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Airline Online Performance

In this project, we are going to make big data available and accessible.

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What will you learn

  • Data preprocessing with Pig
  • Hive vs. MPP database systems (Hive vs. Impala/Drill)
  • Hive/Impala partitioning and clustering
  • Data compression, tuning and query optimization
  • Using database views to represent data.
  • Building time series data model
  • Visuliazing data using Microsoft Excel via ODBC

What will you get

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


  • For purpose of visualization, it is expected that you have Microsoft Excel on your host machine or an equivalent.

Project Description

Before data on any platform will become an asset to any organization, it has to pass through processing stage to ensure quality and availability. Afterward, that data has to be available to users (both human and system users). The availability of quality data in any organization is the guarantee of the value that data science (in general) will be to that organization. 

We are using the airline on-time performance dataset to demonstrate these principles and techniques in this hackerday and we will proceed to answer questions that can be found on the website like:

  • When is the best time of day/day of week/time of year to fly to minimize delays?
  • Do older planes suffer more delays?
  • How does the number of people flying between different locations change over time?

We will also transform the data access model into time series and demonstrate how clients can access data in our big data infrastructure using a simple tool like the Excel spreadsheet.



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...