Apache-Flink-What-How-Why-Who-Where-by-Slim-Baltagi

1. Apache Flink: What, How, Why, Who, Where? By @SlimBaltagi Director of Big Data Engineering Capital One 1 New York City (NYC) Apache Flink Meetup Civic Hall, NYC February 2nd, 2016 New York City (NYC) Apache Flink Meetup Civic Hall, NYC February 2nd, 2016

2. Agenda I. What is Apache Flink stack and how it fits into the Big Data ecosystem? II. How Apache Flink integrates with Hadoop and other open source tools? III. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? IV. Who is using Apache Flink? V. Where to learn more about Apache Flink? 2

3. I. What is Apache Flink stack and how it fits into the Big Data ecosystem? 1. What is Apache Flink? 2. What is Flink Execution Engine? 3. What are Flink APIs? 4. What are Flink Domain Specific Libraries? 5. What is Flink Architecture? 6. What is Flink Programming Model? 7. What are Flink tools? 3

4. 1. What is Apache Flink? 1.1 Apache project with a cool logo! 1.2 Project that evolved the concept of a multi- purpose Big Data analytics framework 1.3 Project with a unique vision and philosophy 1.4 Only Hybrid ( Real-Time streaming + Batch) engine supporting many use cases 1.5 Major contributor to the movement of unification of streaming and batch 1.6 The 4G of Big Data Analytics frameworks 4

5. 1.1 Apache project with a cool logo!  Apache Flink, like Apache Hadoop and Apache Spark, is a community-driven open source framework for distributed Big Data Analytics.  Apache Flink has its origins in a research project called Stratosphere of which the idea was conceived in late 2008 by professor Volker Markl from the Technische Universität Berlin in Germany.  Flink joined the Apache incubator in April 2014 and graduated as an Apache Top Level Project (TLP) in December 2014.  dataArtisans (data-artisans.com) is a German start-up company based in Berlin and is leading the development of Apache Flink. 5

6. 1.1 Apache project with a cool logo Squirrel is an animal! This reflects the harmony with other animals in the Hadoop ecosystem (Zoo): elephant, pig, python, camel, … A squirrel is swift and agile This reflects the meaning of the word ‘Flink’: German for “nimble, swift, speedy” Red color of the body This reflects the roots of the project at German universities: In harmony with red squirrels in Germany Colorful tail This reflects an open source project as the colors match the ones of the feather symbolizing the Apache Software Foundation

7. 1.2 Project that evolved the concept of a multi- purpose Big Data analytics framework 7 What is a typical Big Data Analytics Stack: Hadoop, Spark, Flink, …?

8. 1.2 Project that evolved the concept of a multi- purpose Big Data analytics framework Apache Flink, written in Java and Scala, consists of: 1. Big data processing engine: distributed and scalable streaming dataflow engine 2. Several APIs in Java/Scala/Python: • DataSet API – Batch processing • DataStream API – Real-Time streaming analytics 3. Domain-Specific Libraries: • FlinkML: Machine Learning Library for Flink • Gelly: Graph Library for Flink • Table: Relational Queries • FlinkCEP: Complex Event Processing for Flink8

9. What is Apache Flink stack? Gelly Table HadoopM/R SAMOA DataSet (Java/Scala/Python) Batch Processing DataStream (Java/Scala) Stream Processing FlinkML Local Single JVM Embedded Docker Cluster Standalone YARN, Mesos (WIP) Cloud Google’s GCE Amazon’s EC2 IBM Docker Cloud, … GoogleDataflow Dataflow(WiP) MRQL Table Cascading Runtime - Distributed Streaming Dataflow Zeppelin DEPLOYSYSTEMAPIs&LIBRARIESSTORAGE Files Local HDFS S3, Azure Storage Tachyon Databases MongoDB HBase SQL … Streams Flume Kafka RabbitMQ … Batch Optimizer Stream Builder Storm Gelly-Stream

10. • Declarativity • Query optimization • Efficient parallel in- memory and out-of- core algorithms • Massive scale-out • User Defined Functions • Complex data types • Schema on read • Real-Time Streaming • Iterations • Memory Management • Advanced Dataflows • General APIs Draws on concepts from MPP Database Technology Draws on concepts from Hadoop MapReduce Technology Add 1.3 Project with a unique vision and philosophy Apache Flink’s original vision was getting the best from both worlds: MPP Technology and Hadoop MapReduce Technologies:

11. 1.3 Project with a unique vision and philosophy All streaming all the time: execute everything as streams including batch!! Write like a programming language, execute like a database. Alleviate the user from a lot of the pain of: • manually tuning memory assignment to intermediate operators • dealing with physical execution concepts (e.g., choosing between broadcast and partitioned joins, reusing partitions). 11

12. 1.3 Project with a unique vision and philosophy Little configuration required • Requires no memory thresholds to configure – Flink manages its own memory • Requires no complicated network configurations – Pipelining engine requires much less memory for data exchange • Requires no serializers to be configured – Flink handles its own type extraction and data representation Little tuning required: Programs can be adjusted to data automatically – Flink’s optimizer can choose execution strategies automatically 12

13. 1.3 Project with a unique vision and philosophy Support for many file systems: • Flink is File System agnostic. BYOS: Bring Your Own Storage Support for many deployment options: • Flink is agnostic to the underlying cluster infrastructure. BYOC: Bring Your Own Cluster Be a good citizen of the Hadoop ecosystem • Good integration with YARN Preserve your investment in your legacy Big Data applications: Run your legacy code on Flink’s powerful engine using Hadoop and Storm compatibility layers and Cascading adapter. 13

14. 1.3 Project with a unique vision and philosophy Native Support of many use cases on top of the same streaming engine • Batch • Real-Time streaming • Machine learning • Graph processing • Relational queries Support building complex data pipelines leveraging native libraries without the need to combine and manage external ones. 14

15. 1.4 The only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine natively supporting many use cases: Real-Time stream processing Machine Learning at scale Graph AnalysisBatch Processing 15

16. 1.5 Major contributor to the movement of unification of streaming and batch Dataflow proposal for incubation has been renamed to Apache Beam ( for combination of Batch and Stream) https://wiki.apache.org/incubator/BeamProposal Apache Beam was accepted to the Apache incubation on February 1st, 2016 http://incubator.apache.org/projects/beam.html Dataflow/Beam & Spark: A Programming Model Comparison, February 3rd, 2016https://cloud.google.com/dataflow/blog/dataflow-beam-and-spark-comparison By Tyler Akidau & Frances Perry, Software Engineers, Apache Beam Committers 16

