SOLVING ANALYTICAL PROBLEMS USING SPARK, CASSANDRA, DATASTAX. Rohit Bhardwaj Principal Cloud Engineer Twitter: rbhardwaj1

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "SOLVING ANALYTICAL PROBLEMS USING SPARK, CASSANDRA, DATASTAX. Rohit Bhardwaj Principal Cloud Engineer Twitter: rbhardwaj1"

Transcription

1 SOLVING ANALYTICAL PROBLEMS USING SPARK, CASSANDRA, DATASTAX Rohit Bhardwaj Principal Cloud Engineer Twitter: rbhardwaj1

2 AGENDA Big data characteristics Real time analytics Apache Spark Cassandra no sql database Spark with Cassandra

3 BIG DATA

4 BIG DATA CHARACTERISTICS

5

6

7

8 REAL TIME ANALYTICS USELESS

9 APACHE SPARK FOR DATA IN MOTION

10 DATA ANALYTICS

11 ANALYSIS STEPS Statistical analysis Classification Clustering Regression Similarity matching Collaborative filtering Profiling Dimensionality reduction Feature extraction

12 ANALYTICAL APPLICATION LIFECYCLE

13 SPARK / CASSANDRA ROLE

14 DISTRIBUTED TRANSACTIONAL DATABASE DESIGNED FOR BIG DATA AND HIGH AVAILABILITY Millions transactions per second 1000-node cluster scalability Millisecond response time and linear scalability Extremely high availability and partition tolerance Seamless multi data center support

15 DISTRIBUTED COMPUTATION ENGINE DESIGNED FOR BIG DATA AND IN- MEMORY PROCESSING Interactive and batch analytics Up to 100x faster than Hadoop 5-10x less code than Hadoop Efficiency and scalability Fault-tolerance

16

17

18 WHY SPARK?

19

20 BIG DATA

21 BIG DATA

22 WHY SPARK? Readability Expressiveness Fast Testability Interactive Fault Tolerant Unify Big Data

23 MAP-REDUCE EXPLOSION

24

25

26 LOCS As of April 2015

27 HISTORY OF SPARK

28

29 WHO IS USING SPARK?

30

31

32 SPARK CORE MAINTAINERS

33 SPARK MECHANICS

34

35 SPARK ARCHITECTURE

36 SPARK DEMO Install docker toolbox

37 SPARK DEMO edit/?workflow=50000

38 SPARK CONTEXT Task creator Scheduler Data locality Fault tolerance

39 RESILIENT DISTRIBUTED DATASETS (RDD) Resilient Distributed Datasets (RDD) is a fundamental data structure of Spark. It is an immutable distributed collection of objects. Each dataset in RDD is divided into logical partitions, which may be computed on different nodes of the cluster.

40 RESILIENT DISTRIBUTED DATASETS (RDD) Immutable Re-computable Fault tolerant Reusable

41 RESILIENT DISTRIBUTED DATASET - RDD DAG: Directed Acyclic Graph Transformations - map - filter -... Actions - collect - collect, count, reduce -...

42 DAG: DIRECTED ACYCLIC GRAPH

43 RECOVERY

44 DAG AND STAGES

45 RDD ACTION

46

47

48 SPARK GENERAL FLOW

49 SPARK MECHANICS

50

51 INPUT

52 SPARK WORKFLOW

53 SPARK EXECUTION MODEL

54 SPARK SQL

55 SPARK SQL EXAMPLE

56 DEMO

57 SPARK SQL DEMO Sample: Top salary SELECT sample_07.description, sample_07.salary FROM sample_07 WHERE ( sample_07.salary > ) ORDER BY sample_07.salary DESC LIMIT 1000

58 OUTPUT INFO : Number of reduce tasks determined at compile time: 1 INFO : In order to change the average load for a reducer (in bytes): INFO : set hive.exec.reducers.bytes.per.reducer=<number> INFO : In order to limit the maximum number of reducers: INFO : set hive.exec.reducers.max=<number> INFO : In order to set a constant number of reducers: INFO : set mapreduce.job.reduces=<number> INFO : number of splits:1 INFO : Submitting tokens for job: job_ _0177 INFO : The url to track the job: INFO : Starting Job = job_ _0177, Tracking URL = application_ _0177/ INFO : Kill Command = /opt/cloudera/parcels/cdh cdh5.5.0.p0.8/lib/hadoop/bin/hadoop job -kill job_ _0177 INFO : Hadoop job information for Stage-1: number of mappers: 1; number of reducers: 1 INFO : :36:09,296 Stage-1 map = 0%, reduce = 0% INFO : :36:18,547 Stage-1 map = 100%, reduce = 0%, Cumulative CPU 1.58 sec INFO : :36:27,781 Stage-1 map = 100%, reduce = 100%, Cumulative CPU 3.26 sec INFO : MapReduce Total cumulative CPU time: 3 seconds 260 msec INFO : Ended Job = job_ _0177

59 BATCH VS REAL TIME PROCESSING

60

61 STREAM PROCESSING

62 SPARK STREAMING

63 DISCRETIZED STREAM DSTREAM

64 DSTREAM TRANSFORMATION

65 SPARK STREAMING ARCHITECTURE

66 DATA STREAM SOURCES

67 STREAMINGEXAMPLE.SCALA import org.apache.spark.{sparkconf, SparkContext} import org.apache.spark.sparkcontext._ import org.apache.spark.streaming._ import org.apache.spark.streaming.streamingcontext._ import com.datastax.spark.connector._ import com.datastax.spark.connector.streaming._ object StreamingExample { def main(args: Array[String]) { val conf = new SparkConf(true).setAppName("Streaming Example").setMaster("spark:// :7077").set("spark.cassandra.connection.host", " ").set("spark.cleaner.ttl", "3600").setJars(Array("/ /target/scala-2.10/streaming-example_ jar")) val ssc = new StreamingContext(conf, Seconds(4)) val stream = ssc.sockettextstream(" ", 9999) stream.flatmap(record => record.split(" ")).map(word => (word,1)).reducebykey(_ + _).print() } } ssc.start() ssc.awaittermination()

68 RESULTS OUTPUT Time: ms (Drama,22) (Comedy,12) (Action,16) (Romance,9) (Dance,11) (Family,18) (Adventure,7) (Horror,10) (Thriller,19) (Fantasy,11) Time: ms (Drama,11) (Comedy,14) (Action,13) (Romance,2) (Dance,9) (Family,12) (Adventure,9) (Science,2) (Thriller,3) (Fantasy,8)...

