Apache Flink. Fast and Reliable Large-Scale Data Processing
|
|
- Georgia Miles
- 8 years ago
- Views:
Transcription
1 Apache Flink Fast and Reliable Large-Scale Data Processing Fabian 1
2 What is Apache Flink? Distributed Data Flow Processing System Focused on large-scale data analytics Real-time stream and batch processing Easy and powerful APIs (Java / Scala) Robust execution backend 2
3 What is Flink good at? It s a general-purpose data analytics system Real-time stream processing with flexible windows Complex and heavy ETL jobs Analyzing huge graphs Machine-learning on large data sets... 3
4 Table API Gelly Library ML Library Apache MRQL Dataflow Apache SAMOA Flink in the Hadoop Ecosystem Libraries Flink Core DataSet API (Java/Scala) Optimizer Runtime DataStream API (Java/Scala) Stream Builder Environments Embedded Local Cluster Yarn Apache Tez Data Sources HDFS HCatalog Hadoop IO JDBC Apache HBase Apache Kafka Apache Flume S3 RabbitMQ... 4
5 Flink in the ASF Flink entered the ASF about one year ago 04/2014: Incubation 12/2014: Graduation Strongly growing community Nov.10 Apr.12 Aug.13 Dec.14 #unique git committers (w/o manual de-dup) 5
6 Where is Flink moving? A "use-case complete" framework to unify batch & stream processing Data Streams Kafka RabbitMQ... Historic data HDFS JDBC... Flink Analytical Workloads ETL Relational processing Graph analysis Machine learning Streaming data analysis Goal: Treat batch as finite stream 6
7 Programming Model & APIs HOW TO USE FLINK? 7
8 Unified Java & Scala APIs Fluent and mirrored APIs in Java and Scala Table API for relational expressions Batch and Streaming APIs almost identical with slightly different semantics in some cases 8
9 DataSets and Transformations Input filter First map Second ExecutionEnvironment env = ExecutionEnvironment.getExecutionEnvironment(); DataSet<String> input = env.readtextfile(input); DataSet<String> first = input.filter (str -> str.contains( Apache Flink )); DataSet<String> second = first.map(str -> str.tolowercase()); second.print(); env.execute(); 9
10 Expressive Transformations Element-wise map, flatmap, filter, project Group-wise groupby, reduce, reducegroup, combinegroup, mappartition, aggregate, distinct Binary join, cogroup, union, cross Iterations iterate, iteratedelta Physical re-organization rebalance, partitionbyhash, sortpartition Streaming Window, windowmap, comap,... 10
11 Rich Type System Use any Java/Scala classes as a data type Tuples, POJOs, and case classes Not restricted to key-value pairs Define (composite) keys directly on data types Expression Tuple position Selector function 11
12 Counting Words in Batch and Stream case class Word (word: String, frequency: Int) DataSet API (batch): val lines: DataSet[String] = env.readtextfile(...) lines.flatmap {line => line.split(" ").map(word => Word(word,1))}.groupBy("word").sum("frequency").print() DataStream API (streaming): val lines: DataStream[String] = env.fromsocketstream(...) lines.flatmap {line => line.split(" ").map(word => Word(word,1))}.window(Count.of(1000)).every(Count.of(100)).groupBy("word").sum("frequency").print() 12
13 Table API Execute SQL-like expressions on table data Tight integration with Java and Scala APIs Available for batch and streaming programs val orders = env.readcsvfile( ).as('oid, 'odate, 'shipprio).filter('shipprio === 5) val items = orders.join(lineitems).where('oid === 'id).select('oid, 'odate, 'shipprio, 'extdprice * (Literal(1.0f) - 'discnt) as 'revenue) val result = items.groupby('oid, 'odate, 'shipprio).select('oid, 'revenue.sum, 'odate, 'shipprio) 13
14 Libraries are emerging As part of the Apache Flink project Gelly: Graph processing and analysis Flink ML: Machine-learning pipelines and algorithms Libraries are built on APIs and can be mixed with them Outside of Apache Flink Apache SAMOA (incubating) Apache MRQL (incubating) Google DataFlow translator 14
15 Processing Engine WHAT IS HAPPENING INSIDE? 15
16 System Architecture Client (pre-flight) Master Flink Program Type extraction stack Cost-based optimizer Recovery metadata Task scheduling Workers Coordination Memory manager Data serialization stack Out-of-core algos Pipelined or Blocking Data Transfer 16