17. 1.5 Major contributor to the movement of unification of streaming and batch Apache Flink includes DataFlow on Flink http://data- artisans.com/dataflow-proposed-as-apache-incubator-project/  Keynotes of the Flink Forward 2015 conference: • Keynote on October 12th, 2015 by Kostas Tzoumas and Stephan Ewen of dataArtisanshttp://www.slideshare.net/FlinkForward/k-tzoumas-s- ewen-flink-forward-keynote/ • Keynote on October 13th, 2015 by William Vambenepe of Googlehttp://www.slideshare.net/FlinkForward/william-vambenepe- google-cloud-dataflow-and-flink-stream-processing-by-default 17

18. 1.6 The 4G of Big Data Analytics frameworks Apache Flink is not YABDAF (Yet Another Big Data Analytics Framework)! Flink brings many technical innovations and a unique vision and philosophy that distinguish it from:  Other multi-purpose Big Data analytics frameworks such as Apache Hadoop and Apache Spark  Single-purpose Big Data Analytics frameworks such as Apache Storm Apache Flink is the 4G (4th Generation) of Big Data Analytics frameworks succeeding to Apache Spark. 18

19. Apache Flink as the 4G of Big Data Analytics  Batch  Batch  Interactive  Batch  Interactive  Near-Real Time Streaming  Iterative processing  Hybrid (Streaming +Batch)  Interactive  Real-Time Streaming  Native Iterative processing MapReduce Direct Acyclic Graphs (DAG) Dataflows RDD: Resilient Distributed Datasets Cyclic Dataflows 1st Generation (1G) 2ndGeneration (2G) 3rd Generation (3G) 4th Generation (4G) 19

20. How Big Data Analytics engines evolved?  The evolution of Massive-Scale Data Processing Tyler Akidau, Google. Strata + Hadoop World, Singapore, December 2, 2015. Slides: https://docs.google.com/presentation/d/10vs2PnjynYMtDpwFsqmSePtMnf JirCkXcHZ1SkwDg-s/present?slide=id.g63ca2a7cd_0_527 The world beyond batch: Streaming 101, Tyler Akidau, Google, August 5, 2015 http://radar.oreilly.com/2015/08/the-world-beyond-batch-streaming- 101.html Streaming 102, Tyler Akidau, Google, January 20, 2016 https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-102 It covers topics like event-time vs. processing-time, windowing, watermarks, triggers, and accumulation. 20

21. 2. What is Flink Execution Engine? The core of Flink is a distributed and scalable streaming dataflow engine with some unique features: 1. True streaming capabilities: Execute everything as streams 2. Versatile: Engine allows to run all existing MapReduce, Cascading, Storm, Google DataFlow applications 3. Native iterative execution: Allow some cyclic dataflows 4. Handling of mutable state 5. Custom memory manager: Operate on managed memory 6. Cost-Based Optimizer: for both batch and stream processing 21

22. 3. Flink APIs 3.1 DataSet API for static data - Java, Scala, and Python 3.2 DataStream API for unbounded real-time streams - Java and Scala 22

23. 3.1 DataSet API – Batch processing case class Word (word: String, frequency: Int) val env = StreamExecutionEnvironment.getExecutionEnvironment() val lines: DataStream[String] = env.fromSocketStream(...) lines.flatMap {line => line.split(" ") .map(word => Word(word,1))} .window(Time.of(5,SECONDS)).every(Time.of(1,SECONDS)) .keyBy("word").sum("frequency") .print() env.execute() val env = ExecutionEnvironment.getExecutionEnvironment() val lines: DataSet[String] = env.readTextFile(...) lines.flatMap {line => line.split(" ") .map(word => Word(word,1))} .groupBy("word").sum("frequency") .print() env.execute() DataSet API (batch): WordCount DataStream API (streaming): Window WordCount 23

24. 3.2 DataStream API – Real-Time Streaming Analytics Flink Streaming provides high-throughput, low-latency stateful stream processing system with rich windowing semantics. Streaming Fault-Tolerance allows Exactly-once processing delivery guarantees for Flink streaming programs that analyze streaming sources persisted by Apache Kafka.  Flink Streaming provides native support for iterative stream processing. Data streams can be transformed and modified using high-level functions similar to the ones provided by the batch processing API. 24

25. 3.2 DataStream API – Real-Time Streaming Analytics Flink being based on a pipelined (streaming) execution engine akin to parallel database systems allows to: • implement true streaming & batch • integrate streaming operations with rich windowing semantics seamlessly • process streaming operations in a pipelined way with lower latency than micro-batch architectures and without the complexity of lambda architectures. It has built-in connectors to many data sources like Flume, Kafka, Twitter, RabbitMQ, etc 25

26. 3.2 DataStream API – Real-Time Streaming Analytics Apache Flink: streaming done right. Till Rohrmann. January 31, 2016 https://fosdem.org/2016/schedule/event/hpc_bigdata_flink_streaming/ Web resources about stream processing with Apache Flink at the Flink Knowledge Base http://sparkbigdata.com/component/tags/tag/49-flink-streaming 26

27. 4. Flink Domain Specific Libraries 4.1 FlinkML – Machine Learning Library 4.2 Table – Relational queries 4.3 Gelly – Graph Analytics for Flink 4.4 FlinkCEP: Complex Event Processing for Flink 27

28. 4.1 FlinkML - Machine Learning Library  FlinkML is the Machine Learning (ML) library for Flink. It is written in Scala and was added in March 2015.  FlinkML aims to provide: • an intuitive API • scalable ML algorithms • tools that help minimize glue code in end-to-end ML applications  FlinkML will allow data scientists to: • test their models locally using subsets of data • use the same code to run their algorithms at a much larger scale in a cluster setting. 28

29. 4.1 FlinkML FlinkML unique features are: 1. Exploiting the in-memory data streaming nature of Flink. 2. Natively executing iterative processing algorithms which are common in Machine Learning. 3. Streaming ML designed specifically for data streams. FlinkML: Large-scale machine learning with Apache Flink, Theodore Vasiloudis. October 21, 2015 Slides: https://sics.app.box.com/s/044omad6200pchyh7ptbyxkwvcvaiowu Video: https://www.youtube.com/watch?v=k29qoCm4c_k&feature=youtu.be  Check more FlinkML web resources at the Apache Flink Knowledge Base: http://sparkbigdata.com/component/tags/tag/51-29

