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22nd September 2020, Tuesday
 
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Comparison Amazon EMR Hadoop MapR
 Name AWS EMR MapR
 Version Version 4.2.8 Version 6.1
 Drawbacks NA MapR has done lot of fork on top of Apache products. They even have a MapR filesystem which makes the product quite far from the original apache hadoop distribution.
 Advantages Manage Hadoop Cluster as plug and play --
 Languages Supported Java Python Scala Java Python
 Website aws.amazon.com/rds/aurora/ www.mapr.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. MapR provides access to a variety of data sources from a single computer cluster including big data workloads such as Apache Hadoop Apache Spark and a distributed file system called MapRFS.
 Database Model Relational Database Hadoop File System (HDFS)
 Technical Documentation https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Overview.html https://mapr.com/docs/61/
 License Commercial Commercial
 Cloud-based / SaaS SaaS Service from AWS https://mapr.com/products/orbit-cloud/
 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 Dun & Bradstreet AoL
 Key Differentiator With EMR we can spin up a hadoop cluster in minutes NA
 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