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This was great. The use of Jupyter was great. Prior to learning Python I was a self taught SQL user with advanced skills. I hold a Bachelors in Finance and have 5 years of business experience.. I... Read More
I'm a Graduate student and came into the job market and found a university degree wasn't sufficient to get a good paying job. I aimed at hottest technology in the market Big Data but the word BigData... 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 big data project, we will talk about Apache Zeppelin. We will write code, write notes, build charts and share all in one single data analytics environment using Hive, Spark and Pig.
In this project, we will show how to build an ETL pipeline on streaming datasets using Kafka.
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.