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Big data represents a significant paradigm shift in enterprise technology. Big data radically changes the nature of the data management profession as it introduces new concerns about the volume, …

Big data represents a significant paradigm shift in enterprise technology. Big data radically changes the nature of the data management profession as it introduces new concerns about the volume, velocity and variety of corporate data. Apache Hadoop is the open source defacto standard for implementing big data solutions on the Java platform. Hadoop consists of its kernel, MapReduce, and the Hadoop Distributed Filesystem (HDFS). A challenging task is to send all data to Hadoop for processing and storage (and then get it back to your application later), because in practice data comes from many different applications (SAP, Salesforce, Siebel, etc.) and databases (File, SQL, NoSQL), uses different technologies and concepts for communication (e.g. HTTP, FTP, RMI, JMS), and consists of different data formats using CSV, XML, binary data, or other alternatives. This session shows different open source frameworks and products to solve this challenging task. Learn how to use every thinkable data with Hadoop – without plenty of complex or redundant boilerplate code.

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- 1. Kai Wähner| Talend @KaiWaehner www.kai-waehner.de/blog Xing / LinkedIn Big Data beyond Hadoop How to integrate ALL your Data
- 2. Kai Wähner Main Tasks Requirements Engineering Enterprise Architecture Management Business Process Management Architecture and Development of Applications Service-oriented Architecture Integration of Legacy Applications Cloud Computing Big Data Consulting Developing Coaching Speaking Writing © Talend 2013 Contact Email: kontakt@kai-waehner.de Blog: www.kai-waehner.de/blog Twitter: @KaiWaehner Social Networks: Xing, LinkedIn “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 3. Key messages You have to care about big data to be competitive in the future! You have to integrate different sources to get most value out of it! Big data integration is no (longer) rocket science! © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 4. Agenda • Big data paradigm shift • Challenges for integrating big data • Choosing Hadoop for big data integration • Integration with an open source framework • Integration with an open source suite • Custom big data connectors © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 5. Agenda • Big data paradigm shift • Challenges for integrating big data • Choosing Hadoop for big data integration • Integration with an open source framework • Integration with an open source suite • Custom big data connectors © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 6. Why should you care about big data? “If you can't measure it, you can't manage it.” William Edwards Deming (1900 –1993) American statistician, professor, author, lecturer and consultant © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 7. Why should you care about big data? „Silence the HiPPOs“ (highest-paid person‘s opinion) Being able to interpret unimaginable large data stream, the gut feeling is no longer justified! © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 8. What is big data? The Vs of big data Volume (terabytes, petabytes) Velocity (realtime or nearrealtime) Variety (social networks, blog posts, logs, sensors, etc.) © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner Value
- 9. Big data tasks to solve - before analysis Big Data Integration – Land data in a Big Data cluster – Implement or generate parallel processes Big Data Manipulation – Simplify manipulation, such as sort and filter – Computational expensive functions Big Data Quality & Governance – Identify linkages and duplicates, validate big data – Match connector, execute basic quality features Big Data Project Management – Place frameworks around big data projects – Common Repository, scheduling, monitoring © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 10. Use case: Clickstream Analysis “The advantage of their new system is that they can now look at their data [from their log processing system] in anyway they want: ➜ Nightly MapReduce jobs collect statistics about their mail system such as spam counts by domain, bytes transferred and number of logins. ➜ When they wanted to find out which part of the world their customers logged in from, a quick [ad hoc] MapReduce job was created and they had the answer within a few hours. Not really possible in your typical ETL system.” http://highscalability.com/how-rackspace-now-uses-mapreduce-and-hadoop-query-terabytes-data © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 11. Use case: Risk management Deduce Customer Defections http://hkotadia.com/archives/5021 © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 12. Use case: Flexible pricing ➜ ➜ ➜ ➜ With revenue of almost USD 30 billion and a network of 800 locations, Macy's is considered the largest store operator in the USA Daily price check analysis of its 10,000 articles in less than two hours Whenever a neighboring competitor anywhere between New York and Los Angeles goes for aggressive price reductions, Macy's follows its example If there is no market competitor, the prices remain unchanged http://www.t-systems.com/about-t-systems/examples-of-successes-companies-analyze-big-data-in-record-time-l-t-systems/1029702 © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 13. Storage: Compliance Global Parcel Service A lot of data must be stored „forever“ ➜ Numbers increase exponentially ➜ Goal: As cheap as possible ➜ Problem: (Fast) queries must still be possible ➜ Solution: Commodity servers and „Hadoop querying“ ➜ http://archive.org/stream/BigDataImPraxiseinsatz-SzenarienBeispieleEffekte/Big_Data_BITKOM-Leitfaden_Sept.2012#page/n0/mode/2up © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 14. Agenda • Big data paradigm shift • Challenges for integrating big data • Choosing Hadoop for big data integration • Integration with an open source framework • Integration with an open source suite • Custom big data connectors © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 15. Limited big data experts This is your company Big Data Geek © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 16. Big data tool selection (business perspective) Wanna buy a big data solution for your industry? ➜ Maybe a competitor has a big data solution which adds business value? ➜ The competitor will never publish it (rat-race)! ➜ © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 17. Big data tool selection (technical perspective) Looking for ‚your‘ required big data product? Support your data from scratch? Good luck! © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 18. Data quality Big Data + Poor Data Quality = Big Problems © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 19. How to solve these big data challenges? © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 20. What is your Big Data process? Step 1 Step 2 Step 3 1) Do not begin with the data, think about business opportunities 2) Choose the right data (combine different data sources) 3) Use easy tooling http://hbr.org/2012/10/making-advanced-analytics-work-for-you © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 21. Agenda • Big data paradigm shift • Challenges for integrating big data • Choosing Hadoop for big data integration • Integration with an open source framework • Integration with an open source suite • Custom big data connectors © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 22. Technology perspective How to process big data? © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 23. How to process big data? The critical flaw in parallel ETL tools is the fact that the data is almost never local to the processing nodes. This means that every time a large job is run, the data has to first be read from the source, split N ways and then delivered to the individual nodes. Worse, if the partition key of the source doesn’t match the partition key of the target, data has to be constantly exchanged among the nodes. In essence, parallel ETL treats the network as if it were a physical I/O subsystem. The network, which is always the slowest part of the process, becomes the weakest link in the performance chain. http://blog.syncsort.com/2012/08/parallel-etl-tools-are-dead © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 24. How to process big data? Slides: http://www.slideshare.net/pavlobaron/100-big-data-0-hadoop-0-java Video: http://www.infoq.com/presentations/Big-Data-Hadoop-Java © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 25. How to process big data? The defacto standard for big data processing © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 26. How to process big data? “A big part of [the company’s strategy] includes wiring SQL Server 2012 (formerly known by the codename “Denali”) to the Hadoop distributed computing platform, and bringing Hadoop to Windows Server and Azure” Even Microsoft (the .NET house) relies on Hadoop since 2011 © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 27. What is Hadoop? Apache Hadoop, an open-source software library, is a framework that allows for the distributed processing of large data sets across clusters of commodity hardware using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 28. Map (Shuffle) Reduce Simple example • Input: (very large) text files with lists of strings, such as: „318, 0043012650999991949032412004...0500001N9+01111+99999999999...“ • We are interested just in some content: year and temperate (marked in red) • The Map Reduce function has to compute the maximum temperature for every year Example from the book “Hadoop: The Definitive Guide, 3rd Edition” © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 29. How to process big data? © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 30. Agenda • Big data paradigm shift • Challenges for integrating big data • Choosing Hadoop for big data integration • Integration with an open source framework • Integration with an open source suite • Custom big data connectors © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 31. Alternatives for systems integration Integration Suite Enterprise Service Bus Integration Framework Low Connectivity Routing Transformation © Talend 2013 High + INTEGRATION Tooling Monitoring Support + BUSINESS PROCESS MGT. BIG DATA / MDM REGISTRY / REPOSITORY RULES ENGINE „YOU NAME IT“ “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner Complexity of Integration
- 32. Alternatives for systems integration Integration Framework Enterprise Service Bus Low © Talend 2013 Integration Suite High “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner Complexity of Integration