69

70 APACHE KAFKA Distributed publish-subscribe messaging system Processing real time activity stream data (Logs, metrics, collections, social media streams) Initially by LinkedIn, now Apache project Does not use JMS API and standards Kafka maintains feeds of messages in topics

71

72

73

74

75

76 DEMO Cassandra query language

77 COMBINING SPARK AND CASSANDRA Spark Great for analyzing large amount of data Cassandra Great for storing large amount of data

78 ACID Atomicity: All operations are performed or none of them are. If one part of the transaction fails, then all fail. Consistency: The transaction must meet all rules defined by the system at all times; there are never any half-completed transactions. Isolation: Each transaction is independent unto itself. Durability: Once complete, the transaction cannot be undone.

79 Would ACID work with Bigdata?

80 REPLICATION

81

82 DB SHARDING

83 BASE Basically Available the system guarantees some level of availability to the data even in regards to node failures. The data may be stale, but will still give and accept responses. Soft State the data is in a constant state of flux; so, while a response may be given, the freshness or consistency of the data is not guaranteed to be the most current. Eventual Consistency the data will eventually be consistent through all nodes and in all databases, but not every transaction at every moment. It will reach some guaranteed state eventually.

84

85

86 CAP THEOREM BASE (Basically Available, Soft-State, Eventual Consistency) data store

87

88

89

90

91

92 SPARK AND CASSANDRA ARCHITECTURE

93

94

95 CASSANDRA AND SPARK

96 LAMBDA ARCHITECTURE SPARK AND CASSANDRA

97 LAMBDA ARCHITECTURE SPARK AND CASSANDRA

98 REFERENCES

99 REFERENCES landing_page/current.html

SPARK USE CASE IN TELCO. Apache Spark Night 9-2-2014! Chance Coble!

SPARK USE CASE IN TELCO. Apache Spark Night 9-2-2014! Chance Coble! SPARK USE CASE IN TELCO Apache Spark Night 9-2-2014! Chance Coble! Use Case Profile Telecommunications company Shared business problems/pain Scalable analytics infrastructure is a problem Pushing infrastructure

More information

Spark ΕΡΓΑΣΤΗΡΙΟ 10. Prepared by George Nikolaides 4/19/2015 1

Spark ΕΡΓΑΣΤΗΡΙΟ 10. Prepared by George Nikolaides 4/19/2015 1 Spark ΕΡΓΑΣΤΗΡΙΟ 10 Prepared by George Nikolaides 4/19/2015 1 Introduction to Apache Spark Another cluster computing framework Developed in the AMPLab at UC Berkeley Started in 2009 Open-sourced in 2010

More information

Streaming items through a cluster with Spark Streaming

Streaming items through a cluster with Spark Streaming Streaming items through a cluster with Spark Streaming Tathagata TD Das @tathadas CME 323: Distributed Algorithms and Optimization Stanford, May 6, 2015 Who am I? > Project Management Committee (PMC) member

More information

STREAM PROCESSING AT LINKEDIN: APACHE KAFKA & APACHE SAMZA. Processing billions of events every day

STREAM PROCESSING AT LINKEDIN: APACHE KAFKA & APACHE SAMZA. Processing billions of events every day STREAM PROCESSING AT LINKEDIN: APACHE KAFKA & APACHE SAMZA Processing billions of events every day Neha Narkhede Co-founder and Head of Engineering @ Stealth Startup Prior to this Lead, Streams Infrastructure

More information

Big Data JAMES WARREN. Principles and best practices of NATHAN MARZ MANNING. scalable real-time data systems. Shelter Island

Big Data JAMES WARREN. Principles and best practices of NATHAN MARZ MANNING. scalable real-time data systems. Shelter Island Big Data Principles and best practices of scalable real-time data systems NATHAN MARZ JAMES WARREN II MANNING Shelter Island contents preface xiii acknowledgments xv about this book xviii ~1 Anew paradigm

More information

Spark in Action. Fast Big Data Analytics using Scala. Matei Zaharia. www.spark- project.org. University of California, Berkeley UC BERKELEY

Spark in Action. Fast Big Data Analytics using Scala. Matei Zaharia. www.spark- project.org. University of California, Berkeley UC BERKELEY Spark in Action Fast Big Data Analytics using Scala Matei Zaharia University of California, Berkeley www.spark- project.org UC BERKELEY My Background Grad student in the AMP Lab at UC Berkeley» 50- person

More information

Hadoop vs Apache Spark

Hadoop vs Apache Spark Innovate, Integrate, Transform Hadoop vs Apache Spark www.altencalsoftlabs.com Introduction Any sufficiently advanced technology is indistinguishable from magic. said Arthur C. Clark. Big data technologies

More information

SQL VS. NO-SQL. Adapted Slides from Dr. Jennifer Widom from Stanford

SQL VS. NO-SQL. Adapted Slides from Dr. Jennifer Widom from Stanford SQL VS. NO-SQL Adapted Slides from Dr. Jennifer Widom from Stanford 55 Traditional Databases SQL = Traditional relational DBMS Hugely popular among data analysts Widely adopted for transaction systems

