Fully operational local setup to experiment with a Spark-based ecosystem

An efficient quick-start tool to build a Raspberry Pi (or Debian-based) Cluster with popular ecosystem like Hadoop, Spark

Manage big data on a cluster with HDFS and MapReduce Write programs to analyze data on Hadoop with Pig and Spark Store and query your data with Sqoop, Hive, MySQL, HBase, Cassandra, MongoDB, Drill, Phoenix, and Presto Design real-world systems using the Hadoop ecosystem Learn how your cluster is managed with YARN, Mesos, Zookeeper, Oozie, Zeppelin, and Hue Handle streaming data in real time with Kafka, Flume, Spark Streaming, Flink, and Storm

hadoop-projects IBM stock project Get IBM stock dataset Clean the dataset Load dataset on the HDFS Build MapReduce program Process/ Analyse result Hadoop set up Run single node Hadoop cluster /usr/local/Celler/hadoop Check : https://www.slideshare.net/SunilkumarMohanty3/install-apache-hadoop-on-mac-os-sierra-76275019 http://zhongyaonan.com/hadoop-tutorial/setting-up-hadoop-2-6-on-mac-osx-yosemite.html Go to :http://localhost:50070/dfshealth.html#tab-overview Start : hstart Hadoop command: hadoop fs -ls hadoop fs -mkdir /hbp Upload a file in HDFS hadoop fs -put <localsrc> ... <HDFS_dest_Path> go to : http://localhost:50070/explorer.html#/hbp/ibm-stock Dataset head date - opening stock quote - high - low - traded volume - closing price Clean dataset with command : awk,sed,grep Run the program Copy jar to Hadoop Run the program on Hadoop system: hadoop jar /hbp/ibm-stock/ibm-stock-1.0-SNAPSHOT.jar /hbp/ibm-stock/ibm-stock.csv /hbp/ibm-stock/output Check output dir : hadoop fs -ls /hbp/ibm-stock/output Copy file from HDFS to local file system : hadoop fs -get /hpb/ibm-stock/output/part-r-00000 home/Users/hien/results.csv Check head home/Users/hien/results.csv Customer Analysis Collect data Customer master data : MySQL Logs : text file Twitter feeds : JSON Load data from data sources in HDFS Mug data Create table in Hive to store data in format Query and join tables Export data Set up stack: Hortonwork data platform HDP Install HDP sandbox: HDP 2.3 HDP : hive, squoop , Fraud Detection system Clean dataset Create model Using: Spark and Hadoop Problem: predict payment transaction is suspect Build model : Find relevant field: Apache Spark 2 Spark ecosystem : Spark core Spark streaming Spark SQL MLlib GraphX Spark-R Apache Spark component: + navigate to : localhost:4040 run spark-shell : $SPARK_HOME/bin/spark-shell Word count Create pairRDD : valpairRDD=stringRdd.map( s => (s,1)) Run reducebykey to count the occurency of each word : alwordCountRDD=pairRDD.reduceByKey((x,y) =>x+y) Run the collect to see the result : valwordCountList=wordCountRDD.collect Find the sum of integers Create RDD of even number from integers : valintRDD = sc.parallelize(Array(1,4,5,6,7,10,15)) Filter even numbers from RDD : valevenNumbersRDD=intRDD.filter(i => (i%2==0)) Sum the even numbers from RDD : val sum =evenNumbersRDD.sum Count the number of words in file : Read txt file : cat people.txt Read file from Apache Spark shell : val file=sc.textFile("/usr/local/spark/examples/src/main/resources/people.txt") Flaten the file, prcess and split , with each word : valflattenFile = file.flatMap(s =>s.split(", ")) Check the content of RDD : flattenFile.collect Count all words from RDD : val count = flattenFile.count Working with Data and Storage + Chua hoc 4 (RDD transformation),

Big Data lab assignments using Hadoop ecosystem tools (HDFS, Spark, MapReduce, etc.) — practiced on a Docker-based cluster simulation.

Various ML algorithms, inferences and scripts modified to run on Spark Clusters in Azure HDInsight with Data Lake Store backed storage for Secure hadoop ecosystems

Big Data- Provisioned AWS EMR Spark Cluster, employing PySpark for analysis of IMDB datasets. Stored the data in Hadoop and S3 and engineered and executed ETL pipeline, with utilization of big data infrastructure and Spark ecosystem.

This repo features structured experiments using Apache Ambari to manage and monitor Big Data ecosystems like HDFS, YARN, Hive, Pig, HBase, Spark, and Zeppelin via REST APIs and UI. It automates cluster ops, benchmarks performance, and visualizes system metrics.