Introduction

Database software plays a crucial role in modern businesses, enabling them to manage vast amounts of structured or unstructured data. These systems are essential for everything from customer relationship management to inventory tracking. There are two primary categories of database systems: Relational Database Management Systems (RDBMS) and NoSQL databases.

RDBMS systems are designed to store data in a structured format with predefined schemas and use SQL (Structured Query Language) for querying and managing the data. Examples include MySQL, PostgreSQL, and Oracle. On the other hand, NoSQL databases are more flexible and are built to handle unstructured or semi-structured data, often used in big data, real-time applications, and scalable web applications. Examples include MongoDB, Cassandra, and Couchbase.

In 2026, the database landscape is evolving rapidly, driven by the increasing need for scalable, efficient, and flexible solutions. Whether you’re managing customer data, performing data analytics, or building a complex application, choosing the right database software can have a significant impact on your project’s success.

What to Look for When Choosing Database Software:

• Scalability: Ability to handle increased data loads as your business grows.
• Performance: Fast read/write operations and low latency.
• Data Integrity: Ensuring data consistency and reliability.
• Ease of Use: User-friendly interface, tools, and documentation.
• Support for Queries: Complex querying capabilities for RDBMS or flexibility for NoSQL.
• Cost: Whether the solution fits within your budget.
• Security: Strong encryption, backup, and recovery options.

Top 10 Database Software (RDBMS, NoSQL) Tools in 2026

1. MySQL (RDBMS)

• Short Description: MySQL is a widely used open-source RDBMS. It’s known for its reliability, flexibility, and strong community support.
• Key Features:
  • ACID-compliant for transactional integrity.
  • High-performance storage engines like InnoDB.
  • Replication support for high availability.
  • Strong security features including SSL and data encryption.
  • Cross-platform support (Windows, Linux, macOS).
• Pros:
  • Easy to use and well-documented.
  • Excellent performance and scalability.
  • Strong community and enterprise support.
• Cons:
  • Limited support for advanced analytics.
  • Performance can degrade with complex queries.

2. PostgreSQL (RDBMS)

• Short Description: PostgreSQL is an advanced, open-source object-relational database system. It’s highly extensible and suitable for complex data models.
• Key Features:
  • Support for advanced data types (JSON, XML).
  • Full ACID compliance.
  • Strong support for custom functions and extensions.
  • High concurrency and multiversion concurrency control (MVCC).
  • Full-text search capabilities.
• Pros:
  • Highly customizable and extensible.
  • Strong support for geospatial data (PostGIS).
  • Excellent community-driven development.
• Cons:
  • Steeper learning curve compared to MySQL.
  • Slightly slower for read-heavy operations.

3. Oracle Database (RDBMS)

• Short Description: Oracle Database is a powerful and enterprise-grade RDBMS used in large-scale environments. Known for its scalability and robustness.
• Key Features:
  • In-memory database capabilities.
  • Comprehensive data security with advanced encryption.
  • Real-time analytics support.
  • Autonomous database with machine learning capabilities.
  • Highly optimized for high transaction volumes.
• Pros:
  • Strong performance and scalability.
  • Full enterprise-grade support and services.
  • Advanced security and disaster recovery features.
• Cons:
  • Expensive licensing.
  • Complex setup and management.

4. MongoDB (NoSQL)

• Short Description: MongoDB is a widely used NoSQL database that stores data in a flexible, JSON-like format. It’s popular for web applications and big data.
• Key Features:
  • Document-based data storage with flexible schema.
  • Horizontal scalability with sharding.
  • Aggregation framework for real-time analytics.
  • Rich secondary index support.
  • Strong community and cloud integration.
• Pros:
  • Flexible schema for unstructured data.
  • Good performance with large datasets.
  • Active development and support.
• Cons:
  • Not suitable for complex transactional operations.
  • Limited ACID compliance, though it has improved in recent versions.

5. Microsoft SQL Server (RDBMS)

• Short Description: Microsoft SQL Server is a relational database management system developed by Microsoft, known for its strong integration with other Microsoft tools.
• Key Features:
  • Built-in business intelligence tools.
  • ACID compliance and strong transactional support.
  • Advanced security features with encryption and auditing.
  • Robust backup and disaster recovery options.
  • Excellent integration with Azure and other Microsoft services.
• Pros:
  • Excellent integration with Microsoft products.
  • High availability and scalability options.
  • Enterprise-grade support and tools.
• Cons:
  • Expensive licensing.
  • Not as flexible as open-source solutions.

6. Cassandra (NoSQL)

• Short Description: Apache Cassandra is a distributed NoSQL database optimized for handling large amounts of data across many commodity servers without any single point of failure.
• Key Features:
  • Distributed, decentralized architecture.
  • Horizontal scalability for large data workloads.
  • Strong consistency and availability guarantees.
  • High write throughput.
  • Built-in replication for fault tolerance.
• Pros:
  • Excellent for write-heavy applications.
  • High scalability with minimal latency.
  • No single point of failure.
• Cons:
  • Complex setup and management.
  • Lack of support for complex queries and joins.