More information

Architectures for massive data management

Architectures for massive data management Architectures for massive data management Apache Spark Albert Bifet albert.bifet@telecom-paristech.fr October 20, 2015 Spark Motivation Apache Spark Figure: IBM and Apache Spark What is Apache Spark Apache

More information

Spark. Fast, Interactive, Language- Integrated Cluster Computing

Spark. Fast, Interactive, Language- Integrated Cluster Computing Spark Fast, Interactive, Language- Integrated Cluster Computing Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael Franklin, Scott Shenker, Ion Stoica UC

More information

Apache Spark : Fast and Easy Data Processing Sujee Maniyam Elephant Scale LLC sujee@elephantscale.com http://elephantscale.com

Apache Spark : Fast and Easy Data Processing Sujee Maniyam Elephant Scale LLC sujee@elephantscale.com http://elephantscale.com Apache Spark : Fast and Easy Data Processing Sujee Maniyam Elephant Scale LLC sujee@elephantscale.com http://elephantscale.com Spark Fast & Expressive Cluster computing engine Compatible with Hadoop Came

More information

Scaling Out With Apache Spark. DTL Meeting 17-04-2015 Slides based on https://www.sics.se/~amir/files/download/dic/spark.pdf

Scaling Out With Apache Spark. DTL Meeting 17-04-2015 Slides based on https://www.sics.se/~amir/files/download/dic/spark.pdf Scaling Out With Apache Spark DTL Meeting 17-04-2015 Slides based on https://www.sics.se/~amir/files/download/dic/spark.pdf Your hosts Mathijs Kattenberg Technical consultant Jeroen Schot Technical consultant

More information

Developing Scalable Smart Grid Infrastructure to Enable Secure Transmission System Control

Developing Scalable Smart Grid Infrastructure to Enable Secure Transmission System Control Developing Scalable Smart Grid Infrastructure to Enable Secure Transmission System Control EP/K006487/1 UK PI: Prof Gareth Taylor (BU) China PI: Prof Yong-Hua Song (THU) Consortium UK Members: Brunel University

More information

Overview of Databases On MacOS. Karl Kuehn Automation Engineer RethinkDB

Overview of Databases On MacOS. Karl Kuehn Automation Engineer RethinkDB Overview of Databases On MacOS Karl Kuehn Automation Engineer RethinkDB Session Goals Introduce Database concepts Show example players Not Goals: Cover non-macos systems (Oracle) Teach you SQL Answer what

More information

BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON

BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON BIG DATA IN THE CLOUD : CHALLENGES AND OPPORTUNITIES MARY- JANE SULE & PROF. MAOZHEN LI BRUNEL UNIVERSITY, LONDON Overview * Introduction * Multiple faces of Big Data * Challenges of Big Data * Cloud Computing

More information

Big Data Analytics with Cassandra, Spark & MLLib

Big Data Analytics with Cassandra, Spark & MLLib Big Data Analytics with Cassandra, Spark & MLLib Matthias Niehoff AGENDA Spark Basics In A Cluster Cassandra Spark Connector Use Cases Spark Streaming Spark SQL Spark MLLib Live Demo CQL QUERYING LANGUAGE

More information

Big Data Analytics with Spark and Oscar BAO. Tamas Jambor, Lead Data Scientist at Massive Analytic

Big Data Analytics with Spark and Oscar BAO. Tamas Jambor, Lead Data Scientist at Massive Analytic Big Data Analytics with Spark and Oscar BAO Tamas Jambor, Lead Data Scientist at Massive Analytic About me Building a scalable Machine Learning platform at MA Worked in Big Data and Data Science in the

More information

Unified Big Data Processing with Apache Spark. Matei Zaharia @matei_zaharia

Unified Big Data Processing with Apache Spark. Matei Zaharia @matei_zaharia Unified Big Data Processing with Apache Spark Matei Zaharia @matei_zaharia What is Apache Spark? Fast & general engine for big data processing Generalizes MapReduce model to support more types of processing

More information

Unified Big Data Analytics Pipeline. 连 城 lian@databricks.com

Unified Big Data Analytics Pipeline. 连 城 lian@databricks.com Unified Big Data Analytics Pipeline 连 城 lian@databricks.com What is A fast and general engine for large-scale data processing An open source implementation of Resilient Distributed Datasets (RDD) Has an

More information

Challenges for Data Driven Systems

Challenges for Data Driven Systems Challenges for Data Driven Systems Eiko Yoneki University of Cambridge Computer Laboratory Quick History of Data Management 4000 B C Manual recording From tablets to papyrus to paper A. Payberah 2014 2

More information

Apache Storm vs. Spark Streaming Two Stream Processing Platforms compared

Apache Storm vs. Spark Streaming Two Stream Processing Platforms compared Apache Storm vs. Spark Streaming Two Stream Platforms compared DBTA Workshop on Stream Berne, 3.1.014 Guido Schmutz BASEL BERN BRUGG LAUSANNE ZÜRICH DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG MUNICH

More information

Apache Spark. Christopher Homa. October 11, Apache Spark is an open source cluster computing framework.

Apache Spark. Christopher Homa. October 11, Apache Spark is an open source cluster computing framework. Apache Spark Christopher Homa October 11, 2016 Overview Apache Spark is an open source cluster computing framework. Initially developed at UC Berkeley s AMPLab in 2009, Spark was donated to Apache and

More information

Pulsar Realtime Analytics At Scale. Tony Ng April 14, 2015

Pulsar Realtime Analytics At Scale. Tony Ng April 14, 2015 Pulsar Realtime Analytics At Scale Tony Ng April 14, 2015 Big Data Trends Bigger data volumes More data sources DBs, logs, behavioral & business event streams, sensors Faster analysis Next day to hours

More information

Lambda Architecture. Near Real-Time Big Data Analytics Using Hadoop. January 2015. Email: bdg@qburst.com Website: www.qburst.com

Lambda Architecture. Near Real-Time Big Data Analytics Using Hadoop. January 2015. Email: bdg@qburst.com Website: www.qburst.com Lambda Architecture Near Real-Time Big Data Analytics Using Hadoop January 2015 Contents Overview... 3 Lambda Architecture: A Quick Introduction... 4 Batch Layer... 4 Serving Layer... 4 Speed Layer...