17 Cool technology inside Flink Batch and Streaming in one system Memory-safe execution Built-in data flow iterations Cost-based data flow optimizer Flexible windows on data streams Type extraction and serialization utilities Static code analysis on user functions and much more... 17
18 Pipelined Data Transfer STREAM AND BATCH IN ONE SYSTEM 18
19 Stream and Batch in one System Most systems are either stream or batch systems In the past, Flink focused on batch processing Flink s runtime has always done stream processing Operators pipeline data forward as soon as it is processed Some operators are blocking (such as sort) Stream API and operators are recent contributions Evolving very quickly under heavy development 19
20 Pipelined Data Transfer Pipelined data transfer has many benefits True stream and batch processing in one stack Avoids materialization of large intermediate results Better performance for many batch workloads Flink supports blocking data transfer as well 20
21 Pipelined Data Transfer Program Large Input map Interm. DataSet Small Input join Result Pipelined Large Input map Pipeline 2 No intermediate materialization! Execution Small Input Pipeline 1 Build HT Probe HT join Result 21
22 Memory Management and Out-of-Core Algorithms MEMORY SAFE EXECUTION 22
23 Memory-safe Execution Challenge of JVM-based data processing systems OutOfMemoryErrors due to data objects on the heap Flink runs complex data flows without memory tuning C++-style memory management Robust out-of-core algorithms 23
24 Managed Memory Active memory management Workers allocate 70% of JVM memory as byte arrays Algorithms serialize data objects into byte arrays In-memory processing as long as data is small enough Otherwise partial destaging to disk Benefits Safe memory bounds (no OutOfMemoryError) Scales to very large JVMs Reduced GC pressure 24
25 Going out-of-core Single-core join of 1KB Java objects beyond memory (4 GB) Blue bars are in-memory, orange bars (partially) out-of-core 25
26 Native Data Flow Iterations GRAPH ANALYSIS 26
27 Native Data Flow Iterations Many graph and ML algorithms require iterations Flink features native data flow iterations Loops are not unrolled But executed as cyclic data flows Two types of iterations Bulk iterations Delta iterations Performance competitive with specialized systems 27
28 Iterative Data Flows Flink runs iterations natively as cyclic data flows Operators are scheduled once Data is fed back through backflow channel Loop-invariant data is cached Operator state is preserved across iterations! Replace initial result interm. result join reduce interm. result result other datasets 28
29 # of elements updated Delta Iterations Delta iteration computes Delta update of solution set Work set for next iteration # of iterations Work set drives computations of next iteration Workload of later iterations significantly reduced Fast convergence Applicable to certain problem domains Graph processing 29
30 Iteration Performance 30 Iterations 61 Iterations (Convergence) PageRank on Twitter Follower Graph 30
31 Roadmap WHAT IS COMING NEXT? 31
32 Flink s Roadmap Mission: Unified stream and batch processing Exactly-once streaming semantics with flexible state checkpointing Extending the ML library Extending graph library Interactive programs Integration with Apache Zeppelin (incubating) SQL on top of expression language And much more 32
33 tl;dr What s worth to remember? Flink is general-purpose analytics system Unifies streaming and batch processing Expressive high-level APIs Robust and fast execution engine 34
34 I Flink, do you? ;-) If you find this exciting, get involved and start a discussion on Flink s ML or stay tuned by subscribing to news@flink.apache.org or on Twitter 35
35 36
36 BACKUP 37
37 Data Flow Optimizer Database-style optimizations for parallel data flows Optimizes all batch programs Optimizations Task chaining Join algorithms Re-use partitioning and sorting for later operations Caching for iterations 38
38 Data Flow Optimizer val orders = val lineitems = val filteredorders = orders.filter(o => dataformat.parse(l.shipdate).after(date)).filter(o => o.shipprio > 2) val lineitemsoforders = filteredorders.join(lineitems).where( orderid ).equalto( orderid ).apply((o,l) => new SelectedItem(o.orderDate, l.extdprice)) val pricesums = lineitemsoforders.groupby( orderdate ).sum( l.extdprice ); 39