7. Redis (NoSQL)

• Short Description: Redis is an in-memory key-value store used primarily as a cache and message broker. It’s highly performant and suitable for real-time applications.
• Key Features:
  • In-memory data structure store.
  • Support for data persistence and replication.
  • Pub/Sub messaging system.
  • Advanced data structures like sets, sorted sets, and hashes.
  • Lua scripting support.
• Pros:
  • Extremely fast for read and write operations.
  • Excellent for caching and real-time processing.
  • Simple API and easy to integrate.
• Cons:
  • Primarily an in-memory database, which can be limiting for large datasets.
  • Limited query capabilities compared to other databases.

8. Couchbase (NoSQL)

• Short Description: Couchbase is a distributed NoSQL database that provides high scalability and performance for mission-critical applications.
• Key Features:
  • Multi-model database supporting document, key-value, and SQL++ queries.
  • Built-in full-text search and analytics.
  • Horizontal scaling with automatic data sharding.
  • Cross-datacenter replication for high availability.
  • Strong security features, including encryption.
• Pros:
  • Highly scalable and flexible.
  • Advanced search and analytics features.
  • Strong support for real-time applications.
• Cons:
  • Complexity in initial setup and configuration.
  • Limited support for multi-table joins.

9. MariaDB (RDBMS)

• Short Description: MariaDB is an open-source relational database management system that is a fork of MySQL. It’s designed to be fast, scalable, and fully compatible with MySQL.
• Key Features:
  • ACID compliance for transactional integrity.
  • High-performance storage engines like Aria and InnoDB.
  • Replication and clustering for high availability.
  • Support for JSON data types.
  • Strong community and developer support.
• Pros:
  • Fully compatible with MySQL.
  • Open-source and free to use.
  • Strong performance and scalability.
• Cons:
  • Slightly fewer features compared to MySQL in terms of enterprise-level support.
  • Can struggle with very large data sets.

10. Neo4j (NoSQL)

• Short Description: Neo4j is a graph database used for handling connected data with relationships as the primary focus.
• Key Features:
  • Support for graph data modeling and Cypher query language.
  • High performance for graph traversal and pattern matching.
  • Built-in data visualization tools.
  • ACID-compliant for transactional integrity.
  • Scalable for large datasets with complex relationships.
• Pros:
  • Excellent for handling complex, connected data.
  • Fast queries for graph traversal.
  • Rich visualization tools for graph data.
• Cons:
  • Limited support for traditional relational data models.
  • Requires specialized knowledge to fully utilize.

Comparison Table

Which Database Software (RDBMS, NoSQL) Tool is Right for You?

Decision-Making Guide:

• Small Businesses: MySQL or MariaDB for their simplicity and low cost.
• Enterprises: PostgreSQL or Oracle for complex, mission-critical applications.
• Web Developers: MongoDB for flexible schema management or Redis for caching.
• Big Data Applications: Cassandra for large-scale data or Couchbase for multi-model support.
• Real-Time Applications: Redis for speed or Neo4j for graph-related tasks.

Conclusion

Choosing the right database software is a crucial decision for any business or application in 2026. Whether you need the structure and integrity of RDBMS or the flexibility and scalability of NoSQL, there are plenty of excellent options available. Testing out a few demos or free trials can help you make an informed choice that aligns with your project requirements.

FAQs

1. What’s the main difference between RDBMS and NoSQL databases?

RDBMS databases store data in structured tables with predefined schemas, while NoSQL databases offer flexible schema designs, ideal for unstructured or semi-structured data.

2. Which database is best for high-performance applications?

For high-performance applications, databases like Redis (in-memory) and Cassandra (distributed) are often the best choices.

3. Can I switch from MySQL to PostgreSQL?

Yes, migrating from MySQL to PostgreSQL is possible and supported by many tools. However, you need to account for differences in SQL syntax and features.

4. Which database is best for cloud applications?

MongoDB and Couchbase are excellent choices for cloud applications due to their scalability and flexibility in handling large amounts of data.

5. Is it necessary to use both RDBMS and NoSQL databases?

Not necessarily. It depends on the application. For highly structured data, an RDBMS is usually better, while NoSQL is ideal for handling unstructured or complex data models.

What is MongoDB Query?

MongoDB queries are used to interact with data stored in MongoDB, a NoSQL database that stores information in a document-oriented format. MongoDB uses BSON (Binary JSON) to represent documents, which are flexible, scalable, and can accommodate a variety of data types, including structured, semi-structured, and unstructured data. Queries in MongoDB are written using the MongoDB Query Language (MQL), which allows developers to retrieve, update, insert, and delete documents within collections.

Unlike SQL databases where data is structured into tables, MongoDB allows for schema-less data storage, meaning that each document can have different fields, providing greater flexibility for developers to manage dynamic data models.

MongoDB queries can be executed through various MongoDB drivers available for programming languages like JavaScript, Python, Java, Ruby, and PHP. MongoDB also provides several administrative tools (such as MongoDB Compass and Mongo shell) to interact with the database directly.

