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Competitive Landscape - MongoDM

We provide a competitive landscape analysis of MongoDB and its two competitors — Caspio and Redis. All of our findings have been compiled in the attached spreadsheet.


To find two competitors of MongoDB, we first looked at Crunchbase to see whether there were any companies that identified as "general purpose database platforms" as this is how MongoDB describes themselves. This led us to Caspio which is a "database platform for creating business applications, custom forms and reports fast and without coding", essentially providing the same services as MongoDB. This is further discussed in the spreadsheet.

As there were no other similar companies that we were able to pinpoint simply by looking at Crunchbase, we proceeded to find a technical review website which compared similar databases available. According to such a website, the number one competitor for MongoDB is Redis, which is "an open-source, in-memory NoSQL database, benchmarked as the fastest database available today". Therefore, we used Redis as the second competitor in our spreadsheet. After reviewing both companies, we felt confident that they were operating in the same market space as MongoDB.


We give insight into MongoDB, Caspio and Redis, providing a company overview, competitive advantage, target market, products and services, pricing, and customer feedback. We also link all of our sourcing in the spreadsheet.

All three companies offer a cloud-based, open-end database that is targeted towards large companies in different industries. All companies offer their product for free, but with limited capabilities. Their offering is then expanded in to different price categories, depending on the memory size and other specifications a company might need.

In the spreadsheet, we analyze all the requested data except for providing our own opinion which we will list here.
MongoDB seems to offer the widest range of products and upgrades out of the three companies. Mongo's biggest pull is the fact they offer geographic distribution, which isn't mentioned with the other two companies. Their website also provides the most information into their offering and pricing.

Caspio's biggest advantage is that they are fully self-funded and have a unique set of core values, specifically when it comes to providing excellent service. They also offer version specifically tailored for certain industries, such as the healthcare industry, by fitting different requirements, such as HIPAA, FERPA and FIPS-140 compliance. Their main disadvantage seems to be that they offer only four different plans, and don't really allow for customization.

Redis offers a variety of solutions and plans. Their main pull is that they offer a software-based solution, a cloud-based solution and a VPC-based solution. This distinguishes them clearly from the other two companies. Their biggest disadvantage is the fact they are clearly focused on four industries, and don't provide solutions for companies outside those industries.

All of our findings have been compiled in the attached spreadsheet.
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MongoDB - Demographics

While there is no pre-existing information to fully answer your question, we've used the available data to pull together key findings on the customers and use cases for MongoDB. The company focuses on the sectors of government, retail, financial services, high-tech, media and entertainment, healthcare, and telecommunications. Their clients include B2B customers like eBay, Forbes, Facebook, Google, NYT, Cisco, UK Government, ADP, Pearson, Barclay's, MTV, Verizon, AstraZeneca, Metlife, and many others. The use cases include areas like single view, IoT, mobile, real-time analytics, personalization, content management, and catalog.

Below you'll find an outline of our research methodology to better understand why the information you've requested is publicly unavailable, as well as a deep dive into our findings.


While there is an extremely limited number of news articles on MongoDB customers, we have found the company to be a B2B and we have provided an analysis of each company as personal demographics were not publicly available since its customers are B2B and not consumers where we can find demographic information on consumers. We have included general attribute data of the businesses MongoDB provides services for.

Since searching news articles directly was fruitless, we side-stepped the issue by instead searching for company reports and customer information on the website. We also researched the MongoDB customers to find company attributes.


We have the regional locations of MongoDB customers and B2B customers they partner with and general attributes of their customers. We have also identified use cases of MongoDB services.


Customers include businesses based in North America, Europe, Middle East, Africa, Asia, and Pacific. The sector breakdown of its customers is government, retail, financial services, high-tech, media and entertainment, healthcare, and telecommunications. Each of the sectors is listed below with a selection of companies that utilize MongoDB services and their uses.

Companies: City of Chicago, UK government, YouGov, Driver and Vehicle License Agency, Department of Veteran Affairs, HM Revenue & Customs, defense, and intelligence agencies.

