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Recently I became interested in Hadoop as I think its a great platform for storing and analyzing large structured and unstructured data sets. The experts did a great job not only explaining the... Read More
I came to the platform with no experience and now I am knowledgeable in Machine Learning with Python. No easy thing I must say, the sessions are challenging and go to the depths. I looked at graduate... Read More
In this hive project, we want to take a deeper dive into some analytical features in Hive. Using SQL is still very dominant and will remain so for the nearest features. Most big data tools have been adapted to allow users interact with them using the familiar SQL language. This is because of years of knowledge and skill that has gone into training, acceptance, tooling, standards development and re-engineering. So in many cases, using these cool features of SQL to access data solves a lot of analytical questions without ever needing us to resort to machine learning, BI or data mining.
In this big data project, we want to look at these features in Hive that allows us to perform analytical queries over large datasets.
We will be using the adventure works dataset in a MySQL dataset. Therefore, there will be a need to ingest and transform the data before we proceed to analytics.
The goal of this Spark project is to analyze business reviews from Yelp dataset and ingest the final output of data processing in Elastic Search.Also, use the visualisation tool in the ELK stack to visualize various kinds of ad-hoc reports from the data.
In this project, we will take a look at three different SQL-on-Hadoop engines - Hive, Phoenix, Impala and Presto.
In this big data project, we will be performing an OLAP cube design using AdventureWorks database. The deliverable for this session will 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.