Twitter Data Pipeline. Moves data from Twitter to Elasticsearch through Kafka and RabbitMQ. Process: Tweets are queried through the Twitter API; Tweet is published to Kafka with the twitter username as the key for the ProducerRecord; Tweets are consumed, and the word count is calculated for each Tweet; Word count is serialized to JSON string. For the data science community, it is time to react and track COVID-19's global impact. In this post, I will demonstrate a simple way to study social media — analyzing Twitter. Overviews. This article will talk about how to build a datapipeline using popular big data platforms, Spark and AWS, to achieve sentiment and time series analysis.
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The first step is to open a connection to the Twitter Streaming API. This involves opening a persistent HTTP connection. Tweepy does this with the following code segment, which you can find here: resp = self.session.request ('POST', url, data=self.body, timeout=self.timeout, stream=True, auth=auth, verify=self.verify).
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