Companies in this sector have the following attributes:
1) They can be public, private or government
2) Revenues from $4.3M — $17 million
3) Employee counts of 167 - 377,000

The government sector uses MongoDB for big data, market research, scalability, and digital work.
Companies: eBay, Chico's, GAP

Companies in this sector have the following attributes:
1) They are typically public
2) Revenues from $9.6 billion — $15.9 billion
3) Employee counts of 14,000 - 135,000

The retail sector uses MongoDB for to increase revenue with its e-commerce platform and cataloging ability.
Companies: Barclay's, ADP, Pearson, and Royal Bank of Scotland.

Companies in this sector have the following attributes:
1) They are typically public
2) Revenues from $6 — $24 billion
3) Employee counts of 41,000–377,000

The financial sector uses MongoDB for its ability to analyze risk, asset management and its ability to reinvent systems faster and better.

Companies: Cisco, Thermo Fisher Scientific, Amadeus, Practice Fusion, Vivint, Facebook, Expedia, and Google.

Companies in this sector have the following attributes:
1) They are typically public
2) Revenues from $10 — 94 billion
3) Employee counts of 22,000–72,000

The high-tech sector uses MongoDB for its ability to create innovation faster and less expensive. They use analytics, personalization, and single view.

Companies: NYT, Forbes, NowTV. Elsevier, the Weather Channel, EA Sports, NBC Universal, and MTV.

Companies in this sector have the following attributes:
1) They are typically private
2) Revenues from $150$456 million
3) Employee counts of 1,100–2,950

The media and entertainment sector uses MongoDB for mobile sites, scalable user experiences, and big data scalability.

Companies: Medtronic and AstraZeneca, Genentech, Thermo Fisher Scientific, and MetLife.

Companies in this sector have the following attributes:
1) They are typically public
2) Revenues from $16 -$32 billion
3) Employee counts of 60,000 - 92,000

The healthcare sector uses MongoDB for innovation and analytical reporting, accelerating drug R&D, and reducing experiment times.

Companies: Telefonica Digital, Bouygues Telecom, Verizon, Nokia.

Companies in this sector have the following attributes:
1) They are typically public
2) Revenues from $6 billion — $124 billion
3) Employee counts of 9,200 - 160,000

The Telecommunications sector uses MongoDB for digital acceleration to market, flexibility, scalability, performance, and customer insight with big data.


MongoDB has seven uses cases which are single view, IoT, mobile, real-time analytics, personalization, content management, and cataloging.

1) Single View- A real-time view of business for policies, claims, transactions, and customers.

2) Internet of Things- Utilize sensor data and have faster application builds for machine, event management, and UI for remote condition monitoring.

3) Mobile- Mobile app building, scalability, and speed for variable data and dynamic schema.

4) Real-Time Analytics- Real-time, lightweight, operational analytics at your fingertips. This includes batch analytics with a model-driven insight, summarization, transformation, reporting and high-speed aggregates for sensor data, market data, click streams, social media, and log data.

5) Personalization- Upgrade user experiences with having everything at the fingertips and personalized to customers. This includes an e-Commerce platform, web and mobile content management, ERP/CRM applications, collaboration and communication apps. User information includes demographics, interactions, social feed, and contextual.

6) Content Management- You can use a single database and integrate any feature. Types of services in content management are in a single database for texts, social feed, comments, and user reviews.

7) Catalog — You can organize everything from SKU's to equipment all in one location. Types of services in the catalog are e-Commerce, asset management, reference data, digital media catalog, metadata, inventory, reviews, and pricing.


Revenue for 2016 was $101.4 million. This was approximately a 33% increase from the previous year of $65.3 million.


