4.Building a Data Product using apache Zeppelin - Apache Kylin Meetup @Shanghai
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Building a Data Product using apache Zeppelin - Apache Kylin Meetup @Shanghai
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4.Building a Data Product using apache Zeppelin - Apache Kylin Meetup @Shanghai
- 1. Building a Data Product using apache Zeppelin (incubating) NFLabs for ApacheCon ’15 EU
- 2. Alexander Bezzubov Data engineer @NFLabs based in Seoul, South Korea bzz@apache.org
- 3. So you want to build a data product… What do you need?
- 4. To build a data product we need: Idea Data Software (to process the data) Hardware (to run the software) …and a brain
- 5. Idea Data Software (to process the data) Hardware (to run the software) …and a brain To build a data product we need:
- 6. Software
- 7. Software
- 8. Software … …
- 9. Software … …
- 10. Zeppelin
- 11. Zeppelin Opensouce analytical environment, with pluggable backend data-processing systems, with notebook-style GUI for visualisations
- 12. ASF Incubation12.2014 08.2013 NFLabs Internal project Hive/Shark http://zeppelin.incubator.apache.org 12.2012 Commercial App using AMP Lab Shark 0.5 10.2013 Prototype Hive/Shark Zeppelin History
- 13. Zeppelin History
- 14. Zeppelin History
- 15. Zeppelin History
- 16. Zeppelin History
- 17. Zeppelin Current Status 1 Release 63 Contributors worldwide 689 Stars on GH ~300/900 Emails at users/dev @i.a.o http://zeppelin.incubator.apache.org
- 19. Hardware
- 21. Idea
- 22. An Idea Would not it be cool if …
- 23. An Idea Would not it be cool if …Would not it be cool if … you could have your own Google Analytics?
- 24. An Idea Would not it be cool if …Would not it be cool if … you could have your own Google Analytics? sorry, we already saw it in eCG talk.. ok, let’s pick something else
- 25. An Idea you could be the first to know when there is a new interesting* opensouce project Would not it be cool if …
- 26. Data
- 27. Data: Github archive https://www.githubarchive.org• Github logs, hosted in the cloud • Collaboration between Github and Google engineers • 20+ events, 250+Gb since 2012 • Proprietary software • available on BigQuery
- 28. But what if you could: analyse this data independently, without asking permission or paying anybody?
- 29. But what if you could: analyse this data independent, without asking permission or paying anybody? well, with ASF you can!
- 30. Let’s build a data product for ourselves!
- 32. We are going to build a Notebook that: • Downloads the latest data from GitHub Archive • Read & explore the dataset • Imports, filters the PublicEvent • Join logs w/ more data from Github API calls • Shows simple HTML template, to visualise the list • Sends email notifications
- 33. We are going to build a Notebook that: basically, sends you digest emails:
- 34. Start Zeppelin ./bin/zeppelin-daemon.sh start & create a new notebook http://zeppelin.incubator.apache.org/download.html
- 36. We are going to build a Notebook that: • Downloads the latest data from GitHub Archive • Read & explore the dataset • Imports, filters the PublicEvent • Join logs w/ more data from Github API calls • Shows simple HTML template, to visualise the list • Sends email notifications
- 37. Load Dependency
- 38. We are going to build a Notebook that: • Downloads the latest data from GitHub Archive • Read & explore the dataset • Imports, filters the PublicEvent • Join logs w/ more data from Github API calls • Shows simple HTML template, to visualise the list • Sends email notifications
- 39. Download Data In serial, sample, using shell interpreter
- 40. Download Data In serial, whole day, using shell interpreter Don’t need this as we have data prepared
- 41. Download Data In parallel, using Spark interpreter
- 42. We are going to build a Notebook that: • Downloads the latest data from GitHub Archive • Read & explore the dataset • Imports, filters the PublicEvent • Join on external information through remote API call • Shows simple HTML template to visualise the list • Sends email notifications
- 43. Read Data
- 44. Explore Data #1 Pie Chart of Event types
- 45. Explore Data #2 Top 10 organisations by event type
