7 Best Cloud Database Platforms

Cloud computing has opened new doorways for app improvement and internet hosting. Earlier than cloud companies turned mainstream, builders needed to preserve their very own costly servers. Now, cloud platforms like AWS and Azure present simple database internet hosting with out the excessive {hardware} prices. Cloud databases provide the pliability and comfort of the cloud whereas offering commonplace database performance. They are often relational, NoSQL, or some other database mannequin, accessed by way of API or net interface.

On this assessment article, we’ll discover the highest 7 cloud databases utilized by professionals to construct sturdy functions. These main cloud database platforms allow builders to effectively retailer and handle knowledge within the cloud. We are going to look at the important thing options, execs, and cons of every platform, so you may decide which one is the appropriate match to your app improvement wants.


Azure SQL Database


Azure SQL Database is a completely managed relational cloud database that’s a part of Microsoft’s Azure SQL household. It gives a database-as-a-service resolution constructed particularly for the cloud, combining the pliability of a multi-model database with automated administration, scaling, and safety. Azure SQL database is all the time up-to-date, with Microsoft dealing with all updates, backups, and provisioning. This permits builders to deal with constructing their functions with out database administration overhead.

🔑 Azure SQL Database Key Factors

  • Serverless computing and hyperscale storage options are each versatile and responsive
  • A completely managed database engine that automates updates, provisioning, and backups
  • It has a built-in AI and excessive availability to make sure constant peak efficiency and sturdiness

✅ Execs

  • Person-friendly interface for creating knowledge fashions
  • Simple billing system
  • Absolutely managed and safe SQL database
  • Seamless migration from on-premise to cloud storage

❌ Cons

  • Job and activity managers work in several methods
  • Restricted database measurement
  • Want for extra environment friendly notification and logging system for database errors
  • Expensive scaling up and down with out correct automation implementation


Amazon Redshift


Amazon Redshift is a fully-managed, petabyte-scale cloud-based knowledge warehousing resolution designed to assist organizations retailer, handle, and analyze giant quantities of information effectively. Constructed on prime of the PostgreSQL open-source database system, Redshift makes use of columnar storage expertise and massively parallel processing to ship quick question efficiency on excessive volumes of information. Its distributed structure permits it to elastically scale storage and processing energy to accommodate rising knowledge volumes. Its tight integration with different AWS companies additionally permits seamless knowledge loading from S3, EMR, DynamoDB, and so on. The tip result’s a performant, cost-effective, and versatile cloud knowledge warehouse resolution appropriate for large-scale knowledge analytics.

🔑 Amazon Redshift Key Factors

  • It makes use of column-oriented databases
  • Its structure is predicated on massively parallel processing
  • It consists of machine studying to enhance efficiency
  • It’s fault tolerant

✅ Execs

  • Straightforward setup, deployment, and administration
  • Detailed documentation that makes it simple to be taught
  • Seamless integration with knowledge saved in S3
  • Simplified ETL setup

❌ Cons

  • JSON help in SQL is restricted
  • Array sort columns are lacking and are routinely transformed to strings
  • The logging perform is nearly non-existent


Amazon DynamoDB


Amazon DynamoDB is a quick, versatile, and dependable NoSQL database service that helps builders construct scalable, serverless functions. It helps key-value and doc knowledge fashions, and might deal with large quantities of requests day by day. DynamoDB routinely scales horizontally, guaranteeing availability, sturdiness, and fault tolerance with none further effort from the consumer. Designed for internet-scale functions, DynamoDB affords limitless scalability and constant efficiency with as much as 99.999% availability.

🔑 Amazon DynamoDB Key Factors

  • The power to deal with over 10 trillion requests per day
  • Help for ACID transactions
  • A multi-Area and multi-Grasp database
  • NoSQL database

✅ Execs

  • Quick and easy to function
  • Deal with knowledge that’s dynamic and continually altering
  • Listed knowledge could be retrieved rapidly
  • Performs exceptionally effectively even when working with large-scale functions

❌ Cons

  • If the useful resource shouldn’t be monitored appropriately, the bills could be important
  • Doesn’t help backup in several areas
  • It may be costly for initiatives that require a number of environments to be created


Google BigQuery


Google BigQuery is a robust, fully-managed cloud-based knowledge warehouse that helps companies analyze and handle large datasets. With its serverless structure, BigQuery permits lightning-fast SQL queries and knowledge evaluation, processing thousands and thousands of rows in seconds. You’ll be able to retailer your knowledge in Google Cloud Storage or in BigQuery’s personal storage, and it seamlessly integrates with different GCP merchandise like Knowledge Movement and Knowledge Studio, making it a best choice for knowledge analytics duties.