Despite the limited capabilities of internet archiving on the demographics of MongoDB users, we were able to locate customers and revenue of the customers, but additional information on sex, age group, education level, income level, marital status, and occupation was publicly unavailable. We have included use cases and the types of services the clients are using it for. The sectors of MongoDB B2B customers are government, retail, financial services, high-tech, media and entertainment, healthcare, and telecommunications. Some B2B clients include Facebook, Google, Forbes, NYT, NBC Universal, MTV, AstraZeneca, Genentech, UK government, Royal Bank of Scotland, Cisco, and Expedia. Some service use cases include single view, IoT, mobile, real-time analytics, personalization, content management, and cataloging.

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MongoDM - Issues

With over 15 million downloads in 2016, MongoDB is widely considered an easy to learn and efficient product, making it arguably the most popular NoSQL databases available. However, the flexibility provided by being a NoSQL database results in several user concerns. Although the new update set to release in the summer of 2018 will address this and several other short-comings, security, allowable file size, and lack of relational data are primary concerns.


Across the articles referenced below, not one complained about MongoDB uptime. In fact, Medium on Jan. 19, 2017, claims the database "is designed to offer exceptional uptime" and has successful recovery and backup features in case of system failure. The lack of mention in the articles in either a negative or positive light does suggest that users have no concerns about uptime.

Security is a Concern

In May 2017, several MongoDB users were hacked and proven insecure. MongoDB is an open-sourced software that users may easily download and install. Unfortunately, this easy installation sometimes results in system administrators not being aware of the security options and proper steps to secure their databases. Shortly after nearly 30,000 MongoDB databases were compromised, the company released guidelines to properly secure the software. Several experienced MongoDB users are spreading similar advice; however, users must be aware that the "default settings are not secure" and should do some research. At bare minimum, users should secure port 27017.

Allowable File Size is a Concern

Although MongoDB is an excellent tool for quickly retrieving information, users have complained that documents are limited at 16mb, requiring users to have a 64bit system to successfully work with the software. If the file is too big, it might be lost without warning. There are ways to accommodate this shortcoming, but it will cause users additional work and frustration.

Software is Not Relational

The flexibility and ease of MongoDB is due to its lack of SQL. However, this does mean data with a lot of relationships is not a good fit for the program. In fact, some people claim that the ideal data for MongoDM would have "zero connections" with one another.

There are third party applications that will assist in making MongoDB work with relational data, and it is possible to embed relationships within the data.

MongoDB experts do provide hints for finding related information and suggest maximizing the amount of information placed in each document. The company also claims that doing so will allow users to mimic a relational database. However, there does not appear to be an easy or guaranteed way to mesh the flexibility and ease of a NoSQL database with the reliability and relational environment of a SQL database. This drawback does not appear to be enough to keep many people from using the software; however, it is a weakness that potential customers should be aware of.

Issues Resolved by Summer 2018 Update

Until MongoDB 4.0 has been tested in the open market, it is difficult to ensure the validity of its claims, but so far, the new release appears capable of resolving the following concerns:


Because MongoDB was not previously able to claim ACID support, many users preferred other databases. However, the upcoming update claims to provide Atomicity, Consistency, Isolation, and Durability (ACID) "support for multi-document transactions" and will resolve previous concerns.


Several users expressed frustration in search functionality and use of indexes. However, the new update claims to resolve this concern, providing better filters and searchability.


Durability is one of the four foundational requirements of a database, and in the past, several users have complained about lost data. This shortcoming appears to be one of the main focuses of the new update, and this should no longer be a concern.


Because MongoDB is asynchronous, when a change is made in one place in the system, it might not be translated across the system or not executed correctly. However, the new version promises any change will not be made permanent unless "everything is executed correctly" and this should eliminate "partial changes or corrupted data".