- 46. We are going to build a Notebook that: • Downloads the latest data from GitHub Archive • Read & explore the dataset • Imports, filters the PublicEvent • Join on external information through remote API call • Shows simple HTML template to visualise the list • Sends email notifications
- 47. Filter: cleanup Only organisations open sourcing repositories
- 48. Filter: interesting companies Only organisations open sourcing repositories
- 49. We are going to build a Notebook that: • Downloads the latest data from GitHub Archive • Read & explore the dataset • Imports, filters the PublicEvent • Join on external information through remote API • Shows simple HTML template to visualize the list • Sends email notifications
- 50. Call Github API Getting more information about repository GitHub personal access token to rise rate-limit github.com/<username> => Edit Profile => Personal access tokens => Generate new token
- 51. Join orgs and repos
- 52. We are going to build a Notebook that: • Downloads the latest data from GitHub Archive • Read & explore the dataset • Imports, filters the PublicEvent • Join on external information through remote API call • Shows simple HTML template • Sends email notifications
- 53. HTML Preview To generate a template
- 54. HTML Preview To output the results
- 55. We are going to build a Notebook that: • Downloads the latest data from GitHub Archive • Read & explore the dataset • Imports, filters the PublicEvent • Join on external information through remote API call • Shows simple HTML template to visualise the list • Sends email notifications
- 56. Send Email
- 57. Send Email
- 58. Send Email
- 59. We are going to build a Notebook that: • Downloads the latest data from GitHub Archive • Read & explore the dataset • Imports, filters the PublicEvent • Join on external information through remote API call • Shows simple HTML template to visualise the list • Sends email notifications
- 60. Schedule
Notas do Editor
- short intro designed as a workshop - go home with some practical skills
- Go though some history of Zeppelin project and then I’ll guide through a hands-on example of one data product
- any “product”, based on data
- Easy, because of Apache &BigData
- ASF is a leader in BigData ecosystem one of the biggest foundations, 200+ projects gives you the power to crunch the data that only big companies have interesting engineering challenges, job
- a lot computing options some of them do have a web ui to interact with, but are project-secific
- with Z - you have visualisation options, agnostic to the backend allows you to be flexible with your stack Spark is the most advanced interpreter now - historical reasons
- * what is Zeppelin? and some history
- started at NFLabs in Seoul, South Korea open-source (free, as a beer) since 2013 when mature enough, to provide a value to community -> ASF
- comercial app
- prototype OSS free as a beer
- visualisations 2 different backend Hive and Shark
- with pluggable backend systems, thought ‘interpreters’
- so, what exactly is Zeppelin that’s how it look like in 10 months under ASF
- interpreters abstracted was very important
- if you want to build a data product - you better be prepared for scale quality of the product depends on the amount of data
- * plan for both: a cluster (cloud) and a laptop (dev prod) for free * …and you have those, thanks to ASF!
- good example of data product using web logs!
- talked about Apache a lot (Kylin, Tez..) we all love Opensource Github is the biggest opensource code repository problem: you cannot follow organisation on Github!
- oss is awesome, so many things happen - hard to keep up problem: you cannot follow organisation on Github! say eBay, or any other tech company you are interested in OSS something now have software\hardware and an Idea for data product. Something missing
- Github opensources their log data may companies - log-generating machines, but GH pushes it further * available to crunch, using tones of closed-sourced engineering effort
- Would not that be cool?
- Hands-on part
- break-down: 8 steps
- build a missing feature definition of “interesting” may vary i.e not just “new from big companies”, but intelligent suggestions based on
- going to be using different interpreters: shell, spark, kylin
- break-down: 8 steps
- break-down: 8 steps
- sequential download, does not utilise full channel bandwidth
- * simplicity! * in 24 parallel threads