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  1. Stackups
  2. Application & Data
  3. Databases
  4. Databases
  5. Azure Cosmos DB vs IBM DB2

Azure Cosmos DB vs IBM DB2

OverviewComparisonAlternatives

Overview

IBM DB2
IBM DB2
Stacks245
Followers254
Votes19
Azure Cosmos DB
Azure Cosmos DB
Stacks594
Followers1.1K
Votes130

Azure Cosmos DB vs IBM DB2: What are the differences?

# Key Differences between Azure Cosmos DB and IBM DB2

Azure Cosmos DB and IBM DB2 are both popular databases, but they have distinct differences that cater to different use cases. 

1. **Scalability**: One significant difference between Azure Cosmos DB and IBM DB2 is their approach to scalability. Azure Cosmos DB is designed to be horizontally scalable, meaning it can easily scale out across multiple regions and handle large volumes of data with low latency. On the other hand, IBM DB2 traditionally relies on vertical scaling, which limits its ability to scale out as seamlessly as Azure Cosmos DB.

2. **Data Model**: Another key difference lies in the data model used by Azure Cosmos DB and IBM DB2. Azure Cosmos DB is a NoSQL database that supports various data models such as document, key-value, graph, and column-family. This flexibility allows developers to choose the most suitable data model for their application. In contrast, IBM DB2 is a relational database that follows the traditional row-based data model.

3. **Availability**: When it comes to availability, Azure Cosmos DB offers high availability with guaranteed uptime backed by SLAs. Its globally distributed nature ensures that data is always available even in case of regional failures. IBM DB2, while providing high availability features, may not offer the same level of global distribution and uptime guarantees as Azure Cosmos DB.

4. **Consistency Models**: Azure Cosmos DB supports multiple consistency levels, allowing developers to choose a trade-off between consistency and latency based on their application requirements. In contrast, IBM DB2 follows traditional ACID principles and offers strong consistency by default. This difference in consistency models can influence the application design and performance characteristics.

5. **Ecosystem and Integration**: Azure Cosmos DB is tightly integrated with other Azure services, making it easy to build end-to-end solutions within the Azure ecosystem. It offers seamless integration with popular tools and frameworks used in the cloud-native environment. On the other hand, IBM DB2, being an on-premise relational database, may not have the same level of integration with cloud services or modern development frameworks.

6. **Cost Model**: The pricing models of Azure Cosmos DB and IBM DB2 also differ significantly. Azure Cosmos DB follows a consumption-based pricing model where customers pay for the actual usage of resources. In contrast, IBM DB2 typically follows a traditional licensing model, which may involve upfront costs and additional fees based on the number of users or servers.

In Summary, Azure Cosmos DB and IBM DB2 differ in terms of scalability, data model, availability, consistency models, ecosystem integration, and cost model, catering to different application requirements and use cases.

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Detailed Comparison

IBM DB2
IBM DB2
Azure Cosmos DB
Azure Cosmos DB

DB2 for Linux, UNIX, and Windows is optimized to deliver industry-leading performance across multiple workloads, while lowering administration, storage, development, and server costs.

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

-
Fully managed with 99.99% Availability SLA;Elastically and highly scalable (both throughput and storage);Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes;Globally distributed with multi-region replication;Rich SQL queries over schema-agnostic automatic indexing;JavaScript language integrated multi-record ACID transactions with snapshot isolation;Well-defined tunable consistency models: Strong, Bounded Staleness, Session, and Eventual
Statistics
Stacks
245
Stacks
594
Followers
254
Followers
1.1K
Votes
19
Votes
130
Pros & Cons
Pros
  • 7
    Rock solid and very scalable
  • 5
    BLU Analytics is amazingly fast
  • 2
    Easy
  • 2
    Secure by default
  • 2
    Native XML support
Pros
  • 28
    Best-of-breed NoSQL features
  • 22
    High scalability
  • 15
    Globally distributed
  • 14
    Automatic indexing over flexible json data model
  • 10
    Always on with 99.99% availability sla
Cons
  • 18
    Pricing
  • 4
    Poor No SQL query support
Integrations
Node.js
Node.js
JavaScript
JavaScript
PHP
PHP
Ruby
Ruby
Java
Java
Python
Python
C#
C#
.NET
.NET
C++
C++
Perl
Perl
Azure Machine Learning
Azure Machine Learning
MongoDB
MongoDB
Hadoop
Hadoop
Java
Java
Azure Functions
Azure Functions
Azure Container Service
Azure Container Service
Azure Storage
Azure Storage
Azure Websites
Azure Websites
Apache Spark
Apache Spark
Python
Python

What are some alternatives to IBM DB2, Azure Cosmos DB?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

Amazon DynamoDB

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

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