Additional Concerns

There were additional concerns mentioned by isolated users. The following issues were not discussed in length nor mentioned by many individuals, so they do not appear to be overarching concerns, but they are as listed:

Have to translate SQL to Mongo DB for some searches


Based on my research, the main issues to those running programs on MongoDB are security, allowable file size and the lack of a relational data environment.
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MongoDM - C-Suite view

Our research indicates that traditional, relational databases almost universally employ SQL; and in SQL, viewing privileges are set per viewer, either for an entire database or for specific objects within the database. MongoDB, on the other hand, grants viewing privileges by role assignments. Each user must be assigned at least one role in order to access the system, and the roles to which they have access determine what information they can view. These methods of granting privilege directly control the C-suite view of any company's database; however, it should be noted that these methods are applicable all users, across both systems, regardless of the individual's job position. Below you'll find our relevant findings.


It should be noted that there is no information available which directly states what a given executive, or group of executives, may be privileged to see, in either MongoDB or SQL-based relational databases. We assume this is likely because those view settings will be unique to each company and each executive's needs. For this reason, we have assumed that 'view,' in the context of 'C-suite view,' refers to viewing privileges/access; and our report describes how viewing privileges are granted within both types of system, since this is the direct method by which a 'C-suite view' would be established within the system.

We have also assumed that 'traditional databases' refers to relational databases, which are the database model predating the non-relational models such as MongoDB. Because SQL is "is the standard means of manipulating and querying data in relational databases," we have assumed that comparisons between MongoDB and SQL viewing functionality is a sufficient base for comparison between MongoDB and 'traditional' databases.


Our research indicates that MongoDB is a non-relational database, and this structure allows it to handle much larger data loads with far more ease and efficiency than relational models. However, non-relational databases scale horizontally (unlike relational databases which scale vertically), and this design creates inherent security concerns, especially relative to access. One method for dealing with these security issues is the integration of third-party software solutions like Centrify, which allows for "centralized identity and access management across platforms." While the bulk of our report focuses on the C-suite view functionality built into the MongoDB and relational systems, we have provided this additional reference to third-party software because this type of software solution, if integrated into the database, could affect the C-suite view of MongoDB.


The SQL developer manual indicates that privileges within a SQL-based relational database are granted either by granting viewing privilege to an entire database, or to objects within the database. In the latter case, this means that viewing privileges must be set for each object in the database. This is largely due to the fact that the relational model is static, so the objects themselves do not change. In SQL, privileges are granted using the 'create viewer privilege' code 'Create_view_priv.' Viewing privileges are assigned per individual.


The MongoDB manual states: "MongoDB employs Role-Based Access Control (RBAC) to govern access to a MongoDB system. A user is granted one or more roles that determine the user’s access to database resources and operations. Outside of role assignments, the user has no access to the system." We assume that a C-suite executive is not likely to be a database administrator, as database administration is not generally within the province of C-suite executive roles. For database users (as opposed to database administrators), the built-in roles are limited to 'read' and 'read/write.' User-defined roles (i.e., custom designed roles) can also be created, and are likely to be the function used to create views specific to executive staff. These user-defined roles can then be used to create access to specific collections of information within the database, which could reasonably be used to create a set of data specific for the C-suite view. In MongoDB, executives need to be assigned the roles which allow them access to the information that they need. Access to information is granted per roles, not per individual users.


To wrap it up: traditional, relational SQL-based databases assign viewing privileges based on objects and databases; MongoDB assigns viewing privileges based on roles. This difference in the method of granting access creates security concerns in non-relational databases such as MongoDB.
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Database Solutions - Buying Process

We have provided an overview on the traditional buying process for companies looking for a database solution. Issues to consider during the evaluation process include how often the data will be modified, what hardware is available, and if there is a budget for additional hardware. The positions involved in database selection and purchasing may include architects, developers, database administrators, and operations staff. Decision makers will want to seek out information sources that weigh the pros and cons of each database solution, including whether different users are able to fix bugs, and the frequency of database updates. Lastly, a case study illustrates how fraud protection software company Anura decided on a database solution.


We researched issues surrounding how the buying process begins; who is involved in the buying process; the evaluation process; what information sources are consulted; and how the decision is ultimately made. We primarily researched tech industry sources. We also found a case study detailing how Anura and its parent company eZanga decided on a database solution.