Key Types of MongoDB Queries:

1. Find: Retrieves documents that match specific criteria from a collection.
2. Insert: Adds new documents into a collection.
3. Update: Modifies existing documents within a collection.
4. Delete: Removes documents from a collection.
5. Aggregation: Performs advanced data processing tasks, such as grouping, sorting, and calculating values.

What Are the Major Use Cases of MongoDB Queries?

MongoDB queries are extremely versatile and can be used in a wide variety of applications, especially when dealing with unstructured or semi-structured data. Below are major use cases where MongoDB queries shine:

1. Real-Time Data Processing and Analytics:

• Use Case: MongoDB queries are used in real-time data processing applications where large volumes of data are ingested and analyzed in near real-time.
• Example: A social media analytics platform that analyzes posts, comments, and interactions as they happen.
• Why MongoDB Queries? MongoDB’s flexibility in storing large volumes of data with varying structures and its ability to scale horizontally across multiple machines make it ideal for real-time processing.

2. Content Management Systems (CMS):

• Use Case: MongoDB queries are commonly used in content management systems (CMS) for dynamic content storage, where the structure of each content type can vary.
• Example: A news website that stores articles, images, and videos, where articles and multimedia content are stored in flexible document formats.
• Why MongoDB Queries? MongoDB’s schema-less nature allows it to efficiently manage and retrieve multi-format content without the constraints of rigid table schemas found in relational databases.

3. E-commerce and Product Catalogs:

• Use Case: In e-commerce applications, MongoDB queries help retrieve product data from large catalogs, manage inventory, and filter products based on user preferences.
• Example: An e-commerce platform that allows users to search products based on multiple criteria such as price range, color, brand, and availability.
• Why MongoDB Queries? The flexibility of MongoDB allows for storing diverse product data with varying attributes (such as specifications, prices, images) without having to modify the database schema as product categories change over time.

4. Mobile and IoT Applications:

• Use Case: MongoDB is widely used for applications that deal with Internet of Things (IoT) or mobile applications, where the incoming data may be semi-structured or unstructured.
• Example: A smart home system that collects data from sensors (such as temperature, humidity, motion detection), and MongoDB stores these records with different attributes.
• Why MongoDB Queries? MongoDB can handle high volumes of event-driven data and is well-suited for storing the kind of semi-structured data produced by IoT devices.

5. Data Warehousing and Analytics:

• Use Case: MongoDB queries are used in data warehousing applications for reporting, querying, and aggregating data from various sources to create insights.
• Example: A business intelligence tool that aggregates sales data, processes it, and provides reports for decision-making.
• Why MongoDB Queries? MongoDB’s powerful aggregation framework allows for real-time data transformation and processing on massive datasets, without relying on the rigid structure of traditional relational databases.

6. User-Generated Content (UGC) and Social Media:

• Use Case: MongoDB queries are well-suited for applications that handle user-generated content (UGC), such as social media platforms, review systems, or community forums.
• Example: A video-sharing platform that stores user-submitted videos, comments, likes, and shares. MongoDB allows the platform to handle and query millions of user interactions efficiently.
• Why MongoDB Queries? MongoDB’s scalability and ability to handle large-scale content with varying structures make it perfect for UGC-heavy applications.

How MongoDB Queries Work Along with Architecture?

MongoDB operates with a document-based architecture, meaning data is stored in documents (similar to JSON objects) within collections (akin to SQL tables). Here’s how MongoDB queries fit into the overall MongoDB architecture:

1. MongoDB Architecture Overview:

• Document-Oriented: MongoDB stores data as documents using BSON (Binary JSON), which allows flexible schema design. Each document can contain different fields and types of data.
• Collection: Documents are stored in collections, which act like tables in relational databases but can hold documents with different structures.
• Database: MongoDB instances contain multiple databases, and each database can have multiple collections.

2. Query Execution Flow:

• When a MongoDB query is issued, the query engine searches through the collections and retrieves documents that match the specified query criteria.
• Query Example: To find all users aged 30 and above:

db.users.find({ age: { $gte: 30 } });

• The query engine checks the collection and returns the matching documents based on the conditions defined in the query.

3. Indexing:

• Indexes play a key role in speeding up query execution. MongoDB allows indexing on fields in documents, making data retrieval faster and more efficient.
• Example: If frequently querying the “age” field, you can create an index on this field:

db.users.createIndex({ age: 1 });

• MongoDB then uses this index to optimize the query and return results faster, especially when dealing with large collections.

4. Aggregation Framework:

• For more advanced data manipulation, MongoDB’s aggregation framework enables complex queries such as grouping, filtering, sorting, and transforming data.
• Aggregation Pipeline Example:

db.sales.aggregate([
  { $match: { status: "completed" } },
  { $group: { _id: "$productId", totalSales: { $sum: "$amount" } } }
]);

• This example groups sales by product ID and calculates the total sales for each product.

5. Horizontal Scaling and Sharding:

• MongoDB is designed to scale horizontally by distributing data across multiple servers using sharding. Sharding ensures that large datasets are distributed across different machines, allowing MongoDB to handle massive volumes of data while maintaining performance.
• Query Example in Sharded Cluster: Queries are automatically routed to the appropriate shard based on the shard key.

What Are the Basic Workflow of MongoDB Query?

