![]() You can launch the docker instance for kafka as follows: The following figure shows the running containers: The Flume and Spark container instances will be used to run our Flume agent and Spark streaming application respectively. We can use the instance of this container to create a topic, start producers and start consumers – which will be explained later. The Kafka container instance, as suggested by its name, will be running an instance of the Kafka distributed message queue server along with an instance of the Zookeeper service. Please note that the names Kafka, Spark and Flume are all separate docker instances of “cloudera/quickstart” –. We will be launching three docker instances namely kafka, flume and spark. Launching the required docker container instances Unable to find image 'hello-world:latest' locally Once docker is installed properly you can verify it by running a command as follows: You will find detailed instructions on installing docker at If you do not have docker, First of all you need to install docker on your system. If you don’t have docker available on your machine please go through the Installation section otherwise just skip to launching the required docker instances. We will run three docker instances, more details on that later. The data flow can be seen as follows:Īll of the services mentioned above will be running on docker instances also known as docker container instances. We can then create an external table in hive using hive SERDE to analyze this data in hive. Once spark has parsed the flume events the data would be stored on hdfs presumably a hive warehouse. Spark streaming app will parse the data as flume events separating the headers from the tweets in json format. Spark streaming will read the polling stream from the custom sink created by flume. This data would be stored on kafka as a channel and consumed using flume agent with spark sink. We will use the flume agent provided by cloudera to fetch the tweets from the twitter api. This approach is also informally known as “flafka”. We will use flume to fetch the tweets and enqueue them on kafka and flume to dequeue the data hence flume will act both as a kafka producer and consumer while kafka would be used as a channel to hold data. ![]() The aim of this post is to help you getting started with creating a data pipeline using flume, kafka and spark streaming that will enable you to fetch twitter data and analyze it in hive.
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