When we initially started our research, we looked for analyses of small businesses versus large businesses. However, we found most publicly available information on database solution selection surrounds what features a database has, with little focus on the organization's overall size. We researched database solutions that were on the list you provided, including MongoDB and MySQL.

As with many other business decisions, the process of researching and ultimately purchasing a database solution will vary from organization to organization. By consulting tech industry sources, we have created an overview of what the typical process may look like.

starting The buying & evaluation process

Before purchasing a database, Lifewire recommends performing a needs analysis for your organization, looking at these issues:

"Who will be using the database and what tasks will they perform?
How often will the data be modified? Who will make these modifications?
Who will be providing IT support for the database?
What hardware is available? Is there a budget for purchasing additional hardware?
Who will be responsible for maintaining the data?
Will data access be offered over the Internet? If so, what level of access should be supported?"

Answering these questions will help narrow down the database options that are most appropriate to an organization's needs.

who is involved in the Process

For organizations that purchase MongoDB, Mongo assigns a Dedicated Consulting Engineer (DCE) to assist with deployment. Mongo's DCEs are described as working with, "architects, developers, DBAs, and operations staff," during the deployment process. Since these positions all use the database, we can reasonably assume that they may also be consulted during the initial evaluation and buying stages. Each position can lend insight to create a big picture of the organization's database needs.

consulting information sources

As with many consumer products, there are pros and cons to consider for a database solution. One source we found that does a compare and contrast of three database solutions is a July 2017 article in Open Source magazine.

"Choose the Right Database for Your Application," weighs the pros and cons for MySQL, SQLite, MongoDB, and MariaDB. Some issues considered in the article are whether different users can fix bugs, the frequency of database updates, if the database has an encrypted storage engine, and if it enables validation of documents.

Besides consulting industry publications, decision-makers may also reach out to the database companies. For example, MongoDB posts a wealth of information on their website, including white papers and webinars.

Making the decision

After identifying database needs and weighing the pros and cons with key decision makers, a database will be chosen. A case study on fraud protection software company Anura and its parent company eZanga covers their entire database selection process, from identifying needs through deployment.

"The eZanga team compared a number of SQL and NoSQL based solutions including Galera, Couchbase, RocksDB, and several others before ultimately deciding to standardize on MySQL Cluster to support This decision was largely based on the need to provide a completely redundant, high performing, and uninterrupted service to their customers." If you are interested in more database solution case studies, MySQL has an extensive list on their website.


We have provided an overview of the buying process for companies looking for a database solution. The process typically starts with a needs analysis by an organization's key players, which may include architects, developers, database administrators, and operations staff. For information sources, we found an article in Open Source magazine that weighs the pros and cons of different database solutions. Lastly, we provided a case study that details Anura's database selection, from needs analysis through deployment.
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Database Solutions - Importance Level

C-level executives from technology companies are more likely to be directly involved in choosing their database solutions. Information directly addressing decisions in this regard in other industries was not available. However, according to several studies and market reports, big data and, in consequence, data management is becoming increasingly important for defining strategies at an executive level for all industries. Therefore, many companies have appointed Chief Technology Officers (CTOs) or Chief Data Officers (CDOs) to lead big data and IT investment strategies, including decisions related to database solutions.


After going over several news articles and blogs, I was unable to identify a definitive trend across industries and companies regarding what executive ultimately chooses a database solution. The research did allow me to understand database solutions are often chosen according to company needs, which may differ depending on their business model and industry. B2C companies usually invest more resources in database management systems to obtain and analyze more information about their customers and, if possible, having the ability to provide better access to their products through online or digital means (for example, through apps, websites, or online stores).

Other businesses may use database management systems for internal operations, and may not necessarily be directly using choosing one. For example, some companies use Enterprise Resource Planning software (ERPs) to help them generate and analyze information about company operations and processes. ERPs use database management systems in the backend, but companies buying an ERP make a decision to purchase based on functionalities provided by the ERP, not focusing on details regarding the software's backend database management system on which it is built.

