FoundationDB vs Hadoop: What are the differences?
FoundationDB: Multi-model database with particularly strong fault tolerance, performance, and operational ease. FoundationDB is a NoSQL database with a shared nothing architecture. Designed around a "core" ordered key-value database, additional features and data models are supplied in layers. The key-value database, as well as all layers, supports full, cross-key and cross-server ACID transactions; 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.
FoundationDB and Hadoop can be categorized as "Databases" tools.
"ACID transactions" is the top reason why over 2 developers like FoundationDB, while over 34 developers mention "Great ecosystem" as the leading cause for choosing Hadoop.
Hadoop is an open source tool with 9.26K GitHub stars and 5.78K GitHub forks. Here's a link to Hadoop's open source repository on GitHub.
What is FoundationDB?
What is Hadoop?
Want advice about which of these to choose?Ask the StackShare community!
Sign up to add, upvote and see more prosMake informed product decisions
What are the cons of using FoundationDB?
What are the cons of using Hadoop?
Sign up to get full access to all the companiesMake informed product decisions
What tools integrate with FoundationDB?
Sign up to get full access to all the tool integrationsMake informed product decisions
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...
Importing/Exporting data, interpreting results. Possible integration with SAS