39 Data Flow Optimizer Reduce sort[0,1] hash-part [0,1] Combine partial sort[0,1] Join Hybrid Hash Best plan depends on relative sizes of input files Reduce sort[0,1] Join Hybrid Hash buildht probe buildht probe broadcast forward hash-part [0] hash-part [0] Filter DataSource orders.tbl DataSource lineitem.tbl Filter DataSource orders.tbl DataSource lineitem.tbl 40
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 informationApache 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 informationThe Stratosphere Big Data Analytics Platform
The Stratosphere Big Data Analytics Platform Amir H. Payberah Swedish Institute of Computer Science amir@sics.se June 4, 2014 Amir H. Payberah (SICS) Stratosphere June 4, 2014 1 / 44 Big Data small data
More informationUnified 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 informationBig Data Research in Berlin BBDC and Apache Flink
Big Data Research in Berlin BBDC and Apache Flink Tilmann Rabl rabl@tu-berlin.de dima.tu-berlin.de bbdc.berlin 1 2013 Berlin Big Data Center All Rights Reserved DIMA 2015 Agenda About Data Management,
More informationHadoop2, Spark Big Data, real time, machine learning & use cases. Cédric Carbone Twitter : @carbone
Hadoop2, Spark Big Data, real time, machine learning & use cases Cédric Carbone Twitter : @carbone Agenda Map Reduce Hadoop v1 limits Hadoop v2 and YARN Apache Spark Streaming : Spark vs Storm Machine
More informationBig Data looks Tiny from the Stratosphere
Volker Markl http://www.user.tu-berlin.de/marklv volker.markl@tu-berlin.de Big Data looks Tiny from the Stratosphere Data and analyses are becoming increasingly complex! Size Freshness Format/Media Type
More informationApache MRQL (incubating): Advanced Query Processing for Complex, Large-Scale Data Analysis
Apache MRQL (incubating): Advanced Query Processing for Complex, Large-Scale Data Analysis Leonidas Fegaras University of Texas at Arlington http://mrql.incubator.apache.org/ 04/12/2015 Outline Who am
More informationMoving 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 informationReal 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 informationApache 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 informationHadoop 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 informationDeveloping 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 informationOutline. High Performance Computing (HPC) Big Data meets HPC. Case Studies: Some facts about Big Data Technologies HPC and Big Data converging
Outline High Performance Computing (HPC) Towards exascale computing: a brief history Challenges in the exascale era Big Data meets HPC Some facts about Big Data Technologies HPC and Big Data converging
More informationApache 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 informationHadoop 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 informationAccelerating Hadoop MapReduce Using an In-Memory Data Grid
Accelerating Hadoop MapReduce Using an In-Memory Data Grid By David L. Brinker and William L. Bain, ScaleOut Software, Inc. 2013 ScaleOut Software, Inc. 12/27/2012 H adoop has been widely embraced for
More informationSpark 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 informationbrief contents PART 1 BACKGROUND AND FUNDAMENTALS...1 PART 2 PART 3 BIG DATA PATTERNS...253 PART 4 BEYOND MAPREDUCE...385
brief contents PART 1 BACKGROUND AND FUNDAMENTALS...1 1 Hadoop in a heartbeat 3 2 Introduction to YARN 22 PART 2 DATA LOGISTICS...59 3 Data serialization working with text and beyond 61 4 Organizing and
More informationBeyond 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 informationHadoop 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 informationBig Data Processing with Google s MapReduce. Alexandru Costan
1 Big Data Processing with Google s MapReduce Alexandru Costan Outline Motivation MapReduce programming model Examples MapReduce system architecture Limitations Extensions 2 Motivation Big Data @Google:
More informationBig Data and Apache Hadoop s MapReduce
Big Data and Apache Hadoop s MapReduce Michael Hahsler Computer Science and Engineering Southern Methodist University January 23, 2012 Michael Hahsler (SMU/CSE) Hadoop/MapReduce January 23, 2012 1 / 23
