mirror of https://github.com/apache/druid.git
19 lines
1.4 KiB
Markdown
19 lines
1.4 KiB
Markdown
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layout: doc_page
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---
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Druid vs Hadoop
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===============
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Hadoop has shown the world that it’s possible to house your data warehouse on commodity hardware for a fraction of the price of typical solutions. As people adopt Hadoop for their data warehousing needs, they find two things.
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1. They can now query all of their data in a fairly flexible manner and answer any question they have
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2. The queries take a long time
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The first one is the joy that everyone feels the first time they get Hadoop running. The latter is what they realize after they have used Hadoop interactively for a while because Hadoop is optimized for throughput, not latency.
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Druid is a complementary addition to Hadoop. Hadoop is great at storing and making accessible large amounts of individually low-value data. Unfortunately, Hadoop is not great at providing query speed guarantees on top of that data, nor does it have very good operational characteristics for a customer-facing production system. Druid, on the other hand, excels at taking high-value summaries of the low-value data on Hadoop, making it available in a fast and always-on fashion, such that it could be exposed directly to a customer.
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Druid also requires some infrastructure to exist for [deep storage](../dependencies/deep-storage.html). HDFS is one of the implemented options for this [deep storage](../dependencies/deep-storage.html).
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