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Transcript of Cloudera Impala
data in: HDFS & HBase What is Impala Hadoop Hive Dremel Benefits of Impala Rapid return of information Query data as its being ingested No MapReduce
= low latency Uses its own daemons
to query data directly Impala, Hive, and MapReduce HiveQL Doesn't replace Hive or MR subset of SQL92 1 line Impala query = 100's lines of Java Familiar and unified platform for batch and real-time queries we still need batch Impala features Language similar to HiveQL Supports HDFS & HBase
compressed text, sequence, avro Uses same metadata, ODBC, Hue Beeswax, as Hive Kerberos authentication No SPOF Current limitations No SerDes No UDF's Raises performance bar,
whilst retaining user experience Impala state store coordinates information about all instances of impalad used to find data so the daemons can be used to respond to queries Runs on all datanodes Responds to queries from Impala shell Schedules tasks for optimal execution Updates Impala state store Impala shell Issues queries Perform admin tasks Queries passed via ODBC Trevni columnar binary storage format Impala vs Dremel Distributed scalable aggregation algorithms user decides on flexibility vs pure performance Impala + Trevni = extra awesome! Demo Thanks! @jrkinley email@example.com