependency on Browser Extension
    Requires a browser extension to function, which may not be suitable for all users or devices and could raise privacy/security concerns.
  • Accuracy Variability
    As with many keyword tools, the accuracy of the data can occasionally be inconsistent, which may affect strategic decisions.
  • Limited Advanced Features
    While great for basic keyword research, it lacks some of the advanced features offered by more robust SEO tools, such as detailed competitive analysis or site audits.
  • Potential for Data Overload
    The abundance of data displayed can sometimes be overwhelming, particularly for beginners who may struggle to interpret and utilize it effectively.

Analysis of MongoDB

Overall verdict

  • MongoDB is generally regarded as a good database solution for applications needing flexibility, scalability, and fast development times. However, it may not be the best choice for applications requiring complex transactions or where ACID compliance is critical, as it originally prioritized availability over consistency. Recent improvements, including multi-document transactions, have addressed some concerns, making it more versatile.

Why this product is good

  • MongoDB is considered a good choice for certain types of applications due to its flexible schema design, scalability, horizontal scaling capabilities, and ease of use for developers who require rapid development cycles. It supports a wide range of data types and allows for full-text search, geospatial queries, and aggregation operations. MongoDB's document-oriented storage makes it well-suited for handling large volumes of unstructured data. Its robust ecosystem, including Atlas for cloud deployments, adds to its appeal by offering automated scaling, backups, and distributed architecture.

Recommended for

  • Applications requiring high scalability and performance with unstructured data
  • Real-time analytics and big data applications
  • Web and mobile applications needing rapid development and flexible data models
  • Projects that benefit from cloud-native solutions with managed services

MongoDB videos

+ Add

MySQL vs MongoDB

More videos:

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

Keywords Everywhere videos

+ Add

How to use Keywords Everywhere - SEO keyword research tool

More videos:

  • Review - KEYWORDS EVERYWHERE is now a PAID TOOL - Here's What To Do - Keywords Everywhere Alternative
  • Tutorial - Keywords Everywhere | A Tutorial + Advice on Keywords for YouTube
  • Review - Keywords Everywhere Review: Better Alternative to Google Keyword Planner
  • Review - Keywords Everywhere Review | Best Keyword Search Volume Chrome Extension! 🚀
  • Review - Keywords Everywhere Review 2021 | Keyword research Tool

Category Popularity

0-100% (relative to MongoDB and Keywords Everywhere)

MongoDB

Keywords Everywhere

Databases
100 100%
Databases
0% 0
SEO Tools
0 0%
SEO Tools
100% 100
NoSQL Databases
100 100%
NoSQL Databases
0% 0
SEO
0 0%
SEO
100% 100

User comments

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Reviews

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

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

Keywords Everywhere Reviews

112 Best Chrome Extensions You Should Try (2021 List)
Keywords Everywhere is an alternative to Ubersuggest, a freemium keyword research tool. It shows the search query data on more than 15 websites. For free users, it shows a trend chart, long-tail keywords, and keywords from ‘people also search for’. But, paid users can see monthly search volume, CPC, competition, and trend data. Although solely for keyword research, you do...
Source: www.einsstark.tech
9 Free Keyword Research Tools (That CRUSH Google Keyword Planner)
Keywords Everywhere is a free add-on for Chrome (or Firefox). It adds search volume, CPC & competition data to all your favourite websites.
Source: ahrefs.com
73 Best SEO tools 2021 – The Most Epic List You Shouldn’t Miss
While most use this tool strictly for Paid ads, Keywords Everywhere is very useful to help you discover long-tail keywords related to the ones you are searching for on Google.
Source: blog.linkody.com

Social recommendations and mentions

MongoDB might be a bit more popular than Keywords Everywhere. We know about 18 links to it since March 2021 and only 16 links to Keywords Everywhere. 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.

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 / 3 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 / 10 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 / 9 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

Keywords Everywhere mentions (16)

  • SEO 101 for Software Developers
    To find keywords I use the tool Keywords Everywhere. It gives you information on how many people search for a particular keyword a month, how difficult it will be to rank for, as well ideas for additional keywords. - Source: dev.to / over 1 year ago
  • How to Manage Your Time as a Software Developer ⌛️
    For example, I do a lot of keyword research for my blog posts and YouTube videos. This generally consists of searching for keywords on Google and then copying the numbers that I get from Keywords Everywhere into a spreadsheet. - Source: dev.to / about 2 years ago
  • My Guide To Shopify Store Keyword Research
    You may be thinking to yourself well that's it right? I know what works and what doesn't, well not exactly because you don't just want to copy everything your competition does or you'll be competing with them all the time and that's a losing battle for most small stores. So step 2 is I cross reference it with another tool called keywords everywhere. As I mentioned this tool can be similar to Ahrefs as you can scan... Source: about 2 years ago
  • GMB Stats?
    Keywords everywhere again, not sure if it's match for you. Source: about 2 years ago
  • Keyword research
    Step 2: keywordseverywhere.com ($10 for 100K SV check - it's a chrome extension), run your list through this and get all SV. Source: about 2 years ago
View more

What are some alternatives?

When comparing MongoDB and Keywords Everywhere, you can also consider the following products

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

KeywordTool.io - KeywordTool.io is the best FREE alternative to Google Keyword Planner and Ubersuggest. It uses Google's autocomplete feature to get over 750+ long-tail keywords for any given query.

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

Moz - Backed by industry-leading data and the largest community of SEOs on the planet, Moz builds tools that make inbound marketing easy.

MySQL - The world's most popular open source database

Ahrefs - Ahrefs is a toolset for SEO and marketing. We have tools for backlink research, organic traffic research, keyword research, content marketing & more. Give Ahrefs a try!

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