1 Introduction One can argue that the most challenging task in a Big Data setting is getting the
data that can then be used for data analysis and predictions. Towards this goal, in this
assignment, you will be setting up a pipeline to ingest data from Twitter, clean and process it, and
load it into a Hive table for analysis. You will be using Apache Kafka, Apache Flume for data
ingestion into HDFS, and Spark SQL for data analysis and Spark ML for prediction. 2 Instructions
2.1 Step 1: Setup Kafka producer to ingest tweets Setup a Kafka producer in Python that gets data
from Twitter for a specific set of keywords related to a topic (the choice of topic and keywords are
up to you) and sends it to a topic in a Kafka broker. You will need to sign up for a developer
account with Twitter, which is free. The data should be formatted in a way that can be easily
ingested by the other components of the pipeline. There is a limit on the number of calls that a
producer can make to Twitter at any one time. Check the limitations and adjust your code so that
tweets are received continuously without going over the limit. Some sample code is provided for
setting up the producer as well online videos.
2.2 Step 2: Setup Kafka Consumer Setup a Kafka consumer that reads from the Kafka topic and
saves the data to HDFS. The consumer should be designed to handle large volumes of data and
should be fault-tolerant. Some sample Kafka consumers are available as well.
2.3 Step 3: Setup Flume Agent Apache Flume is a streaming tool typically used for text data.
Unlike Apache Kafka, it is more lightweight in installation and setup. Review the videos posted on
Apache Flume and setup a Flume agent that gets data from Twitter and saves it to HDFS. 1
2.4 Step 4: Clean and Process Data The data that is saved to HDFS needs to be cleaned and put
into multiple columns. It is up to you how you want to clean the data, either in the consumer,
producer for Kafka, or in Flume, or at the end of the pipeline. You should ensure that the data is
formatted in a way that can be easily loaded into Spark for later processing (see below).
2.5 Step 5: Load Data into Spark SQL Data then must be loaded into a Scala DataFrame for
analysis. Use the Scala DataFrame to run some queries on the data that you have read. The
queries will depend on the topic that you have chosen and keywords received from Twitter.
2.6 Step 6: Train a Spark ML algorithm Using the data in HDFS, train a machine learning algorithm
using Spark ML to predict whether the tweets that you have have ingested have positive sentiment
or negative sentiment. You can also choose other predictions depending on the topic..
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
1 Introduction One can argue that the most challenging task .pdf
1. 1 Introduction One can argue that the most challenging task in a Big Data setting is getting the
data that can then be used for data analysis and predictions. Towards this goal, in this
assignment, you will be setting up a pipeline to ingest data from Twitter, clean and process it, and
load it into a Hive table for analysis. You will be using Apache Kafka, Apache Flume for data
ingestion into HDFS, and Spark SQL for data analysis and Spark ML for prediction. 2 Instructions
2.1 Step 1: Setup Kafka producer to ingest tweets Setup a Kafka producer in Python that gets data
from Twitter for a specific set of keywords related to a topic (the choice of topic and keywords are
up to you) and sends it to a topic in a Kafka broker. You will need to sign up for a developer
account with Twitter, which is free. The data should be formatted in a way that can be easily
ingested by the other components of the pipeline. There is a limit on the number of calls that a
producer can make to Twitter at any one time. Check the limitations and adjust your code so that
tweets are received continuously without going over the limit. Some sample code is provided for
setting up the producer as well online videos.
2.2 Step 2: Setup Kafka Consumer Setup a Kafka consumer that reads from the Kafka topic and
saves the data to HDFS. The consumer should be designed to handle large volumes of data and
should be fault-tolerant. Some sample Kafka consumers are available as well.
2.3 Step 3: Setup Flume Agent Apache Flume is a streaming tool typically used for text data.
Unlike Apache Kafka, it is more lightweight in installation and setup. Review the videos posted on
Apache Flume and setup a Flume agent that gets data from Twitter and saves it to HDFS. 1
2.4 Step 4: Clean and Process Data The data that is saved to HDFS needs to be cleaned and put
into multiple columns. It is up to you how you want to clean the data, either in the consumer,
producer for Kafka, or in Flume, or at the end of the pipeline. You should ensure that the data is
formatted in a way that can be easily loaded into Spark for later processing (see below).
2.5 Step 5: Load Data into Spark SQL Data then must be loaded into a Scala DataFrame for
analysis. Use the Scala DataFrame to run some queries on the data that you have read. The
queries will depend on the topic that you have chosen and keywords received from Twitter.
2.6 Step 6: Train a Spark ML algorithm Using the data in HDFS, train a machine learning algorithm
using Spark ML to predict whether the tweets that you have have ingested have positive sentiment
or negative sentiment. You can also choose other predictions depending on the topic.