More informationBig 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 informationBig Data and Scripting Systems beyond Hadoop
Big Data and Scripting Systems beyond Hadoop 1, 2, ZooKeeper distributed coordination service many problems are shared among distributed systems ZooKeeper provides an implementation that solves these avoid
More informationConjugating 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 informationBigData. 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 informationHadoop IST 734 SS CHUNG
Hadoop IST 734 SS CHUNG Introduction What is Big Data?? Bulk Amount Unstructured Lots of Applications which need to handle huge amount of data (in terms of 500+ TB per day) If a regular machine need to
More informationArchitectural patterns for building real time applications with Apache HBase. Andrew Purtell Committer and PMC, Apache HBase
Architectural patterns for building real time applications with Apache HBase Andrew Purtell Committer and PMC, Apache HBase Who am I? Distributed systems engineer Principal Architect in the Big Data Platform
More informationBig 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 informationScaling 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 informationTrend Micro Big Data Platform and Apache Bigtop. 葉 祐 欣 (Evans Ye) Big Data Conference 2015
Trend Micro Big Data Platform and Apache Bigtop 葉 祐 欣 (Evans Ye) Big Data Conference 2015 Who am I Apache Bigtop PMC member Apache Big Data Europe 2015 Speaker Software Engineer @ Trend Micro Develop big
More informationIn-memory data pipeline and warehouse at scale using Spark, Spark SQL, Tachyon and Parquet
In-memory data pipeline and warehouse at scale using Spark, Spark SQL, Tachyon and Parquet Ema Iancuta iorhian@gmail.com Radu Chilom radu.chilom@gmail.com Buzzwords Berlin - 2015 Big data analytics / machine
More informationHadoop & 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 informationHDP Hadoop From concept to deployment.
HDP Hadoop From concept to deployment. Ankur Gupta Senior Solutions Engineer Rackspace: Page 41 27 th Jan 2015 Where are you in your Hadoop Journey? A. Researching our options B. Currently evaluating some
More informationParallel Databases. Parallel Architectures. Parallelism Terminology 1/4/2015. Increase performance by performing operations in parallel
Parallel Databases Increase performance by performing operations in parallel Parallel Architectures Shared memory Shared disk Shared nothing closely coupled loosely coupled Parallelism Terminology Speedup:
More informationArchitectures 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 informationThe 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 informationHow Companies are! Using Spark
How Companies are! Using Spark And where the Edge in Big Data will be Matei Zaharia History Decreasing storage costs have led to an explosion of big data Commodity cluster software, like Hadoop, has made
More informationBuilding Scalable Big Data Infrastructure Using Open Source Software. Sam William sampd@stumbleupon.
Building Scalable Big Data Infrastructure Using Open Source Software Sam William sampd@stumbleupon. What is StumbleUpon? Help users find content they did not expect to find The best way to discover new
More informationApache Kylin Introduction Dec 8, 2014 @ApacheKylin
Apache Kylin Introduction Dec 8, 2014 @ApacheKylin Luke Han Sr. Product Manager lukhan@ebay.com @lukehq Yang Li Architect & Tech Leader yangli9@ebay.com Agenda What s Apache Kylin? Tech Highlights Performance
More informationImpala: A Modern, Open-Source SQL
Impala: A Modern, Open-Source SQL Engine Headline for Goes Hadoop Here Marcel Speaker Kornacker Name Subhead marcel@cloudera.com Goes Here CIDR 2015 Cloudera Impala Agenda Overview Architecture and Implementation
More informationHiBench Introduction. Carson Wang (carson.wang@intel.com) Software & Services Group
HiBench Introduction Carson Wang (carson.wang@intel.com) Agenda Background Workloads Configurations Benchmark Report Tuning Guide Background WHY Why we need big data benchmarking systems? WHAT What is
