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  • Kylin web界面 知识点介绍

    Big Data Era:

    1.More and more data becoming available on Hadoop
    2.Limitations in existing Business Intelligence (BI) Tools
      Limited support for Hadoop
      Data size growing exponentially
      High latency of interactive queries
      Scale-Up architecture
    3.Challenges to adopt Hadoop as interactive analysis system
      Majority of analyst groups are SQL savvy
      No mature SQL interface on Hadoop
      OLAP capability on Hadoop ecosystem not ready yet

    Business Needs for Big Data Analysis

    1.Sub-second query latency on billions of rows
    2.ANSI SQL for both analysts and engineers
    3.Full OLAP capability to offer advanced functionality
    4.Seamless Integration with BI Tools
    5.Support of high cardinality and high dimensions
    6.High concurrency – thousands of end users
    7.Distributed and scale out architecture for large data volume

    Kylin is designed to accelerate 80+% analytics queries performance on Hadoop

    Technical Challenges:

    1.Huge volume data
      Table scan
    2.Big table joins
      Data shuffling
    3.Analysis on different granularity
      Runtime aggregation expensive
    4.Map Reduce job
      Batch processing

    OLAP Cube – Balance between Space and Time

    How Does Kylin Utilize Hadoop Components

    1.Hive
      Input source
      Pre-join star schema during cube building
    2.MapReduce
      Pre-aggregation metrics during cube building
    3.HDFS
      Store intermediated files during cube building.
    4.HBase
      Store data cube.
      Serve query on data cube.
      Coprocessor is used for query processing.

    Cube Designer

    Job Management

    Query and Visualization


    Tableau Integration

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  • 原文地址:https://www.cnblogs.com/panpanwelcome/p/7896508.html
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