More information

Real Time Data Processing using Spark Streaming

Real Time Data Processing using Spark Streaming Real Time Data Processing using Spark Streaming Hari Shreedharan, Software Engineer @ Cloudera Committer/PMC Member, Apache Flume Committer, Apache Sqoop Contributor, Apache Spark Author, Using Flume (O

More information

Lambda Architecture for Batch and Real- Time Processing on AWS with Spark Streaming and Spark SQL. May 2015

Lambda Architecture for Batch and Real- Time Processing on AWS with Spark Streaming and Spark SQL. May 2015 Lambda Architecture for Batch and Real- Time Processing on AWS with Spark Streaming and Spark SQL May 2015 2015, Amazon Web Services, Inc. or its affiliates. All rights reserved. Notices This document

More information

E6895 Advanced Big Data Analytics Lecture 3:! Spark and Data Analytics

E6895 Advanced Big Data Analytics Lecture 3:! Spark and Data Analytics E6895 Advanced Big Data Analytics Lecture 3:! Spark and Data Analytics Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science Mgr., Dept. of Network Science and Big

More information

Spark Streaming!! Real-time big-data processing

Spark Streaming!! Real-time big-data processing Spark Streaming!! Real-time big-data processing Tathagata Das (TD) UC BERKELEY What is Spark Streaming? BlinkDB Shark Spark Streaming GraphX MLlib Spark Extends Spark for doing big data stream processing

More information

Distributed Computing" with Open-Source Software

Distributed Computing with Open-Source Software Distributed Computing" with Open-Source Software Reza Zadeh Presented at Infosys OSSmosis Problem Data growing faster than processing speeds Only solution is to parallelize on large clusters» Wide use

More information

Real Time Analytics for Big Data. NtiSh Nati Shalom @natishalom

Real Time Analytics for Big Data. NtiSh Nati Shalom @natishalom Real Time Analytics for Big Data A Twitter Inspired Case Study NtiSh Nati Shalom @natishalom Big Data Predictions Overthe next few years we'll see the adoption of scalable frameworks and platforms for

More information

Programming Hadoop 5-day, instructor-led BD-106. MapReduce Overview. Hadoop Overview

Programming Hadoop 5-day, instructor-led BD-106. MapReduce Overview. Hadoop Overview Programming Hadoop 5-day, instructor-led BD-106 MapReduce Overview The Client Server Processing Pattern Distributed Computing Challenges MapReduce Defined Google's MapReduce The Map Phase of MapReduce

More information

Apache Spark and Distributed Programming

Apache Spark and Distributed Programming Apache Spark and Distributed Programming Concurrent Programming Keijo Heljanko Department of Computer Science University School of Science November 25th, 2015 Slides by Keijo Heljanko Apache Spark Apache

More information

Big Data Analytics - Accelerated. stream-horizon.com

Big Data Analytics - Accelerated. stream-horizon.com Big Data Analytics - Accelerated stream-horizon.com StreamHorizon & Big Data Integrates into your Data Processing Pipeline Seamlessly integrates at any point of your your data processing pipeline Implements

More information

Big Data Architecture

Big Data Architecture Big Architecture Guido Schmutz BASEL BERN BRUGG DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. GENEVA HAMBURG COPENHAGEN LAUSANNE MUNICH STUTTGART VIENNA ZURICH Guido Schmutz Working for Trivadis for more than

More information

Near Real Time Indexing Kafka Message to Apache Blur using Spark Streaming. by Dibyendu Bhattacharya

Near Real Time Indexing Kafka Message to Apache Blur using Spark Streaming. by Dibyendu Bhattacharya Near Real Time Indexing Kafka Message to Apache Blur using Spark Streaming by Dibyendu Bhattacharya Pearson : What We Do? We are building a scalable, reliable cloud-based learning platform providing services

More information

SQL + NOSQL + NEWSQL + REALTIME FOR INVESTMENT BANKS

SQL + NOSQL + NEWSQL + REALTIME FOR INVESTMENT BANKS Enterprise Data Problems in Investment Banks BigData History and Trend Driven by Google CAP Theorem for Distributed Computer System Open Source Building Blocks: Hadoop, Solr, Storm.. 3548 Hypothetical

More information

CSE-E5430 Scalable Cloud Computing Lecture 11

CSE-E5430 Scalable Cloud Computing Lecture 11 CSE-E5430 Scalable Cloud Computing Lecture 11 Keijo Heljanko Department of Computer Science School of Science Aalto University keijo.heljanko@aalto.fi 30.11-2015 1/24 Distributed Coordination Systems Consensus

More information

Unified Batch & Stream Processing Platform

Unified Batch & Stream Processing Platform Unified Batch & Stream Processing Platform Himanshu Bari Director Product Management Most Big Data Use Cases Are About Improving/Re-write EXISTING solutions To KNOWN problems Current Solutions Were Built

More information

Can the Elephants Handle the NoSQL Onslaught?