More informationDesigning 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 informationDRIVING INNOVATION THROUGH DATA ACCELERATING BIG DATA APPLICATION DEVELOPMENT WITH CASCADING
DRIVING INNOVATION THROUGH DATA ACCELERATING BIG DATA APPLICATION DEVELOPMENT WITH CASCADING Supreet Oberoi VP Field Engineering, Concurrent Inc GET TO KNOW CONCURRENT Leader in Application Infrastructure
More informationBig 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 informationFast and Expressive Big Data Analytics with Python. Matei Zaharia UC BERKELEY
Fast and Expressive Big Data Analytics with Python Matei Zaharia UC Berkeley / MIT UC BERKELEY spark-project.org What is Spark? Fast and expressive cluster computing system interoperable with Apache Hadoop
More informationUnified 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 informationLambda 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 informationHow to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning
How to use Big Data in Industry 4.0 implementations LAURI ILISON, PhD Head of Big Data and Machine Learning Big Data definition? Big Data is about structured vs unstructured data Big Data is about Volume
More informationThe Big Data Ecosystem at LinkedIn. Presented by Zhongfang Zhuang
The Big Data Ecosystem at LinkedIn Presented by Zhongfang Zhuang Based on the paper The Big Data Ecosystem at LinkedIn, written by Roshan Sumbaly, Jay Kreps, and Sam Shah. The Ecosystems Hadoop Ecosystem
More informationDell In-Memory Appliance for Cloudera Enterprise
Dell In-Memory Appliance for Cloudera Enterprise Hadoop Overview, Customer Evolution and Dell In-Memory Product Details Author: Armando Acosta Hadoop Product Manager/Subject Matter Expert Armando_Acosta@Dell.com/
More informationBig Graph Analytics on Neo4j with Apache Spark. Michael Hunger Original work by Kenny Bastani Berlin Buzzwords, Open Stage
Big Graph Analytics on Neo4j with Apache Spark Michael Hunger Original work by Kenny Bastani Berlin Buzzwords, Open Stage My background I only make it to the Open Stages :) Probably because Apache Neo4j
More informationPulsar 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 informationUpcoming Announcements
Enterprise Hadoop Enterprise Hadoop Jeff Markham Technical Director, APAC jmarkham@hortonworks.com Page 1 Upcoming Announcements April 2 Hortonworks Platform 2.1 A continued focus on innovation within
More informationHadoop in Social Network Analysis - overview on tools and some best practices - Headline Goes Here
Hadoop in Social Network Analysis - overview on tools and some best practices - Headline Goes Here Speaker Name or Subhead Goes Here GridKa School 2013, Karlsruhe 2013-08-27 Mirko Kämpf mirko@cloudera.com
More informationBig Data Frameworks: Scala and Spark Tutorial
Big Data Frameworks: Scala and Spark Tutorial 13.03.2015 Eemil Lagerspetz, Ella Peltonen Professor Sasu Tarkoma These slides: http://is.gd/bigdatascala www.cs.helsinki.fi Functional Programming Functional
More informationArchitectures 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 informationBIG DATA What it is and how to use?
BIG DATA What it is and how to use? Lauri Ilison, PhD Data Scientist 21.11.2014 Big Data definition? There is no clear definition for BIG DATA BIG DATA is more of a concept than precise term 1 21.11.14
More informationHow To Scale Out Of A Nosql Database
Firebird meets NoSQL (Apache HBase) Case Study Firebird Conference 2011 Luxembourg 25.11.2011 26.11.2011 Thomas Steinmaurer DI +43 7236 3343 896 thomas.steinmaurer@scch.at www.scch.at Michael Zwick DI
More informationImplement Hadoop jobs to extract business value from large and varied data sets
Hadoop Development for Big Data Solutions: Hands-On You Will Learn How To: Implement Hadoop jobs to extract business value from large and varied data sets Write, customize and deploy MapReduce jobs to
More informationIntroduction 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 informationBig Data With Hadoop
With Saurabh Singh singh.903@osu.edu The Ohio State University February 11, 2016 Overview 1 2 3 Requirements Ecosystem Resilient Distributed Datasets (RDDs) Example Code vs Mapreduce 4 5 Source: [Tutorials
More informationImpala: A Modern, Open-Source SQL Engine for Hadoop. Marcel Kornacker Cloudera, Inc.
