Stream Twitter Data into BigQuery with Cloud Dataprep

Inspired by Lakshmanan V’s article to show just a couple extra features

Jeremy Lorino
Jan 30, 2018 · 3 min read

All code mentioned here can be found in my git repo. Contribute, steal, or do nothing with it — your choice ;)

Let’s see how much data we can get by listening to Twitter’s Streaming API. I will break the code out into chunks to explain, but again all of this is included in the git repo.

Listen to streaming Twitter data

Below we are utilizing a TwitterStream helper class that takes an Array of keywords that will filter the real-time tweet stream.

Configure stream listener with Twitter keyword filters

Then we bind an event listener to the data event that is emitted from the TwitterStream instance from above. This listener will capture all tweets emitted as JSON and buffer them into a working array. Once the buffer is full, a copy of the working array is saved to the configured Google Cloud Storage bucket via StorageProvider; and the working array is cleared to allow for more tweets.

And don’t forget your config.json. Fill in your GCP project id, the location of your default application credentials, twitter app credentials, and storage bucketName.

Import Cloud Storage folder to Dataprep

Open the Google Cloud Platform dashboard — choose from the top left menu; Dataprep.

It is normal at the very bottom of the list — unless you have pinned it to your favorites.

Quick video on importing your dataset. For real, it is quick.

Import dataset

Wrangle newly imported dataset

Cloud Dataprep’s awesomeness is complemented by the fact that it would like you to “wrangle” your data. Particularly appealing to myself being a Texan.

There are at least 10,000 in-depth Cloud Dataprep articles, I will stick to the basics.

Now run that job yo. (top right)


Schedule Dataprep ETL to BigQuery

The best part about this little setup we are about to walk through; we are selecting a “folder” inside of a Cloud Storage bucket. Each time the flow runs it will pull all the files inside the folder and consider those files as the dataset to wrangle. So as the TwitterStream collector runs and dumps files into Cloud Storage Dataprep will continuously be able to get the latest and greatest.

Add a schedule to your flow
Every hour; 15 minutes past the hour
Select a BigQuery dataset
Select ‘Create a new table’
Select ‘Create a new table every run’
End result

Next time…Visualize with Datastudio

Google Cloud - Community

A collection of technical articles published or curated by Google Cloud Developer Advocates. The views expressed are those of the authors and don't necessarily reflect those of Google.

Jeremy Lorino

Written by

Selling Experiences

Google Cloud - Community

A collection of technical articles published or curated by Google Cloud Developer Advocates. The views expressed are those of the authors and don't necessarily reflect those of Google.

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