Can the Elephants Handle the NoSQL Onslaught? Can the Elephants Handle the NoSQL Onslaught? Avrilia Floratou, Nikhil Teletia David J. DeWitt, Jignesh M. Patel, Donghui Zhang University of Wisconsin-Madison Microsoft Jim Gray Systems Lab Presented

More information

Advanced Data Science on Spark

Advanced Data Science on Spark Advanced Data Science on Spark Reza Zadeh @Reza_Zadeh http://reza-zadeh.com Data Science Problem Data growing faster than processing speeds Only solution is to parallelize on large clusters» Wide use in

More information

Big Data Analytics. Lucas Rego Drumond

Big Data Analytics. Lucas Rego Drumond Big Data Analytics Big Data Analytics Lucas Rego Drumond Information Systems and Machine Learning Lab (ISMLL) Institute of Computer Science University of Hildesheim, Germany Apache Spark Apache Spark 1

More information

Introduction to Spark

Introduction to Spark Introduction to Spark Shannon Quinn (with thanks to Paco Nathan and Databricks) Quick Demo Quick Demo API Hooks Scala / Java All Java libraries *.jar http://www.scala- lang.org Python Anaconda: https://

More information

Hadoop & Spark Using Amazon EMR

Hadoop & Spark Using Amazon EMR Hadoop & Spark Using Amazon EMR Michael Hanisch, AWS Solutions Architecture 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Agenda Why did we build Amazon EMR? What is Amazon EMR?

More information

Architectures for massive data management

Architectures for massive data management Architectures for massive data management Apache Kafka, Samza, Storm Albert Bifet albert.bifet@telecom-paristech.fr October 20, 2015 Stream Engine Motivation Digital Universe EMC Digital Universe with

More information

Apache HBase. Crazy dances on the elephant back

Apache HBase. Crazy dances on the elephant back Apache HBase Crazy dances on the elephant back Roman Nikitchenko, 16.10.2014 YARN 2 FIRST EVER DATA OS 10.000 nodes computer Recent technology changes are focused on higher scale. Better resource usage

More information

Spark: Making Big Data Interactive & Real-Time

Spark: Making Big Data Interactive & Real-Time Spark: Making Big Data Interactive & Real-Time Matei Zaharia UC Berkeley / MIT www.spark-project.org What is Spark? Fast and expressive cluster computing system compatible with Apache Hadoop Improves efficiency

More information

BIG DATA ANALYTICS For REAL TIME SYSTEM

BIG DATA ANALYTICS For REAL TIME SYSTEM BIG DATA ANALYTICS For REAL TIME SYSTEM Where does big data come from? Big Data is often boiled down to three main varieties: Transactional data these include data from invoices, payment orders, storage

More information

Introduction to Hadoop. New York Oracle User Group Vikas Sawhney

Introduction to Hadoop. New York Oracle User Group Vikas Sawhney Introduction to Hadoop New York Oracle User Group Vikas Sawhney GENERAL AGENDA Driving Factors behind BIG-DATA NOSQL Database 2014 Database Landscape Hadoop Architecture Map/Reduce Hadoop Eco-system Hadoop

More information

CS555: Distributed Systems [Fall 2015] Dept. Of Computer Science, Colorado State University

CS555: Distributed Systems [Fall 2015] Dept. Of Computer Science, Colorado State University CS 555: DISTRIBUTED SYSTEMS [SPARK] Shrideep Pallickara Computer Science Colorado State University Frequently asked questions from the previous class survey Streaming Significance of minimum delays? Interleaving

More information

Apache Flink Next-gen data analysis. Kostas Tzoumas ktzoumas@apache.org @kostas_tzoumas

Apache Flink Next-gen data analysis. Kostas Tzoumas ktzoumas@apache.org @kostas_tzoumas Apache Flink Next-gen data analysis Kostas Tzoumas ktzoumas@apache.org @kostas_tzoumas What is Flink Project undergoing incubation in the Apache Software Foundation Originating from the Stratosphere research

More information

Big Systems, Big Data

Big Systems, Big Data Big Systems, Big Data When considering Big Distributed Systems, it can be noted that a major concern is dealing with data, and in particular, Big Data Have general data issues (such as latency, availability,

More information

Chapter 5: Stream Processing. Big Data Management and Analytics 193

Chapter 5: Stream Processing. Big Data Management and Analytics 193 Chapter 5: Big Data Management and Analytics 193 Today s Lesson Data Streams & Data Stream Management System Data Stream Models Insert-Only Insert-Delete Additive Streaming Methods Sliding Windows & Ageing

More information

Hadoop MapReduce and Spark. Giorgio Pedrazzi, CINECA-SCAI School of Data Analytics and Visualisation Milan, 10/06/2015

Hadoop MapReduce and Spark. Giorgio Pedrazzi, CINECA-SCAI School of Data Analytics and Visualisation Milan, 10/06/2015 Hadoop MapReduce and Spark Giorgio Pedrazzi, CINECA-SCAI School of Data Analytics and Visualisation Milan, 10/06/2015 Outline Hadoop Hadoop Import data on Hadoop Spark Spark features Scala MLlib MLlib

More information

MongoDB in the NoSQL and SQL world. Horst Rechner horst.rechner@fokus.fraunhofer.de Berlin, 2012-05-15

MongoDB in the NoSQL and SQL world. Horst Rechner horst.rechner@fokus.fraunhofer.de Berlin, 2012-05-15 MongoDB in the NoSQL and SQL world. Horst Rechner horst.rechner@fokus.fraunhofer.de Berlin, 2012-05-15 1 MongoDB in the NoSQL and SQL world. NoSQL What? Why? - How? Say goodbye to ACID, hello BASE You

More information

Conjugating data mood and tenses: Simple past, infinite present, fast continuous, simpler imperative, conditional future perfect

Conjugating data mood and tenses: Simple past, infinite present, fast continuous, simpler imperative, conditional future perfect Matteo Migliavacca (mm53@kent) School of Computing Conjugating data mood and tenses: Simple past, infinite present, fast continuous, simpler imperative, conditional future perfect Simple past - Traditional

More information

Integrating Big Data into the Computing Curricula

Integrating Big Data into the Computing Curricula Integrating Big Data into the Computing Curricula Yasin Silva, Suzanne Dietrich, Jason Reed, Lisa Tsosie Arizona State University http://www.public.asu.edu/~ynsilva/ibigdata/ 1 Overview Motivation Big