Impala: A Modern, Open-Source SQL Engine for Hadoop Marcel Kornacker Cloudera, Inc. Agenda Goals; user view of Impala Impala performance Impala internals Comparing Impala to other systems Impala Overview:
More informationGeneral purpose Distributed Computing using a High level Language. Michael Isard
Dryad and DryadLINQ General purpose Distributed Computing using a High level Language Michael Isard Microsoft Research Silicon Valley Distributed Data Parallel Computing Workloads beyond standard SQL,
More informationA Brief Introduction to Apache Tez
A Brief Introduction to Apache Tez Introduction It is a fact that data is basically the new currency of the modern business world. Companies that effectively maximize the value of their data (extract value
More informationProgramming 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 informationStreaming 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 informationFederated SQL on Hadoop and Beyond: Leveraging Apache Geode to Build a Poor Man's SAP HANA. by Christian Tzolov @christzolov
Federated SQL on Hadoop and Beyond: Leveraging Apache Geode to Build a Poor Man's SAP HANA by Christian Tzolov @christzolov Whoami Christian Tzolov Technical Architect at Pivotal, BigData, Hadoop, SpringXD,
More informationThe Internet of Things and Big Data: Intro
The Internet of Things and Big Data: Intro John Berns, Solutions Architect, APAC - MapR Technologies April 22 nd, 2014 1 What This Is; What This Is Not It s not specific to IoT It s not about any specific
More informationReference Architecture, Requirements, Gaps, Roles
Reference Architecture, Requirements, Gaps, Roles The contents of this document are an excerpt from the brainstorming document M0014. The purpose is to show how a detailed Big Data Reference Architecture
More informationBig Data and Scripting Systems build on top of Hadoop
Big Data and Scripting Systems build on top of Hadoop 1, 2, Pig/Latin high-level map reduce programming platform Pig is the name of the system Pig Latin is the provided programming language Pig Latin is
More informationSpark 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 informationCSE-E5430 Scalable Cloud Computing Lecture 2
CSE-E5430 Scalable Cloud Computing Lecture 2 Keijo Heljanko Department of Computer Science School of Science Aalto University keijo.heljanko@aalto.fi 14.9-2015 1/36 Google MapReduce A scalable batch processing
More informationHADOOP IN ENTERPRISE FUTURE-PROOF YOUR BIG DATA INVESTMENTS WITH CASCADING. Supreet Oberoi Nov. 4-6, 2014 Big Data Expo Santa Clara
DRIVING INNOVATION THROUGH DATA HADOOP IN ENTERPRISE FUTURE-PROOF YOUR BIG DATA INVESTMENTS WITH CASCADING Supreet Oberoi Nov. 4-6, 2014 Big Data Expo Santa Clara ABOUT ME I am a Data Engineer, not a Data
More informationA REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM
A REVIEW PAPER ON THE HADOOP DISTRIBUTED FILE SYSTEM Sneha D.Borkar 1, Prof.Chaitali S.Surtakar 2 Student of B.E., Information Technology, J.D.I.E.T, sborkar95@gmail.com Assistant Professor, Information
More informationSystems Engineering II. Pramod Bhatotia TU Dresden pramod.bhatotia@tu- dresden.de
Systems Engineering II Pramod Bhatotia TU Dresden pramod.bhatotia@tu- dresden.de About me! Since May 2015 2015 2012 Research Group Leader cfaed, TU Dresden PhD Student MPI- SWS Research Intern Microsoft
More informationInternational Journal of Advancements in Research & Technology, Volume 3, Issue 2, February-2014 10 ISSN 2278-7763
International Journal of Advancements in Research & Technology, Volume 3, Issue 2, February-2014 10 A Discussion on Testing Hadoop Applications Sevuga Perumal Chidambaram ABSTRACT The purpose of analysing
More informationHDP Enabling the Modern Data Architecture
HDP Enabling the Modern Data Architecture Herb Cunitz President, Hortonworks Page 1 Hortonworks enables adoption of Apache Hadoop through HDP (Hortonworks Data Platform) Founded in 2011 Original 24 architects,
More informationOpen source software framework designed for storage and processing of large scale data on clusters of commodity hardware
Open source software framework designed for storage and processing of large scale data on clusters of commodity hardware Created by Doug Cutting and Mike Carafella in 2005. Cutting named the program after
More informationBig Data Rethink Algos and Architecture. Scott Marsh Manager R&D Personal Lines Auto Pricing
Big Data Rethink Algos and Architecture Scott Marsh Manager R&D Personal Lines Auto Pricing Agenda History Map Reduce Algorithms History Google talks about their solutions to their problems Map Reduce:
More informationHadoop Job Oriented Training Agenda
1 Hadoop Job Oriented Training Agenda Kapil CK hdpguru@gmail.com Module 1 M o d u l e 1 Understanding Hadoop This module covers an overview of big data, Hadoop, and the Hortonworks Data Platform. 1.1 Module
More informationAli Ghodsi Head of PM and Engineering Databricks
Making Big Data Simple Ali Ghodsi Head of PM and Engineering Databricks Big Data is Hard: A Big Data Project Tasks Tasks Build a Hadoop cluster Challenges Clusters hard to setup and manage Build a data
More informationSpark: 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 informationManaging large clusters resources
Managing large clusters resources ID2210 Gautier Berthou (SICS) Big Processing with No Locality Job( /crawler/bot/jd.io/1 ) submi t Workflow Manager Compute Grid Node Job This doesn t scale. Bandwidth
More informationKafka & Redis for Big Data Solutions
Kafka & Redis for Big Data Solutions Christopher Curtin Head of Technical Research @ChrisCurtin About Me 25+ years in technology Head of Technical Research at Silverpop, an IBM Company (14 + years at Silverpop)
More informationBig Data Primer. 1 Why Big Data? Alex Sverdlov alex@theparticle.com
Big Data Primer Alex Sverdlov alex@theparticle.com 1 Why Big Data? Data has value. This immediately leads to: more data has more value, naturally causing datasets to grow rather large, even at small companies.
