sql MongoDB and PostgreSQL thoughts

A more comprehensive list of statements can be found in the MongoDB documentation. The strength of SQL is its powerful and widely known query language, with a large ecosystem of tools. The right answer for your needs is based of course on what you are trying to do.

MongoDB and PostgreSQL Database Technologies

It also allows users to tune the read committed isolation level up to the serializable isolation level. It can process large volumes of data faster than many other solutions. Users can access the data and make changes or updates to the schema as needed, unlike with the SQL database model where users can only access and store data once it has been processed and properly formatted. Thanks to even data distribution, Cassandra is relevant in applications where large volumes of information are processed. Also, Cassandra fits well with real-time analytics, as it allows linear scaling and data increase in real time.

Senior Solutions Architect

My preference is to keep everything in PostgreSQL to the extent this is possible. However, I would note that doing so really requires expert knowledge of PostgreSQL programming and is not for shops unwilling to dedicate to using advanced features. Since my preference is not something I see listed I will give it to you. Since these constraints disallow any actions that remove links from one table to another and can stop the insertion of invalid data into foreign key columns, this may be a necessary feature for some users.

You can run PostgreSQL as a version that you install and manage yourself, or you can opt for a database as a service option on the major cloud providers. Each implementation performs how the provider behind it intends it to. If you want PostgreSQL support, you need to utilize a cloud version or try third parties providing specialist services. You can accelerate MongoDB’s query performance if you make indexes on fields in documents and sub documents. This database enables all document fields to be indexed and queried simply, as well as those that are deep within sub documents and arrays.


Everything you would ever want from a relational database is present in PostgreSQL. One of the most important parts of the function of any company is a secure database. With phishing attacks, malware, and other threats on the rise, it is essential that you make the right choice to keep your data safe and process it effectively. However, it can be extremely difficult to choose from the wide variety of database solutions on the market today. More recent market entrants, such as HR and finance SaaS vendor Workday, have built their applications on their own database. But the strategy shifted when SAP created its own in-memory database, HANA, around 2010, and built the most recent generation of its ERP application, S/4HANA, on the platform.

  • Sharding distributes data across multiple partitions, and each shard holds a subset of data.
  • In the competitive field of Data Analytics, offering efficient products and services and having a majority customer share in the market does help determine the profit of the company.
  • Also, this DBMS keeps in memory all key names for each value pair.
  • MongoDB is a general-purpose, document-based, distributed database management system built for modern application developers.
  • As a proprietary AWS service, transitioning from DynamoDB to another database system might necessitate significant effort and planning.

In this type of database, data is stored in MongoDB and maps to a flexible schema. If your application’s needs change, you can restructure how your data is stored. Since MongoDB provides schema validation, you can lock down your schema as much or as little as you’d like.

How is MongoDB different from other types of databases?

For writes, it is based on a scale-up architecture, in which a single primary machine running PostgreSQL must be made as powerful as possible in order to scale. For reads, it is possible to scale-out PostgreSQL by creating replicas, but each replica must contain a full copy of the database. Both databases use different syntax and terminology to perform many of the same tasks. Where PostgreSQL uses rows to record data, MongoDB uses documents, etc.

MongoDB and PostgreSQL Database Technologies

MongoDB is based on a distributed architecture that allows users to scale out across many instances, and is proven to power huge applications, whether measured by users or data sizes. The scale-out strategy relies on using a larger number of smaller and usually inexpensive machines. In a relational database, the data in question would be modeled across separate parent-child tables in a tabular schema. This means that updating all the records at once would require a transaction.

MongoDB Key Features

MongoDB is scalable because of partitioning data across instances within the cluster. It doesn’t split the documents into pieces as they are independent units making it easier to distribute them across various servers while data is locally preserved. When starting a new project, postgresql vs mongodb one of the things developers can struggle with is choosing a stack. Zeroing in on the right technology to solve a problem can be a nerve-wracking experience. Databases in particular can be challenging to settle on, especially if you’re unclear about how your data will be used.

MongoDB and PostgreSQL Database Technologies

It also provides you a brief overview of both databases along with their features. Finally, it highlights a few challenges you might face when you use these databases. Read along how you can choose the right database for your organization. In terms of building an OLTP solution and data warehousing applications, Oracle is a good choice as well. When speaking of analytic tools without multiple data layers, it may be reasonable to opt for NoSQL databases like MongoDB. DynamoDB can effortlessly scale up or down to accommodate any level of traffic and data, making it ideal for applications that experience rapid growth or fluctuating demand.

Pros of SQLite

In addition to scalability, it largely contributes to dataset flexibility. Cassandra collects data on the go, and data retrieval shares the same simplicity, despite dataset size. From the data perception and refining perspective, Redis can be considered a colossus.

This is a terrific option if your concerns include exploring the limits of SQL, serving up a huge number of queries from many tables, and compatibility. MongoDB uses primary node replication and secondary nodes to offer availability. PostgreSQL supports B-tree, hash, GIN, GiST, and Sp-GiST index types.

ACID transactions for changing large numbers of documents

This provides redundancy and protection against any downtime that might occur in the event of a scheduled break for maintenance or a system failure, thus increasing the fault tolerance of the database. Partitioning and sharding are essentially about breaking up large datasets into smaller subsets. Sharding implies that the data is stored across multiple computers while partitioning groups this data within a single database instance. BSON skips the keys that aren’t useful for the query, thus making it faster to retrieve data.

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