More information

Brave New World: Hadoop vs. Spark

Brave New World: Hadoop vs. Spark Brave New World: Hadoop vs. Spark Dr. Kurt Stockinger Associate Professor of Computer Science Director of Studies in Data Science Zurich University of Applied Sciences Datalab Seminar, Zurich, Oct. 7,

More information

Oracle Big Data Fundamentals Ed 1

Oracle Big Data Fundamentals Ed 1 Oracle University Contact Us: 001-855-844-3881 Oracle Big Data Fundamentals Ed 1 Duration: 5 Days What you will learn In the Oracle Big Data Fundamentals course, learn to use Oracle's Integrated Big Data

More information

NoSQL in der Cloud Why? Andreas Hartmann

NoSQL in der Cloud Why? Andreas Hartmann NoSQL in der Cloud Why? Andreas Hartmann 17.04.2013 17.04.2013 2 NoSQL in der Cloud Why? Quelle: http://res.sys-con.com/story/mar12/2188748/cloudbigdata_0_0.jpg Why Cloud??? 17.04.2013 3 NoSQL in der Cloud

More information

Apache Ignite TM (Incubating) - In- Memory Data Fabric Fast Data Meets Open Source

Apache Ignite TM (Incubating) - In- Memory Data Fabric Fast Data Meets Open Source Apache Ignite TM (Incubating) - In- Memory Data Fabric Fast Data Meets Open Source DMITRIY SETRAKYAN Founder, PPMC http://www.ignite.incubator.apache.org #apacheignite Agenda Apache Ignite (tm) In- Memory

More information

Making Big Data Processing Simple with Spark. Matei Zaharia

Making Big Data Processing Simple with Spark. Matei Zaharia Making Big Data Processing Simple with Spark Matei Zaharia December 17, 2015 What is Apache Spark? Fast and general cluster computing engine that generalizes the MapReduce model Makes it easy and fast

More information

The Flink Big Data Analytics Platform. Marton Balassi, Gyula Fora" {mbalassi, gyfora}@apache.org

The Flink Big Data Analytics Platform. Marton Balassi, Gyula Fora {mbalassi, gyfora}@apache.org The Flink Big Data Analytics Platform Marton Balassi, Gyula Fora" {mbalassi, gyfora}@apache.org What is Apache Flink? Open Source Started in 2009 by the Berlin-based database research groups In the Apache

More information

Big Data Processing. Patrick Wendell Databricks

Big Data Processing. Patrick Wendell Databricks Big Data Processing Patrick Wendell Databricks About me Committer and PMC member of Apache Spark Former PhD student at Berkeley Left Berkeley to help found Databricks Now managing open source work at Databricks

More information

Spark and the Big Data Library

Spark and the Big Data Library Spark and the Big Data Library Reza Zadeh Thanks to Matei Zaharia Problem Data growing faster than processing speeds Only solution is to parallelize on large clusters» Wide use in both enterprises and

More information

ON-LINE VIDEO ANALYTICS EMBRACING BIG DATA

ON-LINE VIDEO ANALYTICS EMBRACING BIG DATA ON-LINE VIDEO ANALYTICS EMBRACING BIG DATA David Vanderfeesten, Bell Labs Belgium ANNO 2012 YOUR DATA IS MONEY BIG MONEY! Your click stream, your activity stream, your electricity consumption, your call

More information

An Industrial Perspective on the Hadoop Ecosystem. Eldar Khalilov Pavel Valov

An Industrial Perspective on the Hadoop Ecosystem. Eldar Khalilov Pavel Valov An Industrial Perspective on the Hadoop Ecosystem Eldar Khalilov Pavel Valov agenda 03.12.2015 2 agenda Introduction 03.12.2015 2 agenda Introduction Research goals 03.12.2015 2 agenda Introduction Research

More information

Petabyte Scale Data at Facebook. Dhruba Borthakur, Engineer at Facebook, SIGMOD, New York, June 2013

Petabyte Scale Data at Facebook. Dhruba Borthakur, Engineer at Facebook, SIGMOD, New York, June 2013 Petabyte Scale Data at Facebook Dhruba Borthakur, Engineer at Facebook, SIGMOD, New York, June 2013 Agenda 1 Types of Data 2 Data Model and API for Facebook Graph Data 3 SLTP (Semi-OLTP) and Analytics

More information

Beyond Hadoop with Apache Spark and BDAS

Beyond Hadoop with Apache Spark and BDAS Beyond Hadoop with Apache Spark and BDAS Khanderao Kand Principal Technologist, Guavus 12 April GITPRO World 2014 Palo Alto, CA Credit: Some stajsjcs and content came from presentajons from publicly shared

More information

Moving From Hadoop to Spark

Moving From Hadoop to Spark + Moving From Hadoop to Spark Sujee Maniyam Founder / Principal @ www.elephantscale.com sujee@elephantscale.com Bay Area ACM meetup (2015-02-23) + HI, Featured in Hadoop Weekly #109 + About Me : Sujee

More information

Hadoop: A Framework for Data- Intensive Distributed Computing. CS561-Spring 2012 WPI, Mohamed Y. Eltabakh

Hadoop: A Framework for Data- Intensive Distributed Computing. CS561-Spring 2012 WPI, Mohamed Y. Eltabakh 1 Hadoop: A Framework for Data- Intensive Distributed Computing CS561-Spring 2012 WPI, Mohamed Y. Eltabakh 2 What is Hadoop? Hadoop is a software framework for distributed processing of large datasets

More information

Oracle Big Data Fundamentals Ed 1 NEW

Oracle Big Data Fundamentals Ed 1 NEW Oracle University Contact Us: +90 212 329 6779 Oracle Big Data Fundamentals Ed 1 NEW Duration: 5 Days What you will learn In the Oracle Big Data Fundamentals course, learn to use Oracle's Integrated Big