🔑 Google BigQuery Key Factors

  • It could scale as much as a petabyte, making it extremely scalable
  • It affords quick processing speeds, permitting you to investigate knowledge in real-time
  • It’s accessible in each on-demand and flat-rate subscription fashions

✅ Execs

  • Robotically optimizes queries to retrieve knowledge rapidly
  • Nice buyer help
  • Its knowledge exploration and visualization capabilities are very helpful
  • It has a lot of native integrations

❌ Cons

  • Importing databases utilizing Excel could be time-consuming and vulnerable to errors
  • Connecting to different cloud infrastructures like AWS could be troublesome
  • The interface could be troublesome to make use of in case you are not conversant in it


MongoDB Atlas


MongoDB Atlas is a cloud-based, absolutely managed MongoDB service that enables builders to rapidly setup, function, and scale MongoDB deployments within the cloud with just some clicks. Developed by the identical engineers that construct the MongoDB database, Atlas gives all of the options and capabilities of the favored document-based NoSQL database, with out the operational heavy lifting required for on-premise deployments. Atlas simplifies MongoDB cloud operations by automating time-consuming administration duties like infrastructure provisioning, database setup, safety hardening, backups, and extra.

🔑 MongoDB Atlas Key Factors

  • It is a document-oriented database
  • Sharding function permits for simple horizontal scalability
  • The database triggers in MongoDB Atlas are highly effective and might execute code when sure occasions happen
  • Helpful for time collection knowledge

✅ Execs

  • It’s simple to regulate the size of the service primarily based in your wants
  • There are free and trial plans accessible for analysis or testing functions, that are fairly beneficiant
  • Any database data that’s uploaded to MongoDB Atlas is backed up
  • JSON paperwork could be accessed from anyplace

❌ Cons

  • It isn’t potential to immediately obtain all data saved in MongoDB Atlas clusters
  • Lacks extra granular billing
  • No cross desk joins




Snowflake is a robust, self-managed knowledge platform designed for the cloud. In contrast to conventional choices, Snowflake combines a brand new SQL question engine with an revolutionary cloud-native structure, offering a sooner, easier-to-use, and extremely versatile resolution for knowledge storage, processing, and analytics. As a real self-managed service, Snowflake takes care of {hardware} and software program administration, upgrades, and upkeep, permitting customers to deal with deriving insights from their knowledge.

🔑 Snowflake Key Factors

  • Present question and desk optimization
  • It affords safe knowledge sharing and zero-copy cloning
  • Snowflake helps semi-structured knowledge

✅ Execs

  • Snowflake can ingest knowledge from varied cloud platforms, equivalent to AWS, Azure, and GCP
  • You’ll be able to retailer knowledge in a number of codecs, together with structured and unstructured
  • Computer systems are dynamic, which means you may select a pc primarily based on value and efficiency
  • It is nice for managing completely different warehouses

❌ Cons

  • Knowledge visualization might use some enchancment
  • The documentation could be arduous to grasp
  • Snowflake lacks CI/CD integration capabilities


Databricks SQL


Databricks SQL (DB SQL) is a robust, serverless knowledge warehouse that lets you run all of your SQL and BI functions at an enormous scale, with as much as 12x higher value/efficiency than conventional options. It affords a unified governance mannequin, open codecs and APIs, and helps the instruments of your alternative, guaranteeing no lock-in. The wealthy ecosystem of instruments supported by DB SQL, equivalent to Fivetran, dbt, Energy BI, and Tableau, lets you ingest, rework, and question all of your knowledge in-place. This empowers each analyst to entry the most recent knowledge sooner for real-time analytics, and permits seamless transitions from BI to ML, unleashing the complete potential of your knowledge.

🔑 Databricks SQL Key Factors

  • Centralized governance
  • Open and dependable knowledge lake as the muse
  • Seamless integrations with the ecosystem
  • Fashionable analytics
  • Simply ingest, rework and orchestrate knowledge

✅ Execs

  • Enhanced collaboration between Knowledge Science & Knowledge Engineering groups
  • Spark Jobs Execution Engine is very optimized
  • Analytics function just lately added for constructing visualization dashboards
  • Native integration with managed MLflow service
  • Knowledge Science code could be written in SQL, R, Python, Pyspark, or Scala

❌ Cons

  • Working MLflow jobs remotely is difficult and wishes simplification
  • All runnable code have to be stored in Notebooks, which aren’t preferrred for manufacturing
  • Session resets routinely at instances
  • Git connections could be unreliable


Cloud databases have revolutionized how companies retailer, handle, and make the most of their knowledge. As now we have explored, main platforms like Azure SQL Database, Amazon Redshift, DynamoDB, Google BigQuery, MongoDB Atlas, Snowflake, and Databricks SQL every provide distinctive advantages for app improvement and knowledge analytics.

When selecting the best cloud database, key components to contemplate are scalability wants, ease of administration, integrations, efficiency, safety, and prices. The optimum platform will align along with your infrastructure and workload necessities.

Abid Ali Awan (@1abidaliawan) is a licensed knowledge scientist skilled who loves constructing machine studying fashions. Presently, he’s specializing in content material creation and writing technical blogs on machine studying and knowledge science applied sciences. Abid holds a Grasp’s diploma in Expertise Administration and a bachelor’s diploma in Telecommunication Engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college students fighting psychological sickness.

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