30. 4.2 Table – Relational Queries Table API, written in Scala , allows specifying operations using SQL-like expressions instead of manipulating DataSet or DataStream.  Table API can be used in both batch (on structured data sets) and streaming programs (on structured data streams).http://ci.apache.org/projects/flink/flink-docs- master/libs/table.html  Flink Table web resources at the Apache Flink Knowledge Base: http://sparkbigdata.com/component/tags/tag/52- flink-table 30

31. 4.2 Table API – Relational Queries val customers = envreadCsvFile(…).as('id, 'mktSegment) .filter("mktSegment = AUTOMOBILE") val orders = env.readCsvFile(…) .filter( o => dateFormat.parse(o.orderDate).before(date) ) .as("orderId, custId, orderDate, shipPrio") val items = orders .join(customers).where("custId = id") .join(lineitems).where("orderId = id") .select("orderId, orderDate, shipPrio, extdPrice * (Literal(1.0f) – discount) as revenue") val result = items .groupBy("orderId, orderDate, shipPrio") .select("orderId, revenue.sum, orderDate, shipPrio") Table API (queries) 31

32. 4.3 Gelly – Graph Analytics for Flink Gelly is Flink's large-scale graph processing API, available in Java and Scala, which leverages Flink's efficient delta iterations to map various graph processing models (vertex-centric and gather-sum- apply) to dataflows. Gelly provides: • A set of methods and utilities to create, transform and modify graphs • A library of graph algorithms which aims to simplify the development of graph analysis applications • Iterative graph algorithms are executed leveraging mutable state 32

33. 4.3 Gelly – Graph Analytics for Flink Gelly allows Flink users to perform end-to-end data analysis, without having to build complex pipelines and combine different systems. It can be seamlessly combined with Flink's DataSet API, which means that pre-processing, graph creation, graph analysis and post-processing can be done in the same application. Gelly documentation https://ci.apache.org/projects/flink/flink-docs- master/libs/gelly_guide.html Introducing Gelly: Graph Processing with Apache Flink http://flink.apache.org/news/2015/08/24/introducing-flink-gelly.html Check out more Gelly web resources at the Apache Flink Knowledge Base: http://sparkbigdata.com/component/tags/tag/50-gelly33

34. 4.3 Gelly – Graph Analytics for Flink Single-pass Graph Streaming Analytics with Apache Flink. Vasia Kalavri & Paris Carbone. January 31, 2016 FOSDEM'16. Brussels, BELGIUM. • Talk description :https://fosdem.org/2016/schedule/event/graph_processing_apache_flin k/ • Slides: http://www.slideshare.net/vkalavri/gellystream-singlepass- graph-streaming-analytics-with-apache-flink Gelly free training! http://www.slideshare.net/FlinkForward/vasia- kalavri-training-gelly-school http://gellyschool.com/ 34

35. 4.4 FlinkCEP: Complex Event Processing for Flink FlinkCEP is the complex event processing library for Flink. It allows you to easily detect complex event patterns in a stream of endless data. Complex events can then be constructed from matching sequences. This gives you the opportunity to quickly get hold of what’s really important in your data. https://ci.apache.org/projects/flink/flink-docs- master/apis/streaming/libs/cep.html 35

36. 5. What is Flink Architecture?  Flink implements the Kappa Architecture: run batch programs on a streaming system.  References about the Kappa Architecture: • Questioning the Lambda Architecture - Jay Kreps , July 2nd, 2014 http://radar.oreilly.com/2014/07/questioning-the-lambda- architecture.html • Turning the database inside out with Apache Samza -Martin Kleppmann, March 4th, 2015 o http://www.youtube.com/watch?v=fU9hR3kiOK0 (VIDEO) o http://martin.kleppmann.com/2015/03/04/turning-the-database-inside- out.html(TRANSCRIPT) o http://blog.confluent.io/2015/03/04/turning-the-database-inside-out-with- apache-samza/ (BLOG) 36

37. 5. What is Flink Architecture? 5.1 Client 5.2 Master (Job Manager) 5.3 Worker (Task Manager) 37

38. 5.1 Client  Type extraction  Optimize: in all APIs not just SQL queries as in Spark  Construct job Dataflow graph  Pass job Dataflow graph to job manager  Retrieve job results Job Manager Client case class Path (from: Long, to: Long) val tc = edges.iterate(10) { paths: DataSet[Path] => val next = paths .join(edges) .where("to") .equalTo("from") { (path, edge) => Path(path.from, edge.to) } .union(paths) .distinct() next } Optimizer Type extraction Data Source orders.tbl Filter Map DataSource lineitem.tbl Join Hybrid Hash buildHT probe hash-part [0] hash-part [0] GroupRed sort forward 38

39. 5.2 Job Manager (JM) with High Availability  Parallelization: Create Execution Graph  Scheduling: Assign tasks to task managers  State tracking: Supervise the execution Job Manager Data Source orders.tbl Filter Map DataSource lineitem.tbl Join Hybrid Hash buildHT probe hash-part [0] hash-part [0] GroupRed sort forwar d Task Manager Task Manager Task Manager Task Manager Data Source orders.tbl Filter Map DataSour ce lineitem.tbl Join Hybrid Hash build HT prob e hash-part [0] hash-part [0] GroupRed sort forwar d Data Source orders.tbl Filter Map DataSour ce lineitem.tbl Join Hybrid Hash build HT prob e hash-part [0] hash-part [0] GroupRed sort forwar d Data Source orders.tbl Filter Map DataSour ce lineitem.tbl Join Hybrid Hash build HT prob e hash-part [0] hash-part [0] GroupRed sort forwar d Data Source orders.tbl Filter Map DataSource lineitem.tbl Join Hybrid Hash build HT prob e hash-part [0] hash-part [0] GroupRed sort forwar d 39

40. 5.3 Task Manager ( TM)  Operations are split up into tasks depending on the specified parallelism  Each parallel instance of an operation runs in a separate task slot  The scheduler may run several tasks from different operators in one task slot Task Manager S l o t Task ManagerTask Manager S l o t S l o t 40

41. 6. What is Flink Programming Model?  DataSet and DataStream as programming abstractions are the foundation for user programs and higher layers.  Flink extends the MapReduce model with new operators that represent many common data analysis tasks more naturally and efficiently.  All operators will start working in memory and gracefully go out of core under memory pressure. 41