The basic workflow of MongoDB queries consists of defining the query, executing it, and retrieving the results. Below is the breakdown of the typical query workflow:

1. Define the Query Criteria:

• The first step in performing a query is to define the query criteria. This criteria determines which documents you want to retrieve based on certain conditions.
• Example: Retrieve all documents where the age field is greater than 25:

db.users.find({ age: { $gt: 25 } });

2. Specify the Projection (Optional):

• You can specify which fields of the documents to return using projection. This helps limit the data returned and improve performance.
• Example: Return only the name and email fields:

db.users.find({ age: { $gt: 25 } }, { name: 1, email: 1 });

3. Apply Sorting (Optional):

• You can apply sorting to the query results to order the documents based on one or more fields.
• Example: Sort users by age in descending order:

db.users.find({ age: { $gt: 25 } }).sort({ age: -1 });

4. Execute the Query:

• Once you have defined the query, projection, and sort conditions, execute the query to retrieve the matching documents from the collection.
• MongoDB will use its query engine to match documents and return them.

5. Aggregation (Optional):

• If the query requires more advanced operations like grouping, counting, or summarizing data, use the aggregation framework to process the data.
• Example: Group users by age and count how many users are in each age group:

db.users.aggregate([
  { $group: { _id: "$age", count: { $sum: 1 } } }
]);

6. Indexing (Optional for Optimization):

• To speed up query execution, create indexes on the fields that are frequently queried. This ensures faster data retrieval from large collections.

Step-by-Step Getting Started Guide for MongoDB Query

Here’s a step-by-step guide to help you get started with MongoDB queries:

Step 1: Install MongoDB and Set Up Your Database

• Install MongoDB locally or use MongoDB Atlas (cloud-based MongoDB). Set up your MongoDB instance and create a new database.

Step 2: Connect to MongoDB

• Use the Mongo shell or a MongoDB client (like Robo 3T or MongoDB Compass) to connect to your MongoDB database.
• Example (Mongo shell):

mongo

Step 3: Write Basic Queries

• Use the find(), insert(), update(), and delete() commands to interact with the data. For example:

db.users.find({ name: "John" });

Step 4: Implement Indexing for Faster Queries

• Create indexes on commonly queried fields to speed up the query performance. d

db.users.createIndex({ name: 1 });

Step 5: Experiment with Aggregation

• Learn how to use the aggregation framework for more complex queries.

db.users.aggregate([
  { $match: { age: { $gte: 30 } } },
  { $group: { _id: "$age", count: { $sum: 1 } } }
]);

Step 6: Optimize Queries

• Ensure that your queries are optimized, especially for large datasets, by using indexes and ensuring your queries are as efficient as possible.

MongoDB is a document-oriented database management system (DBMS) that uses JSON-like documents with dynamic schemas. This makes it a good choice for storing and querying data that is not well-suited to traditional relational databases, such as semi-structured and unstructured data. MongoDB is open-source and has a large community of users and developers. It is also scalable and can be used to store large amounts of data.

Here are some of the key features of MongoDB:

• Document-oriented: MongoDB stores data in documents, which are JSON-like objects. This makes it easy to store and query data that is not well-suited to traditional relational databases.
• Dynamic schemas: MongoDB documents do not have a fixed schema. This means that you can add new fields to documents without having to change the schema of the database.
• Scalable: MongoDB is a scalable database that can be used to store large amounts of data. It can be scaled horizontally by adding more servers, or vertically by adding more memory and CPU to a single server.
• Open-source: MongoDB is an open-source database, which means that it is free to use and modify. It also has a large community of users and developers who can provide support and help with development.

What is top use cases of MongoDB?

MongoDB is a document-oriented database that is often used for web applications, big data, and real-time applications. Here are some of the top use cases of MongoDB:

• Web applications: MongoDB can be used to store data for web applications, such as user profiles, blog posts, and product catalogs. MongoDB’s flexible schema makes it easy to store and query data that is not well-suited to traditional relational databases. For example, you can store user profiles with a variety of fields, such as name, email address, and interests. You can also store blog posts with a variety of fields, such as title, author, and content.
• Big data: MongoDB can be used to store and analyze large amounts of data, such as social media data, financial data, and sensor data. MongoDB’s scalability makes it a good choice for storing large amounts of data. For example, you can use MongoDB to store social media data from millions of users. You can also use MongoDB to store financial data from thousands of transactions.
• Real-time applications: MongoDB can be used to store and query data in real time, such as for stock trading applications and gaming applications. MongoDB’s document-oriented nature makes it easy to store and query data in real time. For example, you can use MongoDB to store stock prices in real time. You can also use MongoDB to store player data in real time for gaming applications.

What are feature of MongoDB?

MongoDB is a document-oriented database management system (DBMS) that uses JSON-like documents with dynamic schemas. This makes it a good choice for storing and querying data that is not well-suited to traditional relational databases, such as semi-structured and unstructured data.

