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Comparison Amazon EMR Hadoop Hortonworks
 Name AWS EMR Hortonworks
 Version Version 4.2.8 HDP v3.0
 Drawbacks NA --
 Advantages Manage Hadoop Cluster as plug and play --
 Languages Supported Java Python Scala Java Python
 Website aws.amazon.com/rds/aurora/ www.hortonworks.com
 XML Support no no
 JSON Support yes yes
 Brief description You can launch an Amazon EMR cluster in minutes. There is no need to worry about node provisioning cluster setup Hadoop configuration or cluster tuning. Amazon EMR takes care of these tasks so you can focus on analysis. Hortonworks has three interoperable product lines: HDP (based on Apache Hadoop Apache Hive Apache Spark) HDF (based on Apache NiFi Apache Storm, Apache Kafka), and Data Plane Services (based on Apache Atlas and Cloudbreak
 Database Model Relational Database Hadoop File System (HDFS)
 Technical Documentation https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Overview.html https://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.6.5/index.html
 License Commercial Commercial
 Cloud-based / SaaS SaaS Service from AWS Not Available
 Implementation Language NA NA
 Operating System Supported Not Applicable as its managed by AWS Linux
 Options for Integration / Access API Restful HTTP Restful HTTP
 Consistency NA NA
 Foreign Keys NA Not but you can join two files using Hive and Impala
 Streaming Support Yes Yes
 Analytics Support NA Using Mlib in Apache Spark
 Data Storage Schema NA Hadoop File System (HDFS)
 Notable Users NA hotels.com hilton.com
 Key Differentiator With EMR we can spin up a hadoop cluster in minutes More suited for platforms based on windows. Azure has a big partnership with hortonworks.
 Concurrency Yes Yes
 Partitioning No Yes
 Replication Yes Yes
 Secondary Indexes Yes based on secondary indexes using Solr Yes in HBase. Datawarehouse using cloudera is generally built using Hbase. Hbase has secondary indexes
 SchemaLess Yes Yes
 SQL Query NA No. HiveQL similar to SQL can be used withHive