Amazon RDS for Aurora vs Hadoop: What are the differences?
Amazon RDS for Aurora: MySQL and PostgreSQL compatible relational database with several times better performance. Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability; 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.
Amazon RDS for Aurora can be classified as a tool in the "SQL Database as a Service" category, while Hadoop is grouped under "Databases".
"MySQL compatibility " is the top reason why over 11 developers like Amazon RDS for Aurora, while over 34 developers mention "Great ecosystem" as the leading cause for choosing Hadoop.
Hadoop is an open source tool with 9.27K GitHub stars and 5.78K GitHub forks. Here's a link to Hadoop's open source repository on GitHub.
Airbnb, Uber Technologies, and Spotify are some of the popular companies that use Hadoop, whereas Amazon RDS for Aurora is used by Medium, StackShare, and Zumba. Hadoop has a broader approval, being mentioned in 237 company stacks & 127 developers stacks; compared to Amazon RDS for Aurora, which is listed in 121 company stacks and 31 developer stacks.
What is Amazon RDS for Aurora?
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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...
Managed MySQL clustered database so I dont have to deal with the required infrastructure
Importing/Exporting data, interpreting results. Possible integration with SAS
TBD. Good to have I think. Analytics on loads of data, recommendations?
Core database for managing users, teams, tests, and result summaries
We moved our database from compose.io to AWS for speed and price.