- 33. More details about integration frameworks... http://www.kai-waehner.de/blog/2012/12/20/showdown-integration-frameworkspring-integration-apache-camel-vs-enterprise-service-bus-esb/ © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 34. Enterprise Integration Patterns (EIP) Apache Camel Implements the EIPs © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 35. Enterprise Integration Patterns (EIP) © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 36. Enterprise Integration Patterns (EIP) © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 37. Architecture http://java.dzone.com/articles/apache-camel-integration © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 38. Choose your required connectors TCP SQL SMTP Netty RMI FTP Lucene JMX MQ Atom LDAP Quartz XSLT Akka Many many more © Talend 2013 IRC Log HTTP File EJB AMQP RSS AWS-S3 Jetty JDBC Bean-Validation JMS CXF “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner Custom Components
- 39. Choose your favorite DSL XML (not production-ready yet) © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 40. Deploy it wherever you need Standalone Application Server Web Container Spring Container OSGi Cloud © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 41. Enterprise-ready • Open Source • Scalability • Error Handling • Transaction • Monitoring • Tooling • Commercial Support © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 42. Example: Camel integration route © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 43. Hadoop Integration with Apache Camel © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 44. camel-hdfs connector (Java DSL) // Producer from("ftp://user@myServer?password=secret") .to(“hdfs:///myDirectory/myFile.txt?append=true"); // Consumer from(“hdfs:///myDirectory/myBigDataAnalysis.csv") .to(“file:target/reports/report.csv"); © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 45. camel-hbase connector (XML DSL) © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 46. Camel and Hadoop File Formats Choose the appropriate Hadoop file format, e.g. SequenceFile, MapFile, etc... © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 47. camel-avro connector Camel is not just about connectors ... Camel supports a pluggable DataFormat to allow messages to be marshalled to and unmarshalled from binary or text formats, e.g. CSV, JSON, SOAP, EDI, ZIP, or ... ... Avro, a data serialization system used in Apache Hadoop. camel-avro connector provides a dataformat for Avro, which allows serialization and deserialization of messages using Apache Avro's binary data format. Moreover, it provides support for Apache Avro's RPC, by providing producers and consumers endpoint for using Avro over Netty or HTTP. © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 48. Real World Use Case: Storage: Compliance Global Parcel Service A lot of data must be stored „forever“ ➜ Numbers increase exponentially ➜ Goal: As cheap as possible ➜ Problem: (Fast) queries must still be possible ➜ Solution: Commodity servers and „Hadoop querying“ ➜ http://archive.org/stream/BigDataImPraxiseinsatz-SzenarienBeispieleEffekte/Big_Data_BITKOM-Leitfaden_Sept.2012#page/n0/mode/2up © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 49. Real world use case: Storage: Compliance Orders (Server 1) Payments (Server 2) ETL Log Files (Server 3) Log Files (Server 100) © Talend 2013 Storage “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner Query
- 50. Live demo Apache Camel in action... © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 51. camel-pig? camel-hive? camel-hcatalog? Not available yet (current Camel version: 2.12) Workarounds: • • • Use Pig / Hive-Query scripts (via camel-exec connector or any scripting language) Build your own connector (more details later ...) Use Hive-Hbase-Integration and store data in HBase ( „ugly“) © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 52. Agenda • Big data paradigm shift • Challenges for integrating big data • Choosing Hadoop for big data integration • Integration with an open source framework • Integration with an open source suite • Custom big data connectors © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 53. Alternatives for systems integration Integration Suite Enterprise Service Bus Integration Framework Low Connectivity Routing Transformation © Talend 2013 High + INTEGRATION Tooling Monitoring Support + BUSINESS PROCESS MGT. BIG DATA / MDM REGISTRY / REPOSITORY RULES ENGINE „YOU NAME IT“ “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner Complexity of Integration
- 54. Alternatives for systems integration Integration Framework Enterprise Service Bus Low © Talend 2013 Integration Suite High “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner Complexity of Integration
