Software Alternatives, Accelerators & Startups

InfluxData VS Azure Cosmos DB

Compare InfluxData VS Azure Cosmos DB and see what are their differences

InfluxData logo InfluxData

Scalable datastore for metrics, events, and real-time analytics.

Azure Cosmos DB logo Azure Cosmos DB

NoSQL JSON database for rapid, iterative app development.
  • InfluxData Landing page
    Landing page //
    2023-07-30
  • Azure Cosmos DB Landing page
    Landing page //
    2023-03-16

InfluxData features and specs

  • High Performance
    InfluxData's InfluxDB is designed to handle high write and query loads, making it suitable for time-series data and real-time applications.
  • Open-Source
    The core InfluxDB product is open-source, allowing for transparency, community contributions, and the option to self-host the database.
  • Scalability
    InfluxDB offers horizontal scalability, enabling users to handle increasing volumes of data efficiently through clustering.
  • Built-In Data Processing
    InfluxData offers integrated tools for data processing and scripting, such as Kapacitor for real-time processing and Flux for advanced querying.
  • Rich Ecosystem
    InfluxData provides a comprehensive ecosystem including Telegraf for data collection, Chronograf for visualization, and Kapacitor for alerting and processing.
  • Time-Series Focused
    InfluxDB is optimized for time-series data, offering specialized features like time-based retention policies, continuous queries, and downsampling.
  • Easy Integration
    InfluxDB integrates well with many third-party data visualization and monitoring tools such as Grafana, making it easier to build end-to-end solutions.

Possible disadvantages of InfluxData

  • Complexity
    The comprehensive features and tools in the InfluxData ecosystem can result in a steeper learning curve, especially for novices.
  • Cost
    While the open-source version is free, the enterprise and cloud-hosted versions come with a cost, which can be significant for small to mid-sized businesses.
  • Resource Intensive
    InfluxDB can be resource-intensive, especially under high loads, requiring significant hardware resources for optimal performance.
  • Limited SQL Support
    InfluxDB doesn’t fully support SQL, which can be a hurdle for users accustomed to traditional relational databases. It uses its own query languages like InfluxQL and Flux.
  • Fragmented Documentation
    Some users find the documentation fragmented or lacking in depth, which can make troubleshooting and advanced usage more challenging.
  • Data Backup and Restore
    Managing backups and restores in InfluxDB can be intricate and may require additional effort and tools to ensure data integrity and availability.

Azure Cosmos DB features and specs

  • Global Distribution
    Azure Cosmos DB allows for the distribution of data across multiple global regions, enhancing availability and delivering low-latency access to data for users around the world.
  • Multi-Model Support
    It supports multiple data models including document, graph, key-value, and column-family APIs, making it versatile for a variety of applications and use cases.
  • Automatic Scaling
    The database automatically scales up and down to meet the demands of application traffic, helping to manage workloads efficiently without manual intervention.
  • High Throughput and Low Latency
    Cosmos DB offers high performance with single-digit millisecond read and write latencies, ensuring fast access to data for applications.
  • Comprehensive SLAs
    Azure Cosmos DB provides industry-leading SLAs covering availability, throughput, consistency, and latency, offering strong guarantees for customers.
  • Integrated Security
    It includes robust security features such as SSL/TLS encryption, role-based access control, and integration with Azure Active Directory for secure data management.

Possible disadvantages of Azure Cosmos DB

  • Cost
    Azure Cosmos DB can be expensive, especially for high-throughput workloads and global distribution scenarios. Its pricing model based on provisioned throughput (RU/s) can add up quickly.
  • Complexity
    Managing and optimizing Cosmos DB can be complex, requiring a deep understanding of its configuration settings, partitioning strategies, and indexing to achieve optimal performance.
  • Vendor Lock-In
    As a proprietary service, using Cosmos DB tightly couples your application to Azure. This can make it difficult to migrate to other database solutions or cloud providers in the future.
  • Consistency Models
    Azure Cosmos DB supports multiple consistency levels which can introduce complexity in designing applications. Developers need to understand and choose the appropriate consistency level for their specific use case.
  • Limited Native Analytics
    Cosmos DB does not have built-in advanced analytics capabilities. Integrating with other services like Azure Synapse or Databricks may be necessary for sophisticated data analytics and reporting.