MongoDB has a number of features that make it a powerful and versatile database:

• Document-oriented: MongoDB stores data in documents, which are JSON-like objects. This makes it easy to store and query data that is not well-suited to traditional relational databases.
• Dynamic schemas: MongoDB documents do not have a fixed schema. This means that you can add new fields to documents without having to change the schema of the database.
• Scalable: MongoDB is a scalable database that can be used to store large amounts of data. It can be scaled horizontally by adding more servers, or vertically by adding more memory and CPU to a single server.
• Open-source: MongoDB is an open-source database, which means that it is free to use and modify. It also has a large community of users and developers who can provide support and help with development.
• Flexible: MongoDB is a flexible database that can be used for a variety of applications. It is not limited to web applications, big data, and real-time applications.
• Easy to use: MongoDB is a relatively easy database to use. It has a simple API that makes it easy to query and manipulate data.
• Secure: MongoDB is a secure database that supports encryption and authentication.

What are feature of MongoDB?

MongoDB is a document-oriented database management system (DBMS) that uses JSON-like documents with dynamic schemas. This makes it a good choice for storing and querying data that is not well-suited to traditional relational databases, such as semi-structured and unstructured data.

MongoDB has a number of features that make it a powerful and versatile database:

• Document-oriented: MongoDB stores data in documents, which are JSON-like objects. This makes it easy to store and query data that is not well-suited to traditional relational databases.
• Dynamic schemas: MongoDB documents do not have a fixed schema. This means that you can add new fields to documents without having to change the schema of the database.
• Scalable: MongoDB is a scalable database that can be used to store large amounts of data. It can be scaled horizontally by adding more servers, or vertically by adding more memory and CPU to a single server.
• Open-source: MongoDB is an open-source database, which means that it is free to use and modify. It also has a large community of users and developers who can provide support and help with development.
• Flexible: MongoDB is a flexible database that can be used for a variety of applications. It is not limited to web applications, big data, and real-time applications.
• Easy to use: MongoDB is a relatively easy database to use. It has a simple API that makes it easy to query and manipulate data.
• Secure: MongoDB is a secure database that supports encryption and authentication.

What is the workflow of MongoDB?

The workflow of MongoDB is as follows:

• Create a MongoDB database. A MongoDB database is a collection of documents.
• Create a collection in the database. A collection is a group of documents that share the same schema.
• Insert documents into the collection. Documents are the basic unit of data in MongoDB.
• Query the documents in the collection. You can query documents in MongoDB using the MongoDB query language.
• Update the documents in the collection. You can update documents in MongoDB using the MongoDB update language.
• Delete documents from the collection. You can delete documents from MongoDB using the MongoDB delete language.

How MongoDB Works & Architecture?

MongoDB is a document-oriented database management system (DBMS) that uses JSON-like documents with dynamic schemas. This makes it a good choice for storing and querying data that is not well-suited to traditional relational databases, such as semi-structured and unstructured data. MongoDB works by storing data in collections of documents. Each document is a JSON-like object that can contain a variety of fields. The fields in a document can be of different types, such as strings, numbers, and arrays.

MongoDB uses a distributed architecture to store data. This means that data is stored on multiple servers, which makes it scalable and fault-tolerant. MongoDB also uses replication to ensure that data is always available. MongoDB’s document-oriented architecture makes it easy to store and query data that is not well-suited to traditional relational databases. For example, you can store user profiles with a variety of fields, such as name, email address, and interests. You can also store blog posts with a variety of fields, such as title, author, and content.

MongoDB’s distributed architecture makes it scalable and fault-tolerant. This means that MongoDB can be used to store large amounts of data and it can continue to operate even if some of the servers go down. MongoDB’s replication feature ensures that data is always available. This means that you can be sure that your data is safe even if there is a problem with one of the servers.

MongoDB is a good choice for a variety of applications, including:

• Web applications: MongoDB can be used to store data for web applications, such as user profiles, blog posts, and product catalogs.
• Big data: MongoDB can be used to store and analyze large amounts of data, such as social media data, financial data, and sensor data.
• Real-time applications: MongoDB can be used to store and query data in real-time, such as for stock trading applications and gaming applications.

How to Install and Configure MongoDB?

To install and configure MongoDB, you can follow these steps:

• Download the MongoDB installer from the MongoDB website.
• Install the MongoDB installer on your computer.
• Start the MongoDB server.
• Create a MongoDB database.
• Create a MongoDB collection.
• Insert documents into the collection.
• Query the documents in the collection.
• Update the documents in the collection.
• Delete documents from the collection.

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Computer Science, Engineering, Physics, Mathematics or Equivalent Degree / Bachelor of Science
Data application development and deployment
Design and implement infrastructure tools, and work with horizontal frameworks and libraries
Creating a data ingestion pipeline between a legacy data warehouse and a big data stack
Application backend workflow automation
Build and maintain back-end services created by multiple service frameworks
Maintain and enhance applications backed by big data computing applications
Learn new approaches and technologies enthusiastically
Working on big data platforms (Vanilla Hadoop, Cloudera, Hortonworks)
Experience with Scala or other functional languages ​​(Haskell, Clojure, Kotlin, Clean)
Some experience with Apache Hadoop, Spark, Hive, Pig, Oozie, ZooKeeper, MongoDB, CouchbaseDB, Impala, Kudu, Linux, Bash, version control tools, continuous integration tools
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Kudos to the knowledge know-how neighborhood over the previous 12 months, who helped preserve the connectivity and communications that held organizations collectively throughout the nice company dispersal. Not solely have been many organizations capable of keep on and even thrive, however the productiveness of IT departments themselves remained robust.