The research showed CEOs from tech companies that develop content management software or cloud-based solutions do consider database solutions as 'mission critical'. There are several blog posts from tech companies' C level executives discussing the variables that are important to consider for choosing a database management solution. Often, the blog posts include highly technical information which is better suited for executives well versed in database management. In these cases, CEOs are more involved in the decision-making process of selecting IT systems and services mainly because it is a core asset for their business (see Baqend and Structr).


Information about database management systems being acquired by companies in non-tech industries is not widely available. After searching through company websites, surveys, or reports from consulting firms, I was unable to find any blog posts or interviews with C-level executives from other industries that discussed database management systems specifically. This does not mean they are not involved in the IT decision-making process, however. As several studies from McKinsey, PwC, and Deloitte point out, C-level executives are not usually involved and/or thoroughly informed about the latest technology developments and how it may affect their company. Nonetheless, there is a strong acknowledgment that technology is the main disruptor across all industries. As a result, McKinsey has found numerous CEOs and companies are working to make their company boards more technologically savvy.

A recent trend in this direction has been the appointment of Chief Technology Officers (CTOs) or Chief Data Officers (CDOs). According to McKinsey, 81% of C-Suites surveyed agreed their company "is not ahead of the curve" in technology trends and several have begun working on C-Suite structured areas in charge of relevant technology trends, IT management, and data analysis. So far, 54% of companies have appointed a CTO or CDO. Several reports from consulting firms and think tanks have worked on determining which profiles and responsibilities CTOs or CDOs should have.

Therefore, it is safe to say companies are increasingly finding data management to be a priority. PwC found that investing in IT is one of the most relevant decisions that CEOs face today. In a survey undertaken by the consulting firm, 15% of executives considered IT investment as the most important topic they dealt with. Of those respondents, 53% considered their decisions where somewhat data-driven, while 40% considered they were highly data-driven. According to their analysis, C-level executives currently use data to understand what is happening in their business, but they are working in data analytics to make future predictions of what will happen in their business, that is, develop better forecasting tools with big data.


Decisions about what database solution to use are often undertaken by C-level executives of tech companies. I was able to find several blogs and interviews where executives from tech companies discussed their database solution choices. However, I was unable to find any information about C-level executives in other industries. Nonetheless, recent studies and reports from consulting firms and think tanks show data management is increasing its relevance for company operations. Consequently, more companies are likely to appoint CTOs or CDOs who can define strategies and develop data-driven tools which can improve forecasting and provide data relevant to the decision-making process.

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MongoDB Atlas - Competing Products

Competing database services that developers and IT managers use other than MongoDB Atlas includes the following: DataStax's Apache Cassandra database, MapR's Converge Data platform, Databricks' platform, Basho's Riak database, and Stratoscale's Chorus database.


In order to find the competitors of MongoDB Atlas, I first looked at the service features that it provides. MongoDB Atlas is described as a cloud-managed MongoDB service that offers the following: database management, installation, configuration, software management, monitoring, back-ups, and other required setup needed to fully deliver a platform or a Database as a Service (DBaaS) product. MongoDB Atlas also enables quick app development. Given that database setups are technical in nature and involve software handling, I have assumed that the users of these services are developers and other technical personnel like IT managers who are familiar with software languages. Using this as a reference, I have looked for other DBaaS being offered by other companies. I have also found a compiled competitor list for MongoDB Atlas that I have verified first before including in the list below. I excluded those services that are more for the operations team.


The following are the competitors of MongoDB Atlas that are based on a compiled competitor list and a related company profile: DataStax's Apache Cassandra database, MapR's Converge Data platform, Databricks' platform, Basho's Riak database, and Stratoscale's Chorus database.