InfluxData videos

Barbara Nelson [InfluxData] | Best Practices for Data Ingestion into InfluxDB

Azure Cosmos DB videos

Azure Cosmos DB: Comprehensive Overview

More videos:

  • Review - Azure Friday | Azure Cosmos DB with Scott Hanselman
  • Tutorial - Azure Cosmos DB Tutorial | Globally distributed NoSQL database

Category Popularity

0-100% (relative to InfluxData and Azure Cosmos DB)
Databases
37 37%
63% 63
Time Series Database
100 100%
0% 0
NoSQL Databases
18 18%
82% 82
Big Data
100 100%
0% 0

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare InfluxData and Azure Cosmos DB

InfluxData Reviews

ReductStore vs. MinIO & InfluxDB on LTE Network: Who Really Wins the Speed Race?
Maintaining consistency between multiple databases, like MinIO and InfluxDB, adds a layer of complexity. In our setup, MinIO, used for blob storage, is linked to data points in InfluxDB via its filename. Any inconsistencies or mismatches between the two could potentially result in data loss. Furthermore, we need to query both databases, which is quite inefficient. Lastly,...
Apache Druid vs. Time-Series Databases
We occasionally get questions regarding how Apache Druid differs from time-series databases (TSDB) such as InfluxDB or Prometheus, and when to use each technology. This short post serves to help answer these questions.
Source: imply.io
4 Best Time Series Databases To Watch in 2019
InfluxDB is part of the TICK stack : Telegraf, InfluxDB, Chronograf and Kapacitor. InfluxData provides, out of the box, a visualization tool (that can be compared to Grafana), a data processing engine that binds directly with InfluxDB, and a set of more than 50+ agents that can collect real-time metrics for a lot of different data sources.
Source: medium.com

Azure Cosmos DB Reviews

We have no reviews of Azure Cosmos DB yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Azure Cosmos DB should be more popular than InfluxData. It has been mentiond 9 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

InfluxData mentions (2)

  • Can i log data into excel/csv using aws?
    I would highly recommend using a proper Time Series Database like QuestDB or InfluxDB to do this instead. You can always export data from wither of those two into Excel if your boss wants it in excel, but it's much easier to do data transformations, create graphs and reports, etc. If you have all the data in a proper database. Source: over 3 years ago
  • How to stream IoT data into Excel
    I would suggest using something better suited to IoT data than ... a spreadsheet. I'd recommend looking at one of the Time Series Databases for this. 1) QuestDB or 2) InfluxDB as these are much better suited to streaming data. Source: over 3 years ago

Azure Cosmos DB mentions (9)

  • Blazor server app, deployment options
    If you are writing the code maybe consider learning Cosmos DB it’s pretty easy to work with and there is a free tier. Also in my experience it’s much faster than a SQL database. Source: almost 2 years ago
  • Infrastructure as code (IaC) for Java-based apps on Azure
    Sometimes you don’t need an entire Java-based microservice. You can build serverless APIs with the help of Azure Functions. For example, Azure functions have a bunch of built-in connectors like Azure Event Hubs to process event-driven Java code and send the data to Azure Cosmos DB in real-time. FedEx and UBS projects are great examples of real-time, event-driven Java. I also recommend you to go through 👉 Code,... - Source: dev.to / over 2 years ago
  • Deploying a Mostly Serverless Website on GCP
    When debating the database solution for our application we were really seeking for a scalable serverless database that wouldn’t bill us for idle time. Options like AWS Athena, AWS Aurora Serverless, and Azure Cosmos DB immediately came to mind. We believed that GCP would have a comparable service, yet we could not find one. Even after consulting the GCP cloud service comparison documentation we were still unable... - Source: dev.to / almost 3 years ago
  • Which DB to use for API published on Azure?
    If you are looking for one to start with; you can try Cosmos: https://azure.microsoft.com/en-us/services/cosmos-db/. Source: about 3 years ago
  • Basic Setup for Azure Cosmos DB and Example Node App
    I have had an opportunity to work on a project that uses Azure Cosmos DB with the MongDB API as the backend database. I wanted to spend a little more time on my own understanding how to perform basic setup and a simple set of CRUD operations from a Node application, as well as construct an easy-to-follow procedure for other developers. - Source: dev.to / about 3 years ago
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What are some alternatives?

When comparing InfluxData and Azure Cosmos DB, you can also consider the following products

TimescaleDB - TimescaleDB is a time-series SQL database providing fast analytics, scalability, with automated data management on a proven storage engine.

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

Prometheus - An open-source systems monitoring and alerting toolkit.

ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.