The productiveness and steady supply of companies by IT departments by way of the Covid disaster is due in no small half to DevOps — the aligning and syncing of developer initiative with operations cadence. The occasions of the previous 12 months have solely made DevOps extra related.

That is the phrase popping out of a survey of three,200 enterprises launched by RedGate Software program, which performs within the information house, documenting modifications in IT as a direct results of the Covid disaster. Almost three quarters of organizations, 74%, have now adopted DevOps in some type, cross-platform database use has risen markedly. “IT pondering has moved additional, sooner, and maybe extra completely than any earlier 12 months,” the survey’s authors report.

Seventy-four % of organizations within the report are actually taking a DevOps method to improvement, in comparison with 47% when the report was first revealed 5 years in the past. As well as, the survey’s authors report, a transparent correlation between DevOps adoption and software program supply efficiency has emerged, with excessive performers capable of launch each software and database modifications sooner, extra incessantly, and with fewer errors.

The good company dispersal that passed off final 12 months didn’t make a dent in IT productiveness, the survey finds. IT groups have remained productive whereas working remotely. Forty-three % state their organizations have grown above or in step with expectations, 13% have seen neither development nor a decline. Solely 9 % have seen a fall. By way of productiveness and efficiency. A majority, 80%, agree that distant working will stay in the long run, with 63% agreeing it has elevated their productiveness.

The highest obstacles to an efficient DevOps effort embody an absence of applicable expertise within the crew, disruption to present workflows/enterprise, and an absence of alignment between improvement and operations groups.

In relation to bettering software program supply and operational efficiency, metrics corresponding to deployment frequency, lead time for modifications, time to revive service, and alter failure charges, there are two major challenges, the survey exhibits. The survey exhibits an absence of automation in launch processes (41%) and disadvantages with legacy software code (39%) are the main inhibitors to delivering on efficiency. “These two components seem to go hand in hand, with the introduction of automation to established, advanced environments being harder,” the survey’s authors level out. “General, the challenges chosen by greater than a 3rd of respondents are round technical obstacles and useful resource constraints.”

Curiously, lack of finances shouldn’t be seen as a extensively unfold problem to bettering software program supply, with just one in 5 respondents deciding on it, the survey exhibits. Cultural resistance was additionally reported by solely 15% of respondents.

The survey additionally delved right into a associated development, database DevOps adoption. “Utility builders can iterate and ship options at a a lot sooner price,” they state. “Nonetheless, the enterprise can not understand that worth until database modifications are deployed on the identical price, which implies Database DevOps practices must be adopted to allow fast enterprise worth supply.”

Fueling this requirement is the rising variety of knowledge environments. Nobody makes use of simply Oracle or SQL Server and and nothing else. There are open-source and web-friendly databases corresponding to MySQL, PostgreSQL, MongoDB, MariaDB and CouchBase, to call just a few. Greater than 47% of the survey respondents indicated that they’ve NoSQL databases of their enterprise, the survey confirms. Solely 30% of organizations are actually utilizing one database platform in comparison with 38% only one 12 months in the past, the survey exhibits. Twenty-six % have two databases, and practically half of respondents use three or extra databases.

Next-generation database developer MongoDB and Google Cloud are extending the strategic alliance they launched two years ago with a new partnership deal under which the MongoDB Atlas cloud database will be integrated with a slew of Google Cloud services.

The move is expected to provide systems integrators, ISVs and other solution provider partners of the two companies with new opportunities in cloud application development and cloud data migration.

Google Cloud and MongoDB have undertaken the joint engineering task of integrating MongoDB Atlas with a number of Google applications and services that run on the Google Cloud Platform including BigQuery data warehouse, Pub/Sub publish and subscribe messaging, Dataflow data processing, Dataproc managed Hadoop and Spark, TensorFlow machine learning, Cloud Run serverless computing, Cloud Functions serverless execution, App Engine platform-as-a-service, and the Google Kubernetes Engine container environment.

The new pre-built integrations and reference architectures being developed by MongoDB and Google Cloud will make it easier for an ISV partner or an in-house developer to build applications that tie into both MongoDB Atlas and all those Google Cloud services, said Alan Chhabra, MongoDB senior vice president of worldwide partners.

“A developer, who is the person who uses MongoDB the most, potentially can now get a full-stack architecture to develop applications centered around MongoDB Atlas and Google Cloud services,” said Chhabra in an interview with CRN.

The joint development initiative “really solidifies the amount of pre-built integration work between the two companies’ platforms,” said Chhabra.

In February 2020 Google bought Cornerstone Technology and its tools for migrating mainframe workloads to the cloud. Those mainframe modernization tools now support MongoDB Atlas, the companies said Thursday, making it easier to convert legacy Cobol code on mainframes into Java-based applications that can run on MongoDB.

