Amazon Redshift vs Druid: What are the differences?
Developers describe Amazon Redshift as "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. On the other hand, Druid is detailed as "Fast column-oriented distributed data store". Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.
Amazon Redshift can be classified as a tool in the "Big Data as a Service" category, while Druid is grouped under "Big Data Tools".
"Data Warehousing" is the primary reason why developers consider Amazon Redshift over the competitors, whereas "Real Time Aggregations" was stated as the key factor in picking Druid.
Druid is an open source tool with 8.32K GitHub stars and 2.08K GitHub forks. Here's a link to Druid's open source repository on GitHub.
According to the StackShare community, Amazon Redshift has a broader approval, being mentioned in 270 company stacks & 68 developers stacks; compared to Druid, which is listed in 24 company stacks and 12 developer stacks.
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