Amazon Redshift vs Hadoop: What are the differences?
What is Amazon Redshift? Fast, fully managed, petabyte-scale data warehouse service. Redshift makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
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.
Amazon Redshift belongs to "Big Data as a Service" category of the tech stack, while Hadoop can be primarily classified under "Databases".
"Data Warehousing" is the primary reason why developers consider Amazon Redshift over the competitors, whereas "Great ecosystem" was stated as the key factor in picking 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 Redshift is used by Lyft, Coursera, and 9GAG. Hadoop has a broader approval, being mentioned in 237 company stacks & 127 developers stacks; compared to Amazon Redshift, which is listed in 270 company stacks and 68 developer stacks.
What is Amazon Redshift?
What is Hadoop?
Want advice about which of these to choose?Ask the StackShare community!
What are the cons of using Amazon Redshift?
What are the cons of using Hadoop?
What tools integrate with Amazon Redshift?
What tools integrate with Hadoop?
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...
Aggressive archiving of historical data to keep the production database as small as possible. Using our in-house soon-to-be-open-sourced ETL library, SharpShifter.
Importing/Exporting data, interpreting results. Possible integration with SAS
TBD. Good to have I think. Analytics on loads of data, recommendations?