42. 6.1 DataSet DataSet: abstraction for distributed data and the central notion of the batch programming API Files and other data sources are read into DataSets • DataSet<String> text = env.readTextFile(…) Transformations on DataSets produce DataSets • DataSet<String> first = text.map(…) DataSets are printed to files or on stdout • first.writeAsCsv(…) Computation is specified as a sequence of lazily evaluated transformations Execution is triggered with env.execute() 42

43. 6.1 DataSet Used for Batch Processing Data Set Operation Data Set Source Example: Map and Reduce operation Sink b h 2 1 3 5 7 4 … … Map Reduce a 1 2 … 43

44. 6.2 DataStream Real-time event streams Data Stream Operation Data Stream Source Sink Stock Feed Name Price Microsoft 124 Google 516 Apple 235 … … Alert if Microsoft > 120 Write event to database Sum every 10 seconds Alert if sum > 10000 Microsoft 124 Google 516 Apple 235 Microsoft 124 Google 516 Apple 235 Example: Stream from a live stock feed 44

45. 7. What are Apache Flink tools? 7.1 Command-Line Interface (CLI) 7.2 Web Submission Client 7.3 Job Manager Web Interface 7.4 Interactive Scala Shell 7.5 Zeppelin Notebook 45

46. 7.1 Command-Line Interface (CLI)  Flink provides a CLI to run programs that are packaged as JAR files, and control their execution.  bin/flink has 4 major actions • run #runs a program. • info #displays information about a program. • list #lists scheduled and running jobs • cancel #cancels a running job. Example: ./bin/flink info ./examples/KMeans.jar See CLI usage and related examples: https://ci.apache.org/projects/flink/flink-docs-master/apis/cli.html 46

47. 7.2 Web Submission Client 47

48. 7.2 Web Submission Client Flink provides a web interface to: • Upload programs • Execute programs • Inspect their execution plans • Showcase programs • Debug execution plans • Demonstrate the system as a whole The web interface runs on port 8080 by default. To specify a custom port set the webclient.port property in the ./conf/flink.yaml configuration file. 48

49. 7.3 Job Manager Web Interface Overall system status Job execution details Task Manager resource utilization 49

50. 7.3 Job Manager Web Interface The JobManager web frontend allows to : • Track the progress of a Flink program as all status changes are also logged to the JobManager’s log file. • Figure out why a program failed as it displays the exceptions of failed tasks and allow to figure out which parallel task first failed and caused the other tasks to cancel the execution. 50

51. 7.4 Interactive Scala Shell bin/start-scala-shell.sh --host localhost --port 6123 51

52. 7.4 Interactive Scala Shell Flink comes with an Interactive Scala Shell - REPL ( Read Evaluate Print Loop ) :  ./bin/start-scala-shell.sh  Interactive queries  Let’s you explore data quickly  It can be used in a local setup as well as in a cluster setup.  The Flink Shell comes with command history and auto completion.  Complete Scala API available  So far only batch mode is supported. There is plan to add streaming in the future: https://ci.apache.org/projects/flink/flink-docs-master/scala_shell.html 52

53. 7.5 Zeppelin Notebook http://localhost:8080/ 53

54. 7.5 Zeppelin Notebook Web-based interactive computation environment Collaborative data analytics and visualization tool Combines rich text, execution code, plots and rich media Exploratory data science Saving and replaying of written code Storytelling 54

55. Agenda I. What is Apache Flink stack and how it fits into the Big Data ecosystem? II. How Apache Flink integrates with Hadoop and other open source tools? III. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? IV. Who is using Apache Flink? V. Where to learn more about Apache Flink? 55

56. II. How Apache Flink integrates with Hadoop and other open source tools? Service Open Source Tool Storage/Servi ng Layer Data Formats Data Ingestion Services Resource Management 56

57. II. How Apache Flink integrates with Hadoop and other open source tools? Flink integrates well with other open source tools for data input and output as well as deployment. Flink allows to run legacy Big Data applications: MapReduce, Cascading and Storm applications Flink integrates with other open source tools 1. Data Input / Output 2. Deployment 3. Legacy Big Data applications 4. Other tools 57

58. 1. Data Input / Output HDFS to read and write. Secure HDFS support Reuse data types (that implement Writables interface) Amazon S3 Microsoft Azure Storage MapR-FS Flink + Tachyon http://tachyon-project.org/ Running Apache Flink on Tachyon http://tachyon-project.org/Running- Flink-on-Tachyon.html  Flink + XtreemFS http://www.xtreemfs.org/ 58

59. 1. Data Input / Output  Crunching Parquet Files with Apache Flink https://medium.com/@istanbul_techie/crunching-parquet-files-with-apache-flink- 200bec90d8a7 Here are some examples of how to read/write data from/to HBase: https://github.com/apache/flink/tree/master/flink-staging/flink- hbase/src/test/java/org/apache/flink/addons/hbase/example Using MongoDB with Flink: http://flink.apache.org/news/2014/01/28/querying_mongodb.html https://github.com/m4rcsch/flink-mongodb-example 59

60. 1. Data Input / Output Apache Kafka, a system that provides durability and pub/sub functionality for data streams. Kafka + Flink: A practical, how-to guide. Robert Metzger and Kostas Tzoumas, September 2, 2015 http://data-artisans.com/kafka-flink-a-practical-how- to/ https://www.youtube.com/watch?v=7RPQUsy4qOM Click-Through Example for Flink’s KafkaConsumer Checkpointing. Robert Metzger, September 2nd , 2015. http://www.slideshare.net/robertmetzger1/clickthrough-example-for-flinks- kafkaconsumer-checkpointing MapR Streams (proprietary alternative to Kafka that is compatible with Apache Kafka 0.9 API) provides out of the box integration with Apache 60

61. 1. Data Input / Output Using Apache Nifi with Flink: • Flink and NiFi: Two Stars in the Apache Big Data Constellation. Matthew Ring. January 19th , 2016 http://www.slideshare.net/mring33/flink-and-nifi-two-stars-in-the-apache-bigdata-constellation • Integration of Apache Flink and Apache Nifi. Bryan Bende, February 4th , 2016 http://www.slideshare.net/BryanBende/integrating-nifi-and-flink Using Elasticsearch with Flink: https://www.elastic.co/ Building real-time dashboard applications with Apache Flink, Elasticsearch, and Kibana. By Fabian Hueske, December 7, 2015.https://www.elastic.co/blog/building-real-time-dashboard- applications-with-apache-flink-elasticsearch-and-kibana 61