Datastax provides database administration services for the use of its Apache Cassandra platform, especially in application development. As cloud applications become much more in demand, there is a need for the level of service that Datastax can provide to application developers and presumably to their IT managers. The Datastax platform should be able to handle all the data volume and complexity required in order to efficiently distribute the required load and enable the development of necessary applications. The load balancing is particularly important as it reduces the stress that was being borne by individual machines handling the processes. Furthermore, by leveraging Datastax’s strategic platform design of incorporating machine redundancies, defective machines can easily be replaced. This distributed load setup can also allow for the seamless transfer of the work across all data center locations such as on-site, in the cloud, and even in both. This will also ensure that the application is always on and can be accessed as needed.


MapR provides a managed data platform where a large amount of information can be stored for various purposes such as in the development of applications. MapR’s data platform can support open source projects and can strictly adhere to industry-standard APIs for a more streamlined method of developing new applications. Applications are listed in the MapR’s app gallery where developers can support and sell them.
MapR can also provide professional services that can assist during the various stages of application development. Once deployed, there is no need for the client to recruit and train people who will manage the platform on an ongoing basis. Machine learning, AI, and human experts provided by MapR can keep the platform safe and operational day-by-day.


Databricks’s Apache Spark-powered platform enables the development and storage of various tools and apps for multiple industries. With Apache Spark, data analytics activities can be consolidated without the need to use multiple tools. Workflows can also be simplified in order to shorten the launch time. This will result in productivity improvement for all teams that are involved in the workflow. Risks can also be reduced due to the security provided by the platform.
Databricks’ platform also has an interactive workspace feature that can help in the development of data components, enable training machine learning models, and share insights from the same platform. The platform’s integrated data connectors and BI tools can make it easier to generate insights from the data that came from various sources.


Basho’s Riak databases features flexible structures for multiple data storage requirements of applications and systems. It can also be used to link to IoT devices and other technical elements. Basho can also provide various support and professional services that can help open source and enterprise developers in their app development endeavors.
Basho further offers its own network of distributed systems experts who will collaborate with application development teams to build highly-distributed web and IoT apps. These teams plan to overcome the usual challenges encountered when building an app such as lack of data, inaccurate information, and cost of building up the apps.
Basho’s Riak promises to deliver a sustainable, innovative, and massive platform that can help develop high quality apps.


Stratoscale’s Chorus provides a database as a service (DBaaS) offering that includes a feature where developers can choose the most fitting tool for the task from well-known open source and commercial databases.
Other features of Stratoscale’s Chorus that can help developers include the following: (1) availability of self-service database management; (2) databases that can be easily scaled and upgraded uniformly to manage versions; (3) databases that can easily be wounded up or down for optimum resource utilization; (4)overall system reliability and adaptability; and (5) multi-tenant database service offering.


DataStax's Apache Cassandra database, MapR's Converge Data platform, Databricks' platform, Basho's Riak database, and Stratoscale's Chorus database are the products of MongoDB's competitors that developers and IT managers use.

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SWOT - MongoDB Atlas

We conduct a SWOT analysis of MongoDB Atlas. We identified 10 strengths, weaknesses, threats and opportunities that have been noted by developers and IT Managers on forums and in reviews. All findings have been compiled in the attached spreadsheet.


We first looked into what is MongoDB Atlas. It is a "cloud-hosted MongoDB service engineered and run by the same team that builds the database. Build on MongoDB Atlas with confidence, knowing you no longer need to worry about database management, setup and configuration, software patching, monitoring, backups, or operating a reliable, distributed database cluster."

Basically, it is a comprehensive MongoDB service. Therefore, we looked into major strengths, weaknesses, threats and opportunities regarding MongoDB Atlas, and expanded our search to see what are the MongoDB SWOT comments as well. For those comments that were targeted towards MongoDB, we checked whether they were based on the same specifications, such as cloud-based operation, similarities in security issues etc.

There is a higher-than-regular amount of quotes featured in our research. This was done only for those quotes we deemed would lose their tone and direct nature if we would interpret them, especially when it comes to highly technical comments. For all the comments we were able to interpret, we provide a summary without using direct quotes.

Our findings have been compiled in the attached spreadsheet.

From Part 02