More informationApache Mahout's new DSL for Distributed Machine Learning. Sebastian Schelter GOTO Berlin 11/06/2014
Apache Mahout's new DSL for Distributed Machine Learning Sebastian Schelter GOO Berlin /6/24 Overview Apache Mahout: Past & Future A DSL for Machine Learning Example Under the covers Distributed computation
More informationUnified 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 information1. The orange button 2. Audio Type 3. Close apps 4. Enlarge my screen 5. Headphones 6. Questions Pane. SparkR 2
SparkR 1. The orange button 2. Audio Type 3. Close apps 4. Enlarge my screen 5. Headphones 6. Questions Pane SparkR 2 Lecture slides and/or video will be made available within one week Live Demonstration
More informationData Warehousing and Analytics Infrastructure at Facebook. Ashish Thusoo & Dhruba Borthakur athusoo,dhruba@facebook.com
Data Warehousing and Analytics Infrastructure at Facebook Ashish Thusoo & Dhruba Borthakur athusoo,dhruba@facebook.com Overview Challenges in a Fast Growing & Dynamic Environment Data Flow Architecture,
More informationDeveloping MapReduce Programs
Cloud Computing Developing MapReduce Programs Dell Zhang Birkbeck, University of London 2015/16 MapReduce Algorithm Design MapReduce: Recap Programmers must specify two functions: map (k, v) * Takes
More informationMachine- Learning Summer School - 2015
Machine- Learning Summer School - 2015 Big Data Programming David Franke Vast.com hbp://www.cs.utexas.edu/~dfranke/ Goals for Today Issues to address when you have big data Understand two popular big data
More informationOpenbus Documentation
Openbus Documentation Release 1 Produban February 17, 2014 Contents i ii An open source architecture able to process the massive amount of events that occur in a banking IT Infraestructure. Contents:
More informationBig Data Analysis: Apache Storm Perspective
Big Data Analysis: Apache Storm Perspective Muhammad Hussain Iqbal 1, Tariq Rahim Soomro 2 Faculty of Computing, SZABIST Dubai Abstract the boom in the technology has resulted in emergence of new concepts
More informationBig 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 informationHadoop-based Open Source ediscovery: FreeEed. (Easy as popcorn)
+ Hadoop-based Open Source ediscovery: FreeEed (Easy as popcorn) + Hello! 2 Sujee Maniyam & Mark Kerzner Founders @ Elephant Scale consulting and training around Hadoop, Big Data technologies Enterprise
More informationSpark 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 informationCollaborative Big Data Analytics. Copyright 2012 EMC Corporation. All rights reserved.
Collaborative Big Data Analytics 1 Big Data Is Less About Size, And More About Freedom TechCrunch!!!!!!!!! Total data: bigger than big data 451 Group Findings: Big Data Is More Extreme Than Volume Gartner!!!!!!!!!!!!!!!
More informationData Security in Hadoop
Data Security in Hadoop Eric Mizell Director, Solution Engineering Page 1 What is Data Security? Data Security for Hadoop allows you to administer a singular policy for authentication of users, authorize
More information