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Marquez: A Metadata Service for Data Abstraction, Data Lineage, and Event-based Triggers
- 1. Data Platform Marquez: A Metadata Service for Data Abstraction, Data Lineage, and Event-based Triggers DataEngConf NYC ‘18
- 2. Data Platform Hey! I’m Willy Lulciuc Data Engineer Marquez Team, Data Platform @wslulciuc
- 4. Data Platform 268,000 members globally 287 physical locations 72 cities 23 countries
- 5. Data Platform AGENDA Room bookings pipeline (naïve) Intro to Marquez Room bookings pipeline (take 2) 02 03 04 @wslulciuc Future work05 Why metadata?01
- 7. Data lineage ● Add context to data Democratize ● Self-service data culture Data quality ● Build trust in data Why manage and utilize metadata? Data Platform
- 8. … creating a healthy data ecosystem
- 9. Freedom ● Experiment ● Flexible ● Self-sufficient Accountability ● Cost ● Trust Self-service ● Discover ● Explore ● Global context A healthy data ecosystem Data Platform
- 11. Location + floor01 Data Platform
- 12. Data Platform Location + floor01 Open time slot02
- 13. Data Platform Location + floor01 Open time slot02 Duration03
- 14. Data Platform Location + floor01 Open time slot02 Duration03 Confirm04
- 15. Which location has the most bookings? Data Platform
- 18. Data Platform @wslulciuc Requirements Example: Room bookings pipeline (naïve) ● Read room bookings ● Sum room bookings by location ● Write top location ● Run once an hour Read SumStart Write
- 19. Data Platform @wslulciucExample: Room bookings pipeline (naïve) S3 Postgres .csv .csv
- 20. Data Platform @wslulciucExample: Room bookings pipeline (naïve) S3 Postgres .csv .csv b940314,1541624285,2 TSLOCATION ROOM b648485,1541501885,9 b648485,1541710685,4
- 21. Data Platform @wslulciucExample: Room bookings pipeline (naïve) S3 Postgres .csv .csv b940314,1541624285,2 1 b648485 1541721600 2 TSLOCATION ROOM LOCATIONID TS BOOKINGS b648485,1541501885,9 b648485,1541710685,4
- 22. Data Platform Example: Room bookings pipeline (naïve) @wslulciuc Job Scheduler Upstream Downstream S3 Postgres Room Bookings Job Archival Top Locations Workflow We’re live!
- 23. Data Platform @wslulciuc Problems ● What’s our job’s input dataset? ● Does the dataset have an owner? ● How often is the dataset updated? Example: Room bookings pipeline (naïve)
- 24. Data Platform Example: Room bookings pipeline (naïve) @wslulciuc Job Scheduler Upstream Downstream S3 Postgres Room Bookings Job Archival Top Locations Workflow Curses, our job’s failing …
- 25. Data Platform Example: Room bookings pipeline (naïve) @wslulciuc Job Scheduler Upstream Downstream S3 Postgres Room Bookings Job Archival Top Locations Workflow Oh, might be our input data!
- 26. Data Platform @wslulciucExample: Room bookings pipeline (naïve) S3 .csv .csv Room field is of type string b648485,1541501885,9A b940314,1541624285,2G b648485,1541710685,4F TSLOCATION ROOM int
- 27. Data Platform @wslulciuc Problems ● What’s our job’s input dataset? ● Does the dataset have an owner? ● How often is the dataset updated? ● Coordinate changes Example: Room bookings pipeline (naïve)
- 28. Data Platform Example: Room bookings pipeline (naïve) @wslulciuc Job Scheduler Upstream Downstream S3 Postgres Room Bookings Job Archival Top Locations Workflow Ugh, gaps in output data
- 29. Data Platform Example: Room bookings pipeline (naïve) @wslulciuc 00h 01h 02h 03h 04h 05h 06h 07h 08h 09h Backfills! time partitions latest
- 30. Data Platform @wslulciuc Problems ● What’s our job’s input dataset? ● Does the dataset have an owner? ● How often is the dataset updated? ● Coordinate changes ● Figure out backfills Example: Room bookings pipeline (naïve)
- 31. Data Platform @wslulciucExample: Room bookings pipeline (naïve) Job Scheduler S3 Postgres Room Bookings Workflow What we have so far …
- 32. Data Platform @wslulciucExample: Room bookings pipeline (naïve) Job Scheduler S3 Postgres What we have so far … Problems ● What’s our job’s input dataset? ● Does the dataset have an owner? ● How often is the dataset updated? ● Coordinate changes ● Figure out backfillsRoom Bookings Workflow
- 33. … writing a job shouldn’t be this hard!