62. 2. Deployment Deploy inside of Hadoop via YARN • YARN Setup http://ci.apache.org/projects/flink/flink-docs- master/setup/yarn_setup.html • YARN Configuration http://ci.apache.org/projects/flink/flink-docs-master/setup/config.html#yarn Apache Flink cluster deployment on Docker using Docker-Compose by Simons Laws from IBM. Talk at the Flink Forward in Berlin on October 12, 2015.  Slides: http://www.slideshare.net/FlinkForward/simon-laws-apache-flink- cluster-deployment-on-docker-and-dockercompose  Video recording (40’:49): https://www.youtube.com/watch?v=CaObaAv9tLE 62

63. 3. Legacy Big Data applications Flink’s MapReduce compatibility layer allows to: • run legacy Hadoop MapReduce jobs • reuse Hadoop input and output formats • reuse functions like Map and Reduce. References: • Documentation: https://ci.apache.org/projects/flink/flink-docs-release- 0.7/hadoop_compatibility.html • Hadoop Compatibility in Flink by Fabian Hüeske - November 18, 2014 http://flink.apache.org/news/2014/11/18/hadoop-compatibility.html • Apache Flink - Hadoop MapReduce Compatibility. Fabian Hüeske, January 29, 2015 http://www.slideshare.net/fhueske/flink- hadoopcompat20150128 63

64. 3. Legacy Big Data applications  Cascading on Flink allows to port existing Cascading-MapReduce applications to Apache Flink with virtually no code changes. http://www.cascading.org/cascading-flink/  Expected advantages are performance boost and less resources consumption.  References: • Cascading on Apache Flink, Fabian Hueske, data Artisans. Flink Forward 2015. October 12, 2015 • http://www.slideshare.net/FlinkForward/fabian-hueske-training-cascading-on- flink • https://www.youtube.com/watch?v=G7JlpARrFkU • Cascading connector for Apache Flink. Code on Github https://github.com/dataArtisans/cascading-flink • Running Scalding jobs on Apache Flink, Ian Hummel, December 20, 201http://themodernlife.github.io/scala/hadoop/hdfs/sclading/flink/streaming/realtime/2015/12/2 0/running-scalding-jobs-on-apache-flink/ 64

65. 3. Legacy Big Data applications Flink is compatible with Apache Storm interfaces and therefore allows reusing code that was implemented for Storm: • Execute existing Storm topologies using Flink as the underlying engine. • Reuse legacy application code (bolts and spouts) inside Flink programs. https://ci.apache.org/projects/flink/flink-docs- master/apis/streaming/storm_compatibility.html  A Tale of Squirrels and Storms. Mathias J. Sax, October 13, 2015. Flink Forward 2015 http://www.slideshare.net/FlinkForward/matthias-j-sax-a-tale-of-squirrels-and-storms https://www.youtube.com/watch?v=aGQQkO83Ong Storm Compatibility in Apache Flink: How to run existing Storm topologies on Flink. Mathias J. Sax, December 11, 2015 http://flink.apache.org/news/2015/12/11/storm-compatibility.html 65

66.  Ambari service for Apache Flink: install, configure, manage Apache Flink on HDP, November 17, 2015 https://community.hortonworks.com/repos/4122/ambari-service-for-apache- flink.html Exploring Apache Flink with HDP https://community.hortonworks.com/articles/2659/exploring-apache-flink-with- hdp.html  Apache Flink + Apache SAMOA for Machine Learning on streams http://samoa.incubator.apache.org/  Flink Integrates with Zeppelin http://zeppelin.incubator.apache.org/ http://www.slideshare.net/FlinkForward/moon-soo-lee-data-science-lifecycle- with-apache-flink-and-apache-zeppelin Flink + Apache MRQL http://mrql.incubator.apache.org 66 4. Other tools

67.  Google Cloud Dataflow (GA on August 12, 2015) is a fully-managed cloud service and a unified programming model for batch and streaming big data processing. https://cloud.google.com/dataflow/ (Try it FREE) Flink-Dataflow is a Google Cloud Dataflow SDK Runner for Apache Flink. It enables you to run Dataflow programs with Flink as an execution engine. References: Google Cloud Dataflow on top of Apache Flink, Maximilian Michels, data Artisans. Flink Forward conference, October 12, 2015  http://www.slideshare.net/FlinkForward/maximilian-michels-google- cloud-dataflow-on-top-of-apache-flink Slides  https://www.youtube.com/watch?v=K3ugWmHb7CE Video recording 67 4. Other tools

68. Agenda I. What is Apache Flink stack and how it fits into the Big Data ecosystem? II. How Apache Flink integrates with Hadoop and other open source tools for data input and output as well as deployment? III. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? IV. Who is using Apache Flink? V. Where to learn more about Apache Flink? 68

69. III. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? 1. Why Flink is an alternative to Hadoop MapReduce? 2. Why Flink is an alternative to Apache Storm? 3. Why Flink is an alternative to Apache Spark? 4. What are the benchmarking results against Flink? 69

70. 2. Why Flink is an alternative to Hadoop MapReduce? 1. Flink offers cyclic dataflows compared to the two- stage, disk-based MapReduce paradigm. 2. The application programming interface (API) for Flink is easier to use than programming for Hadoop’s MapReduce. 3. Flink is easier to test compared to MapReduce. 4. Flink can leverage in-memory processing, data streaming and iteration operators for faster data processing speed. 5. Flink can work on file systems other than Hadoop. 70

71. 2. Why Flink is an alternative to Hadoop MapReduce? 6. Flink lets users work in a unified framework allowing to build a single data workflow that leverages, streaming, batch, sql and machine learning for example. 7. Flink can analyze real-time streaming data. 8. Flink can process graphs using its own Gelly library. 9. Flink can use Machine Learning algorithms from its own FlinkML library. 10. Flink supports interactive queries and iterative algorithms, not well served by Hadoop MapReduce. 71

72. 2. Why Flink is an alternative to Hadoop MapReduce? 11. Flink extends MapReduce model with new operators: join, cross, union, iterate, iterate delta, cogroup, … Input Map Reduce Output DataSet DataSet DataSet Red Join DataSet Map DataSet OutputS Input 72

73. 3. Why Flink is an alternative to Storm? 1. Higher Level and easier to use API 2. Lower latency • Thanks to pipelined engine 3. Exactly-once processing guarantees • Variation of Chandy-Lamport 4. Higher throughput • Controllable checkpointing overhead 5. Flink Separates application logic from recovery • Checkpointing interval is just a configuration parameter 73

