Apache Spark Streaming | Real-time data processing for Hadoop | Big Data Tutorial
This lecture is all about Apache Spark Streaming where we have seen how to process real-time data streams using Apache Spark using its Spark Streaming API which allows multiple sources such as Kafka, file system, Cassandra, MySQL, HBase and processes the data in real time efficiently and reliably and uses sink as many options like HDFS, Kafka, Cassandra, MySQL, HBase etc.
In the previous lecture we have streaming data to HDFS using Apache Flume where we have set up the Flume Agent to listen to a directory in HDP Sandbox using SpoolDir as a source and streamed to HDFS directory in real time.
Commands for this lecture:
wget https://raw.githubusercontent.com/ash...
cd /usr/hdp/current/flume-server
bin/flume-ng agent --conf conf --conf-file /home/maria_dev/flume_logs.conf --name a1 -Dflume.root.logger=INFO, console
mkdir spool
On Ambari, create directory under user/maria_dev/flume
cp access_log.txt spool/logs.txt
Go to Ambari and verify
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HDP Sandbox Installation links:
Oracle VM Virtualbox: https://download.virtualbox.org/virtu...
HDP Sandbox link: https://archive.cloudera.com/hwx-sand...
HDP Sandbox installation guide: https://hortonworks.com/tutorial/sand...
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Audience
This tutorial is made for professionals who are willing to learn the basics of Big Data Analytics using Hadoop Ecosystem and become a Hadoop Developer. Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course.
Prerequisites
Before you start proceeding with this course, I am assuming that you have some basic knowledge to Core Java, database concepts, and any of the Linux operating system flavors.
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