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SQL Analytics with Hive

In this project, we will look the features in Hive that allow us to perform analytical queries over large datasets.

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What will you learn

  • Hive high-level recap
  • Data ingestion/transformation using Sqoop, Spark and Hive
  • File formats and query performance
  • Writing aggregate queries using UDAFs.
  • Aggregation using widowing functions.
  • Query optimizations in Hive

What will you get

  • Access to recording of the complete project
  • Access to all material related to project like data files, solution files etc.


  • Installation Cloudera Quickstart VM.
  • It is also essential for students to have a fair knowledge of Hadoop.
  • No knowledge of Hive is required for this hackerday as we will do a little overview of hive before we begin.

Project Description

In this 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 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.



Big Data & Enterprise Software Engineer

I am passionate about software development, databases, data analysis and the android platform. My native language is java but no one has stopped me so far from learning and using angular and node.js. Data and data analysis is thrilling and so are my experiences with SQL on Oracle, Microsoft SQL Server, Postgres and MyS see more...