PyConDE 2016 - Building Data Pipelines with Python

Transcript

1. Building Data 
 Pipelines with Python Data  Engineer  @  TY
 @mfcabrera
 mfcabrera@gmail.com Miguel  Cabrera
 PyCon  Deutschland  30.10.2016
2. Agenda
3. Agenda Context   Data  Pipelines  with  Luigi   Tips  and  Tricks   Examples
4. Data Processing Pipelines
5. cat  file.txt  |  wc  -­‐  l  |  
 mail  -­‐s  “hello”  me@mail.org
6. ETL
7. ETL • Extract  data  from  a  data  source   • Transform  the  data   • Load  into  a  sink  
9. Feature 
 Extraction Parameter Estimation Model Training Feature 
 Extraction Model Predict Visualize/ Format
10. Steps  in  different  technologies
11. Steps  can  be  run  in  parallel
12. Steps  have    complex   dependencies  among  them
13. Workflows • Repeat     • Parametrize     • Resume   • Schedule  it
16. “A Python framework for data flow definition and execution” Luigi
17. Concepts
18. Concepts Tasks   Parameters   Targets   Scheduler  &  Workers
19. Tasks
25. WordCountTask file.txt wc.txt
26. WordCountTask file.txt wc.txt ToJsonTask wc.json
28. Parameters
30. Parameters Used  to  idenNfy  the  task     From  arguments  or  from  configuraNon   Many  types  of  Parameters  (int,  date,   boolean,  date  range,  Nme  delta,  dict,   enum)
31. Targets
32. Targets Resources  produced  by  a  Task   Typically  Local  files  or  files  distributed  file   system  (HDFS)   Must  implement  the  method  exists()   Many  targets  available
34. Scheduler  &  Workers
36. Source:  h@p:/ /www.arashrouhani.com/luigid-­‐basics-­‐jun-­‐2015
37. BaVeries  Included
38. Batteries Included Package  contrib  filled  with  goodies   Good  support  for  Hadoop     Different  Targets   Extensible
39. Task Types Task  -­‐  Local   Hadoop  MR,  Pig,  Spark,  etc   SalesForce,  ElasNcsearch,  etc.   ExternalProgram   check  luigi.contrib  !
40. Target LocalTarget   HDFS,  S3,  FTP,  SSH,    WebHDFS,  etc.   ESTarget,  MySQLTarget,  MSQL,  Hive,   SQLAlchemy,  etc.
42. Tips  &  Tricks
43. Separate  pipeline  and  logic
44. Extend  to  avoid  boilerplate  code
45. DRY
46. Conclusion Luigi  is  a  mature,  baVeries-­‐included   alternaNve  for  building  data  pipelines   Lacks  of  powerful  visualizaNon  of  the   pipelines   Requires  a  external  way  of  launching  jobs   (i.e.  cron).   Hard  to  debug  MR  Jobs
47. Lear More hVps:/ /github.com/spoNfy/luigi   hVp:/ /luigi.readthedocs.io/en/stable/
48. Thanks!
49. Credits • pipe  icon  by  Oliviu  Stoian  from  the  Noun  Project   • Photo  Credit:  (CC)  h@ps:/ /www.flickr.com/photos/ 47244853@N03/29988510886  from  hb.s  via  Compfight     • Concrete  Mixer:  (CC)    h@ps:/ /www.flickr.com/photos/ 145708285@N03/30138453986  by  MasLabor  via   Compfight