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Perform OLAP on Hadoop big data platform has been a burden for a while, primarily due to high latency of queries. A different open source project like impala, presto and even apache hawq have tried to fix the problem with an MPP style of query execution architecture, but with an even larger dataset, performing query aggregation which is key to OLAP queries is still far from desirable.
Apache Kylin (kylin.apache.org) is a Distributed Analytics Engine that provides SQL interface and multidimensional analysis (OLAP) on the large dataset using MapReduce or Spark. This means that I can answer classical MDX questions in the Hadoop platform with a decent amount of latency.
In this big data project, we will be performing an OLAP cube design using the AdventureWorks dataset. The deliverable for this hadoop be to design a cube, build and implement it using Kylin, query the cube and even connect familiar tools (like Excel) with our new cube.
In this project, we will look at two database platforms - MongoDB and Cassandra and look at the philosophical difference in how these databases work and perform analytical queries.
In this project, we will take a look at three different SQL-on-Hadoop engines - Hive, Phoenix, Impala and Presto.
Analyze clickstream data of a website using Hadoop Hive to increase sales by optimizing every aspect of the customer experience on the website from the first mouse click to the last.