More information

Beyond Lambda - how to get from logical to physical. Artur Borycki, Director International Technology & Innovations

Beyond Lambda - how to get from logical to physical. Artur Borycki, Director International Technology & Innovations Beyond Lambda - how to get from logical to physical Artur Borycki, Director International Technology & Innovations Simplification & Efficiency Teradata believe in the principles of self-service, automation

More information

Spark and Shark. High- Speed In- Memory Analytics over Hadoop and Hive Data

Spark and Shark. High- Speed In- Memory Analytics over Hadoop and Hive Data Spark and Shark High- Speed In- Memory Analytics over Hadoop and Hive Data Matei Zaharia, in collaboration with Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Cliff Engle, Michael Franklin, Haoyuan Li,

More information

BIG DATA. Using the Lambda Architecture on a Big Data Platform to Improve Mobile Campaign Management. Author: Sandesh Deshmane

BIG DATA. Using the Lambda Architecture on a Big Data Platform to Improve Mobile Campaign Management. Author: Sandesh Deshmane BIG DATA Using the Lambda Architecture on a Big Data Platform to Improve Mobile Campaign Management Author: Sandesh Deshmane Executive Summary Growing data volumes and real time decision making requirements

More information

Hadoop Ecosystem B Y R A H I M A.

Hadoop Ecosystem B Y R A H I M A. Hadoop Ecosystem B Y R A H I M A. History of Hadoop Hadoop was created by Doug Cutting, the creator of Apache Lucene, the widely used text search library. Hadoop has its origins in Apache Nutch, an open

More information

NOT IN KANSAS ANY MORE

NOT IN KANSAS ANY MORE NOT IN KANSAS ANY MORE How we moved into Big Data Dan Taylor - JDSU Dan Taylor Dan Taylor: An Engineering Manager, Software Developer, data enthusiast and advocate of all things Agile. I m currently lucky

More information

Hadoop Ecosystem Overview. CMSC 491 Hadoop-Based Distributed Computing Spring 2015 Adam Shook

Hadoop Ecosystem Overview. CMSC 491 Hadoop-Based Distributed Computing Spring 2015 Adam Shook Hadoop Ecosystem Overview CMSC 491 Hadoop-Based Distributed Computing Spring 2015 Adam Shook Agenda Introduce Hadoop projects to prepare you for your group work Intimate detail will be provided in future

More information

Designing Agile Data Pipelines. Ashish Singh Software Engineer, Cloudera

Designing Agile Data Pipelines. Ashish Singh Software Engineer, Cloudera Designing Agile Data Pipelines Ashish Singh Software Engineer, Cloudera About Me Software Engineer @ Cloudera Contributed to Kafka, Hive, Parquet and Sentry Used to work in HPC @singhasdev 204 Cloudera,

More information

Rakam: Distributed Analytics API

Rakam: Distributed Analytics API Rakam: Distributed Analytics API Burak Emre Kabakcı May 30, 2014 Abstract Today, most of the big data applications needs to compute data in real-time since the Internet develops quite fast and the users

More information

Data Management in the Cloud

Data Management in the Cloud Data Management in the Cloud Ryan Stern stern@cs.colostate.edu : Advanced Topics in Distributed Systems Department of Computer Science Colorado State University Outline Today Microsoft Cloud SQL Server

More information

BigData. An Overview of Several Approaches. David Mera 16/12/2013. Masaryk University Brno, Czech Republic

BigData. An Overview of Several Approaches. David Mera 16/12/2013. Masaryk University Brno, Czech Republic BigData An Overview of Several Approaches David Mera Masaryk University Brno, Czech Republic 16/12/2013 Table of Contents 1 Introduction 2 Terminology 3 Approaches focused on batch data processing MapReduce-Hadoop

More information

The Top 10 7 Hadoop Patterns and Anti-patterns. Alex Holmes @

The Top 10 7 Hadoop Patterns and Anti-patterns. Alex Holmes @ The Top 10 7 Hadoop Patterns and Anti-patterns Alex Holmes @ whoami Alex Holmes Software engineer Working on distributed systems for many years Hadoop since 2008 @grep_alex grepalex.com what s hadoop...

More information

Hadoop. MPDL-Frühstück 9. Dezember 2013 MPDL INTERN

Hadoop. MPDL-Frühstück 9. Dezember 2013 MPDL INTERN Hadoop MPDL-Frühstück 9. Dezember 2013 MPDL INTERN Understanding Hadoop Understanding Hadoop What's Hadoop about? Apache Hadoop project (started 2008) downloadable open-source software library (current

More information

A Performance Analysis of Distributed Indexing using Terrier

A Performance Analysis of Distributed Indexing using Terrier A Performance Analysis of Distributed Indexing using Terrier Amaury Couste Jakub Kozłowski William Martin Indexing Indexing Used by search

More information

Xiaoming Gao Hui Li Thilina Gunarathne

Xiaoming Gao Hui Li Thilina Gunarathne Xiaoming Gao Hui Li Thilina Gunarathne Outline HBase and Bigtable Storage HBase Use Cases HBase vs RDBMS Hands-on: Load CSV file to Hbase table with MapReduce Motivation Lots of Semi structured data Horizontal

More information

Apache Ignite TM (Incubating) - In- Memory Data Fabric Fast Data Meets Open Source

Apache Ignite TM (Incubating) - In- Memory Data Fabric Fast Data Meets Open Source Apache Ignite TM (Incubating) - In- Memory Data Fabric Fast Data Meets Open Source DMITRIY SETRAKYAN Founder, PPMC http://www.ignite.incubator.apache.org @apacheignite @dsetrakyan Agenda About In- Memory

More information

Simplifying Big Data Analytics: Unifying Batch and Stream Processing. John Fanelli,! VP Product! In-Memory Compute Summit! June 30, 2015!!