Those capabilities will assist global and regional systems integration partners that MongoDB and Google Cloud work with for projects to modernize legacy mainframe applications and move them to Google Cloud, said Chhabra.

Shortly after taking the job of Google Cloud CEO, Thomas Kurian said the company would work more closely with developers of open-source software. In 2019 MongoDB took Google Cloud up on that promise, striking a go-to-market partnership through which MongoDB Atlas was offered through the Google Cloud Platform Marketplace.

And in 2020 MongoDB Atlas became a fully managed service directly integrated with the Google Console, providing integrated billing and support for the companies’ joint customers.

This week Chhabra said that in the last year MongoDB and Google Cloud have seen a 300 percent year-over-year increase in joint customer wins, particularly in such vertical industries as financial services and retail.

Altogether the database vendor has some 22,600 customers.

MongoDB runs on all three of the hyper-scale cloud platforms, including Amazon Web Services and Microsoft Azure. (AWS offers Amazon DocumentDB, its own database software that some observers see as competing with MongoDB.)

In 2019 MongoDB allied with Alibaba to bring its cloud database service to China.

“We are delighted to expand our partnership with MongoDB to deliver Atlas at global scale on Google Cloud and help businesses leverage data in their digital transformations,” said Kevin Ichhpurani, corporate vice president, global ecosystem at Google Cloud, in a statement. “This announcement builds on our shared commitment to helping businesses build a foundation for data-driven transformation. We are excited to build on significant customer successes with MongoDB on Google Cloud.”

MongoDB Open Sources Compass Under the SSPL

At its London developer event in September 2019, MongoDB co-founder and CTO Eliot Horowitz announced that the company was open sourcing its Compass graphical user interface, releasing the code to GitHub under the Server Side Public License (SSPL).

Compass is a GUI tool that lets users visually explore, query, and manipulate data stored in MongoDB. It has shipped alongside MongoDB since version 3.2, but had previously been available only as a proprietary, closed-source application. Making the source code available on GitHub represents a notable shift in how MongoDB positions the tool — particularly as the company looks to expand its identity from a database vendor into a broader data platform offering more analytics capabilities out of the box.

What Is the SSPL and Why Does It Matter?

The Server Side Public License was introduced by MongoDB in 2018 as a modification of the GNU Affero General Public License (AGPL). Its primary intent is to close what MongoDB perceived as a loophole: cloud providers taking open source database software, wrapping it in a managed service, and offering it commercially — without contributing back to the project or compensating the original developer.

Under the SSPL, any organisation that offers the licensed software as a service must also open source the entire stack used to run that service — including orchestration, monitoring, and deployment tooling. This requirement is significantly broader than traditional copyleft licenses, which is precisely why the SSPL proved controversial.

The Open Source Initiative (OSI) declined to approve the SSPL as an open source license, arguing it places restrictions on the use of the software beyond the bounds of the Open Source Definition. Critics echoed this view, saying the license blurs the line between open source and source-available software.

Consequences of the SSPL Adoption

The backlash to MongoDB’s original SSPL adoption in 2018 had concrete consequences:

• MongoDB was removed from Red Hat’s Satellite platform.
• MongoDB support was dropped from Red Hat Enterprise Linux (RHEL) 8.0.
• Several major Linux distributions removed MongoDB packages from their official repositories, citing license incompatibility.

Despite this fallout, MongoDB chose to release Compass under the same license. Sahir Azam, MongoDB’s SVP for cloud products and GTM, told DevClass that customer reaction to the SSPL had been “not as much as might be expected.” He said most partners simply wanted to clarify whether the license affected them at all.

MongoDB’s Open Source Position

The decision to open source Compass — even under the SSPL — signals MongoDB’s intent to continue expanding its open source footprint, albeit on its own licensing terms. Azam stated: “We still fully believe in the principles of free and open software. We open sourced Compass — you’ll see more of that from us.”

Whether the SSPL qualifies as genuinely “open source” remains contested, but for engineers consuming the software internally or contributing to the project, the practical effect is that the Compass source code is now publicly available and accepts community contributions via GitHub.

Other Announcements: Terraform, Kubernetes, and Atlas

The Compass release was not the only news from the London event. Several other updates were announced:

• Terraform and Kubernetes integrations reached general availability, making it easier to provision and manage MongoDB Atlas clusters through infrastructure-as-code workflows and container orchestration platforms.
• Atlas Data Lake — then still in beta — was available exclusively on AWS at the time of the announcement. Azure and Google Cloud versions were described as being in development, with availability expected “probably some time next year” (i.e., 2020).
• Atlas revenue: Azam stated that MongoDB Atlas, the company’s hosted cloud database service, accounted for approximately 37 percent of total revenues at the time, split among AWS, Azure, and Google Cloud Platform roughly in proportion to their respective overall market shares.
• Google Cloud partnership: Azam noted that MongoDB’s relationship with Google was “picking up a bit faster,” with Google reportedly moving more quickly to establish deals with open source companies.

