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I have 11 years of experience and work with IBM. My domain is Travel, Hospitality and Banking - both sectors process lots of data. The way the projects were set up and the mentors' explanation was... Read More
My Interaction was very short but left a positive impression. I enrolled and asked for a refund since I could not find the time. What happened next: They initiated Refund immediately. Their... Read More
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 hive project, you will design a data warehouse for e-commerce environments.
In this big data project, we will continue from a previous hive project "Data engineering on Yelp Datasets using Hadoop tools" and do the entire data processing using spark.
In this hive project, you will work on denormalizing the JSON data and create HIVE scripts with ORC file format.