74. 3. Why Flink is an alternative to Storm? 6. More light-weight fault tolerance strategy 7. Stateful operators 8. Native support for iterative stream processing. 9. Flink does also support batch processing 10. Flink offers Storm compatibility • Flink is compatible with Apache Storm interfaces and therefore allows reusing code that was implemented for Storm. https://ci.apache.org/projects/flink/flink-docs- master/apis/storm_compatibility.html 74

75. 3. Why Flink is an alternative to Storm? Extending the Yahoo! Streaming Benchmark, by Jamie Grier. February 2nd, 2016 http://data-artisans.com/extending-the-yahoo-streaming-benchmark/ Code at Github: https://github.com/dataArtisans/yahoo-streaming-benchmark Results show that Flink has a much better throughput compared to storm and better fault-tolerance guarantees: exactly-once. High-throughput, low-latency, and exactly-once stream processing with Apache Flink. The evolution of fault-tolerant streaming architectures and their performance – Kostas Tzoumas, August 5th 2015 http://data-artisans.com/high-throughput-low-latency-and-exactly-once-stream- processing-with-apache-flink/ 75

76. 4. Why Flink is an alternative to Spark? 4.1 True Low latency streaming engine • Spark’s micro-batches aren’t good enough! • Unified batch and real-time streaming in a single engine • The streaming model of Flink is based on the Dataflow model similar to Google Dataflow 4.2 Unique windowing features not available in Spark • support for event time • out of order streams • a mechanism to define custom windows based on window assigners and triggers. 76

77. 4. Why Flink is an alternative to Spark? 4.3 Native closed-loop iteration operators • make graph and machine learning applications run much faster 4.4 Custom memory manager • no more frequent Out Of Memory errors! • Flink’s own type extraction component • Flink’s own serialization component 4.5 Automatic Cost Based Optimizer • little re-configuration and little maintenance when the cluster characteristics change and the data evolves over time 77

78. 4. Why Flink is an alternative to Apache Spark? 4.6 Little configuration required 4.7 Little tuning required 4.8 Flink has better performance 78

79. 4.1 True low latency streaming engine  Some claim that 95% of streaming use cases can be handled with micro-batches!? Really!!! Spark’s micro-batching isn’t good enough for many time-critical applications that need to process large streams of live data and provide results in real-time. Below are Several use cases, taken from real industrial situations where batch or micro batch processing is not appropriate. References: • MapR Streams FAQ https://www.mapr.com/mapr-streams-faq#question12 • Apache Spark vs. Apache Flink, January 13, 2015. Whiteboard walkthrough by Balaji Narasimhalu from MapR https://www.youtube.com/watch?v=Dzx-iE6RN4w 79

80. 4.1 True low latency streaming engine Financial Services – Real-time fraud detection. – Real-time mobile notifications. Healthcare – Smart hospitals - collect data and readings from hospital devices (vitals, IVs, MRI, etc.) and analyze and alert in real time. – Biometrics - collect and analyze data from patient devices that collect vitals while outside of care facilities. Ad Tech – Real-time user targeting based on segment and preferences. Oil & Gas – Real-time monitoring of pumps/rigs. 80

81. 4.1 True low latency streaming engine Retail – Build an intelligent supply chain by placing sensors or RFID tags on items to alert if items aren’t in the right place, or proactively order more if supply is low. – Smart logistics with real-time end-to-end tracking of delivery trucks. Telecommunications – Real-time antenna optimization based on user location data. – Real-time charging and billing based on customer usage, ability to populate up-to-date usage dashboards for users. – Mobile offers. – Optimized advertising for video/audio content based on what users are consuming. 81

82. 4.1 True low latency streaming engine  “I would consider stream data analysis to be a major unique selling proposition for Flink. Due to its pipelined architecture Flink is a perfect match for big data stream processing in the Apache stack.” – Volker Markl Ref.: On Apache Flink. Interview with Volker Markl, June 24th 2015 http://www.odbms.org/blog/2015/06/on-apache-flink-interview-with-volker-markl/ Apache Flink uses streams for all workloads: streaming, SQL, micro-batch and batch. Batch is just treated as a finite set of streamed data. This makes Flink the most sophisticated distributed open source Big Data processing engine. 82

83. 4.2 Unique windowing features not available in Spark Streaming Besides arrival time, support for event time or a mixture of both for out of order streams Custom windows based on window assigners and triggers. How Apache Flink enables new streaming applications. Part I: The power of event time and out of order stream processing. December 9, 2015 by Stephan Ewen and Kostas Tzoumas http://data- artisans.com/how-apache-flink-enables-new-streaming-applications-part-1/ How Apache Flink enables new streaming applications. Part II: State and versioning. February 3, 2016 by Ufuk Celebi and Kostas Tzoumas http://data-artisans.com/how-apache-flink-enables-new-streaming-applications/ 83

84. 4.2 Unique windowing features not available in Spark Streaming Flink 0.10: A significant step forward in open source stream processing. November 17, 2015. By Fabian Hueske and Kostas Tzoumashttp://data-artisans.com/flink-0-10-a- significant-step-forward-in-open-source-stream-processing/  Dataflow/Beam & Spark: A Programming Model Comparison. February 3, 2016. By Tyler Akidau & Frances Perry, Software Engineers, Apache Beam Committershttps://cloud.google.com/dataflow/blog/dataflow-beam-and- spark-comparison 84

85. 4.3 Iteration Operators Why Iterations? Many Machine Learning and Graph processing algorithms need iterations! For example:  Machine Learning Algorithms • Clustering (K-Means, Canopy, …) • Gradient descent (Logistic Regression, Matrix Factorization)  Graph Processing Algorithms • Page-Rank, Line-Rank • Path algorithms on graphs (shortest paths, centralities, …) • Graph communities / dense sub-components • Inference (Belief propagation) 85

86. 4.2 Iteration Operators  Flink's API offers two dedicated iteration operations: Iterate and Delta Iterate.  Flink executes programs with iterations as cyclic data flows: a data flow program (and all its operators) is scheduled just once.  In each iteration, the step function consumes the entire input (the result of the previous iteration, or the initial data set), and computes the next version of the partial solution 86

87. 4.3 Iteration Operators  Delta iterations run only on parts of the data that is changing and can significantly speed up many machine learning and graph algorithms because the work in each iteration decreases as the number of iterations goes on.  Documentation on iterations with Apache Flinkhttp://ci.apache.org/projects/flink/flink-docs-master/apis/iterations.html 87