MongoDB and AWS: Competing and Cooperating

A notable tension exists in the MongoDB–AWS relationship. The SSPL was widely interpreted as a direct response to Amazon Web Services offering Amazon DocumentDB — a MongoDB-compatible managed database service — without contributing to the upstream project. Yet, despite this friction, MongoDB and AWS continued to collaborate on other initiatives, including Atlas being available on AWS infrastructure and Atlas Data Lake launching first on AWS.

This dynamic reflects a pattern seen across the cloud-native software industry: upstream open source vendors and hyperscale cloud providers simultaneously competing and cooperating, often project by project.

Key Takeaways for DevOps Engineers

• The MongoDB Compass source code is available on GitHub — you can build it, contribute to it, or adapt it, subject to SSPL terms.
• If your organisation intends to offer Compass or MongoDB as a managed service, review the SSPL carefully with legal counsel — the obligations are broader than AGPL.
• Internal use of MongoDB and Compass within an organisation is generally unaffected by the SSPL.
• The Terraform provider and Kubernetes Operator for MongoDB Atlas reaching GA in 2019 made Atlas a more viable option for teams with GitOps-style infrastructure workflows.

NEW YORK, Sept. 3, 2019 /PRNewswire/ — MongoDB Inc. (Nasdaq: MDB), the leading modern, general purpose database platform, today announced an expanded relationship with Microsoft. MongoDB Atlas will be available on Microsoft Azure Marketplace. MongoDB will be part of Microsoft’s strategic Partner Reported ACR co-sell program. 

MongoDB Atlas launched in the summer of 2016 as a fully-managed global cloud database service. It is now available across 26 Azure regions providing service to thousands of customers that depend on MongoDB Atlas to drive their business. Atlas on Azure tightly integrates with popular Azure services such as PowerBI, Azure Databricks and Sitecore on Azure.

The new availability of MongoDB Atlas on Azure Marketplace will make it easier for established Azure customers to purchase and use MongoDB Atlas. The cost of MongoDB Atlas will be integrated into a customer’s Azure bill, resulting in a single, simple payment.

“Microsoft has been a leader in making it easier for customers to consume and pay for cloud services, which are driving transformative innovations across many organizations,” said Dev Ittycheria, President and CEO, MongoDB. “We are excited about the latest step in our strategic go-to-market partnership with Microsoft which will help bring MongoDB Atlas to the growing ecosystem of Azure Marketplace customers.”

Scott Guthrie, Executive Vice President of Cloud and AI, Microsoft said, “Since launching on Azure in 2017, MongoDB Atlas has been a popular service running on Azure. Today’s announcement will make it even easier for customers to consume Atlas on Azure through the Azure Marketplace. We are committed to working alongside partners like MongoDB to give our joint customers best of breed choice in technology that meets their unique business demands.”

MongoDB Atlas is now generally available in the Azure Marketplace.

Resources:

• Get started today with MongoDB Atlas on Azure free tier

About MongoDB

MongoDB is the leading modern, general purpose database platform, designed to unleash the power of software and data for developers and the applications they build. Headquartered in New York, MongoDB has more than 14,200 customers in over 100 countries. The MongoDB database platform has been downloaded over 70 million times and there have been more than one million MongoDB University registrations.

Forward-Looking Statements

This press release includes certain “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, or the Securities Act, and Section 21E of the Securities Exchange Act of 1934, as amended, including statements concerning the anticipated benefits of our expanded partnership with Microsoft. These forward-looking statements include, but are not limited to, plans, objectives, expectations and intentions and other statements contained in this press release that are not historical facts and statements identified by words such as “anticipate,” “believe,” “continue,” “could,” “estimate,” “expect,” “intend,” “may,” “plan,” “project,” “will,” “would” or the negative or plural of these words or similar expressions or variations. These forward-looking statements reflect our current views about our plans, intentions, expectations, strategies and prospects, which are based on the information currently available to us and on assumptions we have made. Although we believe that our plans, intentions, expectations, strategies and prospects as reflected in or suggested by those forward-looking statements are reasonable, we can give no assurance that the plans, intentions, expectations or strategies will be attained or achieved. Furthermore, actual results may differ materially from those described in the forward-looking statements and are subject to a variety of assumptions, uncertainties, risks and factors that are beyond our control including, without limitation: our limited operating history; our history of losses; failure of our database platform to satisfy customer demands; the effects of increased competition; our investments in new products and our ability to introduce new features, services or enhancements; our ability to effectively expand our sales and marketing organization; our ability to continue to build and maintain credibility with the developer community; our ability to add new customers or increase sales to our existing customers; our ability to maintain, protect, enforce and enhance our intellectual property; the growth and expansion of the market for database products and our ability to penetrate that market; our ability to maintain the security of our software and adequately address privacy concerns; our ability to manage our growth effectively and successfully recruit and retain highly-qualified personnel; the price volatility of our common stock; and those risks detailed from time-to-time under the caption “Risk Factors” and elsewhere in our Securities and Exchange Commission filings and reports, including our Annual Report on Form 10-K filed on April 1, 2019 and our Quarterly Report on Form 10-Q filed on June 7, 2019, as well as future filings and reports by us. Except as required by law, we undertake no duty or obligation to update any forward-looking statements contained in this release as a result of new information, future events, changes in expectations or otherwise.