- 35. Data Platform Metadata Service ● Centralized metadata management ○ Jobs ○ Datasets ● Modular ○ Data discovery ○ Data health ○ Data triggers Marquez: Design @wslulciuc Clients (JVM) Clients (Python) Marquez Search Health Triggers REST API
- 36. Data Platform Module: Search ● Unified search ● Documentation ○ Owner ○ Schema ○ Datasource @wslulciuc Marquez Search Health Triggers Marquez: Data discovery
- 37. @wslulciucMarquez: Data discovery room bo Room Bookings (SF) All created: jul. 8, 2018 Room Booking Metrics (GLBL) created: feb. 15, 2010 All San Francisco room bookings Global room booking metrics Search Datasets TagsS3
- 38. Data Platform Module: Health ● Owner ○ Team / project ● Schema ● Location ● Description ● Size ○ Growth over time ○ Number of records ● Lineage @wslulciuc Marquez Search Health Triggers Marquez: Data health
- 41. Data Platform Module: Triggers ● Timely processing of data ○ No polling! ● Reduce manual handling of backfills ● Reduce production of bad data ○ Incomplete data ○ Low-quality data @wslulciuc Marquez Search Health Triggers Marquez: Data triggers
- 45. Core concepts
- 46. Data Platform Job + Datasets Input Dataset Output Dataset Job @wslulciucMarquez: Core concepts
- 47. Data Platform Dataset versions! @wslulciucMarquez: Core concepts A dataset version contains a complete snapshot of data as of some point in time v1 v1 v2 v2 v3 Job
- 48. Data Platform Deltas “diffs”! v1 v1 v2 v2 v3 Job @wslulciucMarquez: Core concepts INSERT INTO room_bookings (location, bookings) VALUES (b648485, 2)
- 49. Data Platform Deltas “diffs”! v1 v1 v2 v2 v3 Job @wslulciucMarquez: Core concepts Δv2￫v3 INSERT INTO room_bookings (location, bookings) VALUES (b648485, 2)
- 50. Data Platform Job versions! @wslulciucMarquez: Core concepts A job version is created when business logic has changed v1 v1 v2 v2 v3 Job v1 Job v2
- 51. Data Platform Job runs! v1 v1 v2 v2 v3 Job v1 @wslulciucMarquez: Core concepts Job Dataset New Run Job v2
- 52. Data Platform Job runs! v1 v1 v2 v2 v3 Job v1 @wslulciucMarquez: Core concepts Dataset New Run v4 Job Job v2
- 53. Data Platform Job runs! v1 v1 v2 v2 v3 Job v1 @wslulciucMarquez: Core concepts Dataset New Run v4 Finish Update Job Job v2
- 54. Data Platform Data triggers! v1 v1 v2 v2 v3 Job v1 @wslulciucMarquez: Core concepts Dataset New Run v4 Trigger Job v7 Job v10 Job Update Finish Job v2
- 55. Data Platform Job failures! v1 v1 v2 v2 v3 Job v1 @wslulciucMarquez: Core concepts Dataset New Run FailureJob v4 Job v2
- 56. Data Platform Delayed datasets! v1 v1 v2 v2 v3 Job v1 @wslulciucMarquez: Core concepts Dataset New RunJob v4 Job v2 Failure Delay
- 57. Data Platform Design benefits @wslulciucMarquez: Core concepts ● Early upstream failure detection ● Debugging ○ What job version(s) produced / consumed dataset version X? ● Recoverability ○ Full / incremental processing ● Coordination
- 58. Data model
- 59. Job Marquez: Data model @wslulciuc Dataset JobVersion JobRunDatasetVersion * 1 * 1 * 1 1* 1*
- 60. Marquez: Data model @wslulciuc DbTable Filesystem Stream Datasource Types Job Dataset JobVersion JobRunDatasetVersion * 1 * 1 * 1 1* 1*
- 62. Data Platform @wslulciucMarquez: Metadata collection How is metadata collected? ● Marquez API ● Language-specific SDKs ○ Java ○ Python Marquez Job record metadata
- 63. Data Platform @wslulciucMarquez: Metadata collection Workflow Register Job ● Job version ● Inputs / outputs (logical names) ● Owner ● Description
- 64. Data Platform @wslulciucMarquez: Metadata collection Register Job ● Job version ● Inputs / outputs (logical names) ● Owner ● Description Register Job Run Workflow
- 65. Data Platform @wslulciucMarquez: Metadata collection Register Job ● Job version ● Inputs / outputs (logical names) ● Owner ● Description Register Job Run Start ● Update job run state to STARTED Complete ● Update job run state to COMPLETED Workflow
- 66. Data Platform @wslulciucMarquez: Metadata collection Register Job ● Job version ● Inputs / outputs (logical names) ● Owner ● Description Register Job Run Start ● Update job run state to STARTED Complete ● Update job run state to COMPLETED Register Job Run Outputs ● Outputs (physical locations) Workflow
- 67. Room bookings pipeline (take 2) 04
- 68. Data Platform Example: Room bookings pipeline (take 2) @wslulciuc Recall, we are tasked with analyzing room booking trends …
- 69. Data Platform Example: Room bookings pipeline (take 2) @wslulciuc Job Postgres Room Bookings Workflow Top Locations S3 Scheduler Recall, we are tasked with analyzing room booking trends …
