Input Data
● Insert rates are very high, but updates are less common.
● You want to load streaming data from sources like Apache Kafka (Druid supports exactly once semantics) or Amazon Kinesis, or batch data from HDFS, flat files, or an object storage like Amazon S3, Google Cloud Storage or Azure Storage.
Scale of Data
● You have very large data volumes, from many terabytes to petabytes.
● You have a large number of concurrent users, such as operational staff across your company, or end customers.
Data Queries
● Most of your queries are aggregation and reporting queries ("group by" queries). You may also have searching and scanning queries.
● You are targeting query latencies of 100ms to a few seconds, such as to power a user-facing application where you want your users to be able to self-service iterative ad hoc queries (i.e. data exploration), perhaps through a visual interface.
Types of Data
● Your data has a time component. Druid includes optimizations and design choices specifically related to time.
● You have high cardinality data columns (e.g. URLs, user IDs) and need fast counting and ranking over them.