88. 4.3 Iteration Operators Step Step Step Step Step Client for (int i = 0; i < maxIterations; i++) { // Execute MapReduce job } Non-native iterations in Hadoop and Spark are implemented as regular for-loops outside the system. 88

89. 4.3 Iteration Operators  Although Spark caches data across iterations, it still needs to schedule and execute a new set of tasks for each iteration. In Spark, it is driver-based looping: • Loop outside the system, in driver program • Iterative program looks like many independent jobs In Flink, it is Built-in iterations: • Dataflow with Feedback edges • System is iteration-aware, can optimize the job  Spinning Fast Iterative Data Flows - Ewen et al. 2012 : http://vldb.org/pvldb/vol5/p1268_stephanewen_vldb2012.pdf The Apache Flink model for incremental iterative dataflow processing. 89

90. 4.4 Custom Memory Manager Features:  C++ style memory management inside the JVM  User data stored in serialized byte arrays in JVM  Memory is allocated, de-allocated, and used strictly using an internal buffer pool implementation. Advantages: 1. Flink will not throw an OOM exception on you. 2. Reduction of Garbage Collection (GC) 3. Very efficient disk spilling and network transfers 4. No Need for runtime tuning 5. More reliable and stable performance 90

91. 4.4 Custom Memory Manager public class WC { public String word; public int count; } empty page Pool of Memory Pages Sorting, hashing, caching Shuffles/ broadcasts User code objects ManagedUnmanagedFlink contains its own memory management stack. To do that, Flink contains its own type extraction and serialization components. JVM Heap 91 Network Buffers

92. 4.4 Custom Memory Manager Flink provides an Off-Heap option for its memory management component References: • Peeking into Apache Flink's Engine Room - by Fabian Hüske, March 13, 2015 http://flink.apache.org/news/2015/03/13/peeking-into-Apache-Flinks- Engine-Room.html • Juggling with Bits and Bytes - by Fabian Hüske, May 11,2015 https://flink.apache.org/news/2015/05/11/Juggling-with-Bits-and-Bytes.html • Memory Management (Batch API) by Stephan Ewen- May 16, 2015 https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=53741525 92

93. 4.4 Custom Memory Manager Compared to Flink, Spark is catching up with its project Tungsten for Memory Management and Binary Processing: manage memory explicitly and eliminate the overhead of JVM object model and garbage collection. April 28, 2014https://databricks.com/blog/2015/04/28/project-tungsten-bringing- spark-closer-to-bare-metal.html It seems that Spark is adopting something similar to Flink and the initial Tungsten announcement read almost like Flink documentation!! 93

94. 4.5 Built-in Cost-Based Optimizer  Apache Flink comes with an optimizer that is independent of the actual programming interface.  It chooses a fitting execution strategy depending on the inputs and operations.  Example: the "Join" operator will choose between partitioning and broadcasting the data, as well as between running a sort-merge-join or a hybrid hash join algorithm.  This helps you focus on your application logic rather than parallel execution.  Quick introduction to the Optimizer: section 6 of the paper: ‘The Stratosphere platform for big data analytics’http://stratosphere.eu/assets/papers/2014- VLDBJ_Stratosphere_Overview.pdf 94

95. 4.5 Built-in Cost-Based Optimizer Run locally on a data sample on the laptop Run a month later after the data evolved Hash vs. Sort Partition vs. Broadcast Caching Reusing partition/sort Execution Plan A Execution Plan B Run on large files on the cluster Execution Plan C What is Automatic Optimization? The system's built-in optimizer takes care of finding the best way to execute the program in any environment. 95

96. 4.5 Built-in Cost-Based Optimizer In contrast to Flink’s built-in automatic optimization, Spark jobs have to be manually optimized and adapted to specific datasets because you need to manually control partitioning and caching if you want to get it right. Spark SQL uses the Catalyst optimizer that supports both rule-based and cost-based optimization. References: • Spark SQL: Relational Data Processing in Sparkhttp://people.csail.mit.edu/matei/papers/2015/sigmod_spark_sql.p df • Deep Dive into Spark SQL’s Catalyst Optimizer https://databricks.com/blog/2015/04/13/deep-dive-into-spark-sqls- catalyst-optimizer.html 96

97. 4.6 Little configuration required  Flink requires no memory thresholds to configure • Flink manages its own memory  Flink requires no complicated network configurations • Pipelining engine requires much less memory for data exchange  Flink requires no serializers to be configured • Flink handles its own type extraction and data representation 97

98. 4.7 Little tuning required Flink programs can be adjusted to data automatically • Flink’s optimizer can choose execution strategies automatically According to Mike Olsen, Chief Strategy Officer of Cloudera Inc. “Spark is too knobby — it has too many tuning parameters, and they need constant adjustment as workloads, data volumes, user counts change. Reference: http://vision.cloudera.com/one-platform/ Tuning Spark Streaming for Throughput By Gerard Maas from Virdata. December 22, 2014 http://www.virdata.com/tuning-spark/ Spark Tuning: http://spark.apache.org/docs/latest/tuning.html 98

99. 4.8 Flink has better performance Why Flink provides a better performance? • Custom memory manager • Native closed-loop iteration operators make graph and machine learning applications run much faster . • Role of the built-in automatic optimizer. For example, more efficient join processing • Pipelining data to the next operator in Flink is more efficient than in Spark. Reference: • A comparative performance evaluation of Flink, Dongwon Kim, Postech. October 12, 2015http://www.slideshare.net/FlinkForward/dongwon-kim-a-comparative- performance-evaluation-of-flink 99

100. 5. What are the benchmarking results against Flink? I am maintaining a list of resources related to benchmarks against Flink: http://sparkbigdata.com/102-spark-blog- slim-baltagi/14-results-of-a-benchmark-between-apache-flink-and-apache-spark A couple resources worth mentioning: • A comparative performance evaluation of Flink, Dongwon Kim, POSTECH, Flink Forward October 13, 2015 http://www.slideshare.net/FlinkForward/dongwon-kim-a-comparative- performance-evaluation-of-flink • Benchmarking Streaming Computation Engines at Yahoo December 16, 2015 Code at github: http://yahooeng.tumblr.com/post/135321837876/benchmarking- streaming-computation-engines-at https://github.com/yahoo/streaming-benchmarks 100

101. Agenda I. What is Apache Flink stack and how it fits into the Big Data ecosystem? II. How Apache Flink integrates with Hadoop and other open source tools for data input and output as well as deployment? III. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark. IV. Who is using Apache Flink? V. Where to learn more about Apache Flink? 101

