Azure Cosmos DB vs Hadoop: What are the differences?
What is Azure Cosmos DB? A fully-managed, globally distributed NoSQL database service. 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.
What is Hadoop? Open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Azure Cosmos DB can be classified as a tool in the "NoSQL Database as a Service" category, while Hadoop is grouped under "Databases".
"Best-of-breed NoSQL features" is the top reason why over 13 developers like Azure Cosmos DB, while over 34 developers mention "Great ecosystem" as the leading cause for choosing Hadoop.
Hadoop is an open source tool with 9.18K GitHub stars and 5.74K GitHub forks. Here's a link to Hadoop's open source repository on GitHub.
According to the StackShare community, Hadoop has a broader approval, being mentioned in 237 company stacks & 116 developers stacks; compared to Azure Cosmos DB, which is listed in 24 company stacks and 23 developer stacks.
What is Azure Cosmos DB?
What is Hadoop?
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Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.Apps
- Desktop: And Electron to ship it as a desktop application.
- Android: a mix of Java and Kotlin.
- iOS: written in a mix of Objective C and Swift.
- The core application and the API written in PHP/Hack that runs on HHVM.
- The data is stored in MySQL using Vitess.
- Caching is done using Memcached and MCRouter.
- The search service takes help from SolrCloud, with various Java services.
- The messaging system uses WebSockets with many services in Java and Go.
- Load balancing is done using HAproxy with Consul for configuration.
- Most services talk to each other over gRPC,
- Some Thrift and JSON-over-HTTP
- Voice and video calling service was built in Elixir.
- Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
The MapReduce workflow starts to process experiment data nightly when data of the previous day is copied over from Kafka. At this time, all the raw log requests are transformed into meaningful experiment results and in-depth analysis. To populate experiment data for the dashboard, we have around 50 jobs running to do all the calculations and transforms of data.
in 2009 we open sourced mrjob, which allows any engineer to write a MapReduce job without contending for resources. We’re only limited by the amount of machines in an Amazon data center (which is an issue we’ve rarely encountered).
The massive volume of discovery data that powers Pinterest and enables people to save Pins, create boards and follow other users, is generated through daily Hadoop jobs...
If you need a document-based database with geo-redundancy (imagine AU-HU distance), this is the way to go.
Importing/Exporting data, interpreting results. Possible integration with SAS