- 70. Data Platform @wslulciuc Problems ● What’s our job’s input dataset? ● Does the dataset have an owner? ● How often is the dataset updated? ● Coordinate changes ● Figure out backfills Example: Room bookings pipeline (take 2)
- 71. Enter Marquez
- 72. @wslulciuc room bo Room Bookings (ALL) All created: feb. 15, 2010 Room Bookings (SF) created: jul. 8, 2018 All room bookings since beginning of time All San Francisco room bookings Example: Room bookings pipeline (take 2) Data Platform S3 S3
- 73. @wslulciuc room bo All Room Bookings (SF) created: jul. 8, 2018All San Francisco room bookings Example: Room bookings pipeline (take 2) Well, that was easy! Room Bookings (ALL) created: feb. 15, 2010All room bookings since beginning of time Data Platform S3 S3
- 74. @wslulciucExample: Room bookings pipeline (take 2) Room Bookings (ALL) created: feb. 15, 2010All room bookings since beginning of time Owner: Data Engineering Location: s3://room_bookings/raw/ Info Schema: https://registry.wework.com/schemas/ids/1 Updated: Hourly Data Platform Description: All room bookings since beginning of time S3
- 75. @wslulciucExample: Room bookings pipeline (take 2) Room Bookings (ALL) created: feb. 15, 2010All room bookings since beginning of time Owner: Data Engineering Location: s3://room_bookings/raw/ Info Schema: https://registry.wework.com/schemas/ids/1 Updated: Hourly Data Platform Description: All room bookings since beginning of time S3
- 76. @wslulciucExample: Room bookings pipeline (take 2) Room Bookings (ALL) created: feb. 15, 2010All room bookings since beginning of time Owner: Data Engineering Location: s3://room_bookings/raw/ Info Schema: https://registry.wework.com/schemas/ids/1 Updated: Hourly Data Platform Description: All room bookings since beginning of time Bonus! S3
- 77. Data Platform @wslulciuc Problems ● What’s our job’s input dataset? ● Does the dataset have an owner? ● How often is the dataset updated? ● Coordinate changes ● Figure out backfills Example: Room bookings pipeline (take 2)
- 78. Data Platform @wslulciuc Problems ● What’s our job’s input dataset? ● Does the dataset have an owner? ● How often is the dataset updated? ● Coordinate changes ● Figure out backfills Example: Room bookings pipeline (take 2)
- 79. Data Platform @wslulciuc Problems ● What’s our job’s input dataset? ● Does the dataset have an owner? ● How often is the dataset updated? ● Coordinate changes ● Figure out backfills Example: Room bookings pipeline (take 2)
- 80. Data Platform @wslulciuc Problems ● What’s our job’s input dataset? ● Does the dataset have an owner? ● How often is the dataset updated? ● Coordinate changes ● Figure out backfills Example: Room bookings pipeline (take 2)
- 81. Data Platform Example: Room bookings pipeline (take 2) @wslulciuc Job Postgres Room Bookings Workflow Top Locations S3 We also had to coordinate changes to our input data Scheduler
- 82. Our view Dataset Job Job failure Room bookings workflow
- 83. Global view! Dataset Job Job failure Room bookings workflow Top locations dataset
- 84. @wslulciucExample: Room bookings pipeline (take 2) Room Bookings (ALL) created: feb. 15, 2010All room bookings since beginning of time Owner: Data Engineering Location: s3://room_bookings/raw/ Info Schema: https://registry.wework.com/schemas/ids/2 Updated: Hourly Data Platform Description: All room bookings since beginning of time Oh, version bumped! S3
- 85. Patch, deploy, trigger! Dataset Job Room bookings workflow Top locations dataset Trigger
- 86. Data Platform @wslulciuc Problems ● What’s our job’s input dataset? ● Does the dataset have an owner? ● How often is the dataset updated? ● Coordinate changes ● Figure out backfills Example: Room bookings pipeline (take 2)
- 87. Data Platform @wslulciuc Problems ● What’s our job’s input dataset? ● Does the dataset have an owner? ● How often is the dataset updated? ● Coordinate changes ● Figure out backfills Example: Room bookings pipeline (take 2)
- 88. Data Platform @wslulciuc Problems ● What’s our job’s input dataset? ● Does the dataset have an owner? ● How often is the dataset updated? ● Coordinate changes ● Figure out backfills Example: Room bookings pipeline (take 2)
- 89. Data Platform @wslulciuc RECAP ● Make it trival to discovery datasets ● Global context when debugging ● Easily handle backfills ○ Datasets as dependencies
- 90. Future work05
- 91. Data Platform WeWork + Marquez ● Data platform built around Marquez ● Internal integrations ○ Scheduling ○ Batching ○ Streaming @wslulciucMarquez: Future work
- 92. Data Platform Roadmap ● Short-term ○ Release Marquez 0.1.0 ○ Docs ● Long-term ○ Marquez UI @wslulciucMarquez: Future work
- 94. Thanks! Data Platform DataEngConf NYC ‘18
- 96. Questions? Data Platform DataEngConf NYC ‘18