- 55. More details about ESBs and suites... http://www.kai-waehner.de/blog/2013/01/23/spoilt-for-choicehow-to-choose-the-right-enterprise-service-bus-esb/ © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 56. Hadoop Integration with Talend Open Studio © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 57. Vision: Democratize big data Talend Open Studio for Big Data • • …an open source ecosystem © Talend 2013 Generates Hadoop code and run transforms inside Hadoop • Native support for HDFS, Pig, Hbase, Hcatalog, Sqoop and Hive • Pig Improves efficiency of big data job design with graphic interface 100% open source under an Apache License • Standards based “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 58. Vision: Democratize big data Talend Platform for Big Data • Builds on Talend Open Studio for Big Data • Adds data quality, advanced scalability and management functions • • © Talend 2013 Data cleansing • • Data quality and profiling • …an open source ecosystem Shared Repository and remote deployment • Pig MapReduce massively parallel data processing Reporting and dashboards Commercial support, warranty/IP indemnity under a subscription license “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 59. Talend Open Studio for Big Data © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 60. Real world Use case: Clickstream Analysis “The advantage of their new system is that they can now look at their data [from their log processing system] in anyway they want: ➜ Nightly MapReduce jobs collect statistics about their mail system such as spam counts by domain, bytes transferred and number of logins. ➜ When they wanted to find out which part of the world their customers logged in from, a quick [ad hoc] MapReduce job was created and they had the answer within a few hours. Not really possible in your typical ETL system.” http://highscalability.com/how-rackspace-now-uses-mapreduce-and-hadoop-query-terabytes-data © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 61. Example: A semi-structured log file © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 62. Potential Uses of Clickstream Data One of the original uses of Hadoop at Yahoo was to store and process their massive volume of clickstream data. Now enterprises of all types can use Hadoop to refine and analyze clickstream data. They can then answer business questions such as: • • • What is the most efficient path for a site visitor to research a product, and then buy it? What products do visitors tend to buy together, and what are they most likely to buy in the future? Where should I spend resources on fixing or enhancing the user experience on my website? Goal: Data visualization can help you optimize your website and convert more visits into sales and revenue. Source: for Clickstream Example: „Hortonworks Hadoop Tutorials - Real Life Use Cases” http://hortonworks.com/blog/hadoop-tutorials-real-life-use-cases-in-the-sandbox © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 63. Real world use case: Clickstream Analysis Log Files (Server 1) ETL Log Files (Server 2) Log Files (Server 100) © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 64. Example: ETL Job „... using Talend’s HDFS and Hive Components” © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 65. Example: ETL Job „... using Talend’s Map Reduce Components*” * Not available in open source version of Talend Studio © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 66. Example: Analysis with Microsoft Excel We can see that the largest number of page hits in Florida were for clothing, followed by shoes. Source: for Clickstream Example: „Hortonworks Hadoop Tutorials - Real Life Use Cases” http://hortonworks.com/blog/hadoop-tutorials-real-life-use-cases-in-the-sandbox © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 67. Example: Analysis with Microsoft Excel The chart shows that the majority of men shopping for clothing on our website are between the ages of 22 and 30. With this information, we can optimize our content for this market segment. Source: for Clickstream Example: „Hortonworks Hadoop Tutorials - Real Life Use Cases” http://hortonworks.com/blog/hadoop-tutorials-real-life-use-cases-in-the-sandbox © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 68. Example: Analysis with Tableau Spoilt for Choice Use your preferred BI or Analysis tool! © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 69. Live demo „Talend Open Studio for Big Data“ in action... © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 70. Agenda • Big data paradigm shift • Challenges for integrating big data • Choosing Hadoop for big data integration • Integration with an open source framework • Integration with an open source suite • Custom big data connectors © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 71. Custom connectors Easy to realize for all integration alternatives * • Integration Framework • Enterprise Service Bus • Integration Suite * At least for open source solutions © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 72. Custom connectors You might need a ... • ... Hive connector for Camel • ... Impala connector for Talend • ... custom connector for your internal data format © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 73. Live demo (Example: Apache Camel) Custom connectors in action... © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 74. Did you get the key message? © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 75. Key messages You have to care about big data to be competitive in the future! You have to integrate different sources to get most value out of it! Big data integration is no (longer) rocket science! © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 76. Did you get the key message? © Talend 2013 “Big Data beyond Hadoop – How to integrate ALL your Data” by Kai Wähner
- 77. Thank you for your attention. Questions? Kai Wähner| Talend @KaiWaehner www.kai-waehner.de/blog Xing / LinkedIn

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