Apache Bean proof of concept Java and Phyton Integrations

Overview

Google BigQuery as enterprise-wide analytics platform and currently has a number of projects for creating data pipelines which get data from various sources, perform necessary transformations and output data to BigQuery.

The chosen technology for these data pipelines is Google DataFlow which has been open-sourced by Google and re-branded as Apache Beam. There are two Google Apache Beam SDKs: Java SDK and Python SDK. We expect you to be able to work with both.

Test

The test is a typical de-normalization task that is performed frequently when loading data to BigQuery. The test itself doesn’t require interaction with BigQuery, as we find that final output of transformed data to BigQuery is the easy part. The transformations in Google DataFlow are more complex and this is what we would like you to do.

You’ll be given 3 files in gzip-archived JSON format that we receive from Spotify API: streams, tracks and users. Your job is to develop two pipelines in Google DataFlow (one in Java and one in Python) to denormalize these three files into one flat output JSON file.

Source Files

Schema of each source file is provided below. For simplicity, consider all the fields of type “string”.

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One record example:

{"message":"APIStreamData","version":"2","user_id":"9302111e473e4b76da615a47a133097a","track_i d":"7718fc9605964e8698bc108bf9008864","length":"201","cached":"","source":"others_playlist","sour ce_uri":"spotify:user:12150809594:playlist:60XVOmMeSorE8ZgLVpqDTN","device_type":"desktop","os" :"Windows","stream_country":"CO","timestamp":"2016-10-19 19:00:00 UTC","report_date":"2016-10- 19 00:00:00 UTC"}

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One record example:

{"message":"APITrackData","version":"2","track_id":"7ce9b6b3b51e46b09f5698b53ab37ea9","uri":"spo tify:track:3NHR4rVd31G2wZ2m6JNM3D","isrc":"SEUM70500101","album_code":"00602537010196","al bum_artist":"Abba","track_name":"Should I Laugh Or Cry - Intro Version","album_name":"The Visitors","track_artists":"ABBA"}

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One record example:

{"message":"APIUserData","version":"2","user_id":"47ca39a988516984267a922d586fcccf","zip_code":" 1","region":"","country":"UY","gender":"female","birth_year":"1996","referral":"","partner":"","product ":"","type":"ad","access":"free"}

Output File

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One record example:

{"user_id":"aba7aebaa4972d3139a268444f1ca009","cached":"","timestamp":"2016-10-19 16:45:00 UTC","source_uri":"","track_id":"c7ff4b8cc72345bbaeec1ff6eb9bfee9","source":"other","length":"215", "version":"2","device_type":"tablet”,"message":"APIStreamData","os":"iOS","stream_country":"DO","re port_date":"2016-10-19 00:00:00 UTC","isrc":"USUL10400965","album_code":"00602498827840","product":"","country":"DO","region":" ","zip_code":"12","access":"free","gender":"male","partner":"","referral":"","type":"ad","birth_year":"1 986"}

Instructions

Preparation

Create account on Google Cloud Platform. To get one, go to [login to view URL] and login with your Gmail email account. If you don’t have Gmail email account, register for one. You’ll be given a trial period and $300 in free spending.

Create a new bucket on Google Cloud Storage and upload provided source files into it.

Create a new bucket on Google Cloud Storage for staging files. You will need it to run DataFlow jobs

Create two solutions one for Java SDK and one for Python SDK. It’s up to you what IDE and configuration to use. At UMG we are using Intellij IDEA + Gradle for Java SDK and Intellij PyCharm IDE for Python.

Read files from Google Cloud Storage

Write resulting denormalized file to Google Cloud Storage

Approximate number of code lines for Java solution is 350 lines and for Python solution around 100 lines.

There will be some routine tasks such as converting from/to JSON, dealing with key/value pairs for joining data, etc. We leave this to your expertise to figure out how to do it.

Good luck!

Google Cloud Platform Google Cloud Storage Java Python Arhitektura porgramske opreme

ID projekta: #19353895

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