102. IV. Who is using Apache Flink? You might like what you saw so far about Apache Flink and still reluctant to give it a try! You might wonder: Is there anybody using Flink in pre-production or production environment? I asked this question to our friend ‘Google’ and I came with a short list in the next slide! I also heard more about who is using Flink in production at the Flink Forward conference on October 12-13, 2015 in Berlin, Germany! http://flink-forward.org/ 102

103. IV. Who is using Apache Flink? How companies are using Flink as presented at Flink Forward 2015. Kostas Tzoumas and Stephan Ewen. http://www.slideshare.net/stephanewen1/flink-use-cases-bay-area-meetup- october-2015 Powered by Flink page: https://cwiki.apache.org/confluence/display/FLINK/Powered+by+Flink 103

104. IV. Who is using Apache Flink? 6 Apache Flink Case Studies from the 2015 Flink Forward conference http://sparkbigdata.com/102-spark-blog-slim- baltagi/21-6-apache-flink-case-studies-from-the-2015-flinkforward-conference Mine the Apache Flink User mailing list to discover more! Gradoop: Scalable Graph Analytics with Apache Flink • Gradoop project page http://dbs.uni- leipzig.de/en/research/projects/gradoop • Gradoop: Scalable Graph Analytics with Apache Flink @ FOSDEM 2016. January 31, 2016http://www.slideshare.net/s1ck/gradoop-scalable-graph-analytics-with- apache-flink-fosdem-2016 104

105. PROTEUS http://www.proteus-bigdata.com/ a European Union funded research project to improve Apache Flink and mainly to develop two libraries (visualization and online machine learning) on top of Flink core. PROTEUS: Scalable Online Machine Learning by Rubén Casado at Big Data Spain 2015 • Video: https://www.youtube.com/watch?v=EIH7HLyqhfE • Slides: http://www.slideshare.net/Datadopter/proteus-h2020-big-data 105 IV. Who is using Apache Flink?

106. IV. Who is using Apache Flink?  has its hack week and the winner was a Flink based streaming project! December 18, 2015 • Extending the Yahoo! Streaming Benchmark and Winning Twitter Hack-Week with Apache Flink. Posted on February 2, 2016 by Jamie Grier http://data- artisans.com/extending-the-yahoo-streaming-benchmark/  did some benchmarks to compare performance of their use case implemented on Apache Storm against Spark Streaming and Flink. Results posted on December 18, 2015 http://yahooeng.tumblr.com/post/135321837876/benchmarking- streaming-computation-engines-at 106

107. Agenda I. What is Apache Flink stack and how it fits into the Big Data ecosystem? II. How Apache Flink integrates with Hadoop and other open source tools for data input and output as well as deployment? III. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? IV. Who is using Apache Flink? V. Where to learn more about Apache Flink? 107

108. V. Where to learn more about Apache Flink? 1. What is Flink 2016 roadmap? 2. How to get started quickly with Apache Flink? 3. Where to find more resources about Apache Flink? 4. How to contribute to Apache Flink? 5. What are some Key Takeaways? 108

109. 1 What is Flink 2016 roadmap? SQL/StreamSQL and Table API CEP Library: Complex Event Processing library for the analysis of complex patterns such as correlations and sequence detection from multiple sources https://github.com/apache/flink/pull/1557 January 28, 2015 Dynamic Scaling: Runtime scaling for DataStream programs Managed memory for streaming operators Support for Apache Mesos https://issues.apache.org/jira/browse/FLINK-1984 Security: Over-the-wire encryption of RPC (Akka) and data transfers (Netty) Additional streaming connectors: Cassandra, Kinesis109

110. 1 What is Flink roadmap? Expose more runtime metrics: Throughput / Latencies, Backpressure monitoring, Spilling / Out of Core Making YARN resource dynamic DataStream API enhancements DataSet API Enhancements References: • Apache Flink Roadmap Draft, December 2015 https://docs.google.com/document/d/1ExmtVpeVVT3TIhO1JoBpC5JKXm- 778DAD7eqw5GANwE/edit • What’s next? Roadmap 2016. Robert Metzger, January 26, 2016. Berlin Apache Flink Meetup. http://www.slideshare.net/robertmetzger1/january-2016-flink-community- update-roadmap-2016/9 110

111. 2. How to get started quickly with Apache Flink? Step-By-Step Introduction to Apache Flinkhttp://www.slideshare.net/sbaltagi/stepbystep-introduction-to-apache-flink Implementing BigPetStore with Apache Flink http://www.slideshare.net/MrtonBalassi/implementing-bigpetstore-with-apache-flink Apache Flink Crash Course http://www.slideshare.net/sbaltagi/apache- flinkcrashcoursebyslimbaltagiandsrinipalthepu Free training from Data Artisans http://dataartisans.github.io/flink-training/ All talks at the Flink Forward 2015 http://sparkbigdata.com/102-spark-blog-slim-baltagi/22-all-talks-of-the- 2015-flink-forward-conference 111

112. 3. Where to find more resources about Flink? Flink at the Apache Software Foundation: flink.apache.org/ data-artisans.com @ApacheFlink, #ApacheFlink, #Flink apache-flink.meetup.com github.com/apache/flink user@flink.apache.org dev@flink.apache.org Flink Knowledge Base http://sparkbigdata.com/component/tags/tag/27-flink 112

113. 4. How to contribute to Apache Flink?  Contributions to the Flink project can be in the form of: • Code • Tests • Documentation • Community participation: discussions, questions, meetups, …  How to contribute guide ( also contains a list of simple “starter issues”) http://flink.apache.org/how-to-contribute.html 113

114. 5. What are some key takeaways? 1. Although most of the current buzz is about Spark, Flink offers the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine natively supporting many use cases. 2. With the upcoming release of Apache Flink 1.0, I foresee more adoption especially in use cases with Real-Time stream processing and also fast iterative machine learning or graph processing. 3. I foresee Flink embedded in major Hadoop distributions and supported! 4. Apache Spark and Apache Flink will both have their sweet spots despite their “Me Too Syndrome”! 114

115. Thanks! 116 • To all of you for attending! • To Bloomberg for sponsoring this event. • To data Artisans for allowing me to use some of their materials for my slide deck. • To Capital One for giving me time to prepare and give this talk. • Yes, we are hiring for our New York City offices and our other locations! http://jobs.capitalone.com • Drop me a note at sbaltagi@gmail.com if you’re interested.

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