Simplifying Big Data Analytics: Unifying Batch and Stream Processing. John Fanelli,! VP Product! In-Memory Compute Summit! June 30, 2015!! Simplifying Big Data Analytics: Unifying Batch and Stream Processing John Fanelli,! VP Product! In-Memory Compute Summit! June 30, 2015!! Streaming Analy.cs S S S Scale- up Database Data And Compute Grid

More information

Big Data at Spotify. Anders Arpteg, Ph D Analytics Machine Learning, Spotify

Big Data at Spotify. Anders Arpteg, Ph D Analytics Machine Learning, Spotify Big Data at Spotify Anders Arpteg, Ph D Analytics Machine Learning, Spotify Quickly about me Quickly about Spotify What is all the data used for? Quickly about Spark Hadoop MR vs Spark Need for (distributed)

More information

The evolution of database technology (II) Huibert Aalbers Senior Certified Executive IT Architect

The evolution of database technology (II) Huibert Aalbers Senior Certified Executive IT Architect The evolution of database technology (II) Huibert Aalbers Senior Certified Executive IT Architect IT Insight podcast This podcast belongs to the IT Insight series You can subscribe to the podcast through

More information

In Memory Accelerator for MongoDB

In Memory Accelerator for MongoDB In Memory Accelerator for MongoDB Yakov Zhdanov, Director R&D GridGain Systems GridGain: In Memory Computing Leader 5 years in production 100s of customers & users Starts every 10 secs worldwide Over 15,000,000

More information

Real Time Big Data Processing

Real Time Big Data Processing Real Time Big Data Processing Cloud Expo 2014 Ian Meyers Amazon Web Services Global Infrastructure Deployment & Administration App Services Analytics Compute Storage Database Networking AWS Global Infrastructure

More information

Big Data Technology ดร.ช ชาต หฤไชยะศ กด. Choochart Haruechaiyasak, Ph.D.

Big Data Technology ดร.ช ชาต หฤไชยะศ กด. Choochart Haruechaiyasak, Ph.D. Big Data Technology ดร.ช ชาต หฤไชยะศ กด Choochart Haruechaiyasak, Ph.D. Speech and Audio Technology Laboratory (SPT) National Electronics and Computer Technology Center (NECTEC) National Science and Technology

More information

LAB 2 SPARK / D-STREAM PROGRAMMING SCIENTIFIC APPLICATIONS FOR IOT WORKSHOP

LAB 2 SPARK / D-STREAM PROGRAMMING SCIENTIFIC APPLICATIONS FOR IOT WORKSHOP LAB 2 SPARK / D-STREAM PROGRAMMING SCIENTIFIC APPLICATIONS FOR IOT WORKSHOP ICTP, Trieste, March 24th 2015 The objectives of this session are: Understand the Spark RDD programming model Familiarize with

More information

BIG DATA TOOLS. Top 10 open source technologies for Big Data

BIG DATA TOOLS. Top 10 open source technologies for Big Data BIG DATA TOOLS Top 10 open source technologies for Big Data We are in an ever expanding marketplace!!! With shorter product lifecycles, evolving customer behavior and an economy that travels at the speed

More information

Not Relational Models For The Management of Large Amount of Astronomical Data. Bruno Martino (IASI/CNR), Memmo Federici (IAPS/INAF)

Not Relational Models For The Management of Large Amount of Astronomical Data. Bruno Martino (IASI/CNR), Memmo Federici (IAPS/INAF) Not Relational Models For The Management of Large Amount of Astronomical Data Bruno Martino (IASI/CNR), Memmo Federici (IAPS/INAF) What is a DBMS A Data Base Management System is a software infrastructure

More information

Monitis Project Proposals for AUA. September 2014, Yerevan, Armenia

Monitis Project Proposals for AUA. September 2014, Yerevan, Armenia Monitis Project Proposals for AUA September 2014, Yerevan, Armenia Distributed Log Collecting and Analysing Platform Project Specifications Category: Big Data and NoSQL Software Requirements: Apache Hadoop

More information

Apache Spark 11/10/15. Context. Reminder. Context. What is Spark? A GrowingStack

Apache Spark 11/10/15. Context. Reminder. Context. What is Spark? A GrowingStack Apache Spark Document Analysis Course (Fall 2015 - Scott Sanner) Zahra Iman Some slides from (Matei Zaharia, UC Berkeley / MIT& Harold Liu) Reminder SparkConf JavaSpark RDD: Resilient Distributed Datasets

More information

The Big Data Ecosystem at LinkedIn Roshan Sumbaly, Jay Kreps, and Sam Shah LinkedIn

The Big Data Ecosystem at LinkedIn Roshan Sumbaly, Jay Kreps, and Sam Shah LinkedIn The Big Data Ecosystem at LinkedIn Roshan Sumbaly, Jay Kreps, and Sam Shah LinkedIn Presented by :- Ishank Kumar Aakash Patel Vishnu Dev Yadav CONTENT Abstract Introduction Related work The Ecosystem Ingress

More information

INTRODUCTION TO APACHE HADOOP MATTHIAS BRÄGER CERN GS-ASE

INTRODUCTION TO APACHE HADOOP MATTHIAS BRÄGER CERN GS-ASE INTRODUCTION TO APACHE HADOOP MATTHIAS BRÄGER CERN GS-ASE AGENDA Introduction to Big Data Introduction to Hadoop HDFS file system Map/Reduce framework Hadoop utilities Summary BIG DATA FACTS In what timeframe

More information

Introduction to Big Data! with Apache Spark" UC#BERKELEY#

Introduction to Big Data! with Apache Spark UC#BERKELEY# Introduction to Big Data! with Apache Spark" UC#BERKELEY# So What is Data Science?" Doing Data Science" Data Preparation" Roles" This Lecture" What is Data Science?" Data Science aims to derive knowledge!

More information