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home [2020/02/21 05:48]
root [Stream Processsing]
home [2020/02/21 05:54]
root [Big Data]
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 * [[EMC'​s Options]] * [[EMC'​s Options]]
 +
 +Although Apache Spark and Apache Ignite both carries the power of in-memory computing, Still they have some conceptual differences:​
 +
 +Spark doesn’t store data, actually it loads data from other storages for processing purpose, usually disk-based. As soon as the processing is finished it discards the data. On the other hand, Ignite provides a distributed in-memory key-value store (distributed cache or data grid) with SQL querying capabilities and ACID transactions.
 +
 +Spark is used for non-transactional,​ read-only data (RDDs don’t support in-place mutation), while Ignite supports both fully ACID compliant transactions (OLTP) and non-transactional (OLAP) payloads.
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 Data Analytics ​ Data Analytics ​
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 == Stream Processsing ==  == Stream Processsing == 
 * [[kafka]] and [[python kafka]] ​ and [[https://​github.com/​yahoo/​CMAK | kafka manager]] * [[kafka]] and [[python kafka]] ​ and [[https://​github.com/​yahoo/​CMAK | kafka manager]]
 +** http://​curator.apache.org/​ Zookeeper manager -  a distributed coordination service. It includes a highlevel API framework and utilities to make using Apache ZooKeeper
 *  http://​samza.incubator.apache.org/​ *  http://​samza.incubator.apache.org/​
 * It’s worth noting that Cassandra or HBase can be used a resequencing buffer, this technique is described in http://​www.slideshare.net/​dave_revell/​nearrealtime-analytics-with-kafka-and-hbase * It’s worth noting that Cassandra or HBase can be used a resequencing buffer, this technique is described in http://​www.slideshare.net/​dave_revell/​nearrealtime-analytics-with-kafka-and-hbase
 
home.txt · Last modified: 2020/06/01 02:01 by root
 
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