The path for the same is flume/Twitter/ on my HDFS. The channel that we have set for HDFS sink is MemCh. To be more specific, these are defined to get the values for the new filters we are adding from the Flume configuration file. It uses a simple extensible data model that allows for an online analytic application.”įirstly, in TwitterSourceConstants.java, I have defined the following new Strings. Also, it is robust and faults tolerant with tunable reliability mechanisms and many failovers and recovery mechanisms. It has a simple and flexible architecture based on streaming data flows. We are using Flume to access the real-time streaming data.Īccording to, “Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. Moreover, the PowerTrack API provides customers with the ability to filter the full Twitter firehose, and therefore only receive the data that they or their customers are interested in. So, for the entire data, we would need an Enterprise account to access Twitter’s PowerTrack API. But using a normal account can only extract around 1% of the Twitter data. With a big data tool like Apache Flume, we are able to extract real-time tweets. However, the amount of tweets we were able to collect with our previous Twitter program per keyword was around 200. Similarly, in a previous blog post, we learned how to get a sample of tweets with Twitter API using Python. In addition, twitter data insights are especially useful for businesses as they allow for the analysis of large amounts of data available online, which would be nearly impossible to investigate otherwise. More importantly, twitter data can be used for a variety of purposes such as research, consumer insights, demographic insights and many more. With more than 330 million active users, Twitter is one of the top platforms where people like to share their thoughts.
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