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Hadoop First ETL On
Apache Falcon
Srikanth Sundarrajan
Naresh Agarwal
About Authors
!  Srikanth Sundarrajan
!  Principal Architect, InMobi Technology Services
!  Naresh Agarwal
!  Director – Engineering, InMobi Technology Services
Agenda
!  ETL & Challenges with Big Data
!  Apache Falcon – Background
!  Pipeline Designer – Overview
!  Pipeline Designer – Internals
Agenda
!  ETL & Challenges with Big Data
!  Apache Falcon – Background
!  Pipeline Designer – Overview
!  Pipeline Designer – Internals
ETL (Extract Transform Load)
Intelligence
Information
Data
Value
ETL Use cases
Data
Warehouse
Data
Migration
Data
Consolidation
Master Data
Management
Data
Synchronization
Data Archiving
ETL Authoring
Hand
coded
In-house
tools
Off-
shelf
tools
ETL & Big Data – Challenges
Challenges
Volume
VarietyVelocity
Big Data ETL
!  Mostly Hand coded (High Cost – Implementation +
Maintenance)
!  Map Reduce
!  Hive (i.e. SQL)
!  Pig
!  Crunch / Cascading
!  Spark
!  Off-shelf tools (Scale/Performance)
!  Mostly Retrofitted
Agenda
!  ETL & Challenges with Big Data
!  Apache Falcon – Background
!  Pipeline Designer – Overview
!  Pipeline Designer – Internals
Apache Falcon
!  Off the shelf, Falcon provides standard data
management functions through declarative
constructs
!  Data movement recipes
!  Cross data center replication
!  Cross cluster data synchronization
!  Data retention recipes
!  Eviction
!  Archival
Apache Falcon
!  However ETL related functions are still largely left
to the developer to implement. Falcon today
manages only
!  Orchestration
!  Late data handling / Change data capture
!  Retries
!  Monitoring
Agenda
!  ETL & Challenges with Big Data
!  Apache Falcon – Background
!  Pipeline Designer – Overview
!  Pipeline Designer – Internals
Pipeline Designer – Basics
Pipeline Designer – Basics
!  Feed
!  Is a data entity that Falcon manages and is physically
present in a cluster.
!  Data present in this feed conforms to a schema and
partitions of the same are registered with Hcatalog
!  Data Management functions such as eviction, archival
etc are declaratively specified through Falcon Feed
definitions
Pipeline Designer – Basics
Pipeline Designer – Basics
!  Process
!  Workflow that defines various actions that needs to be
performed along with control flow
!  Executes at a specified frequency on one or more
clusters
!  Pipelines
!  Logical grouping of Falcon processes owned and
operated together
Pipeline Designer – Basics
Pipeline Designer – Basics
!  Actions
!  Actions in designer are the building blocks for the
process workflows.
!  Actions have access to output variables earlier in the
flow and can emit output variables
!  Actions can transition to other actions
!  Default / Success Transition
!  Failure Transition
!  Conditional Transition
!  Transformation action is a special action that further
is a collection of transforms
Pipeline Designer – Basics
Pipeline Designer – Basics
!  Transforms
!  Is a data manipulation function that accepts one or
more inputs with well defined schema and produces
ore or more outputs
!  Multiple transform elements can be stitched together
to compose a single transformation action which can
further be used to build a flow
!  Composite Transformations
!  Transforms that are built through a combination of
multiple primitive transforms
!  Possible to add more transforms and extend the
system
Pipeline Designer – Basics
!  Deployment & Monitoring
!  Once a process and the pipeline is composed, the
same is deployed in Falcon as a standard process
Agenda
!  ETL & Challenges with Big Data
!  Apache Falcon – Background
!  Pipeline Designer – Overview
!  Pipeline Designer – Internals
Pipeline Designer Service
Pipeline Designer
Pipeline
Designer
Service
REST API
Versioned
Storage
Flow /
Action /
Transforms
Compiler +
Optimizer
Falcon
Server
Hcatalog
Service
DesignerUI
FalconDashboard
Process
Feed
Schema
Pipeline Designer – Internals
!  Transformation actions are compiled into PIG
scripts
!  Actions and Flows are compiled into Falcon Process
definitions
Text
Q & A
Thanks
mailto:sriksun@apache.org
mailto:naresh.agarwal@inmobi.com

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Hadoop First ETL On Apache Falcon Using Pipeline Designer

  • 1. Hadoop First ETL On Apache Falcon Srikanth Sundarrajan Naresh Agarwal
  • 2. About Authors !  Srikanth Sundarrajan !  Principal Architect, InMobi Technology Services !  Naresh Agarwal !  Director – Engineering, InMobi Technology Services
  • 3. Agenda !  ETL & Challenges with Big Data !  Apache Falcon – Background !  Pipeline Designer – Overview !  Pipeline Designer – Internals
  • 4. Agenda !  ETL & Challenges with Big Data !  Apache Falcon – Background !  Pipeline Designer – Overview !  Pipeline Designer – Internals
  • 5. ETL (Extract Transform Load) Intelligence Information Data Value
  • 6. ETL Use cases Data Warehouse Data Migration Data Consolidation Master Data Management Data Synchronization Data Archiving
  • 8. ETL & Big Data – Challenges Challenges Volume VarietyVelocity
  • 9. Big Data ETL !  Mostly Hand coded (High Cost – Implementation + Maintenance) !  Map Reduce !  Hive (i.e. SQL) !  Pig !  Crunch / Cascading !  Spark !  Off-shelf tools (Scale/Performance) !  Mostly Retrofitted
  • 10. Agenda !  ETL & Challenges with Big Data !  Apache Falcon – Background !  Pipeline Designer – Overview !  Pipeline Designer – Internals
  • 11. Apache Falcon !  Off the shelf, Falcon provides standard data management functions through declarative constructs !  Data movement recipes !  Cross data center replication !  Cross cluster data synchronization !  Data retention recipes !  Eviction !  Archival
  • 12. Apache Falcon !  However ETL related functions are still largely left to the developer to implement. Falcon today manages only !  Orchestration !  Late data handling / Change data capture !  Retries !  Monitoring
  • 13. Agenda !  ETL & Challenges with Big Data !  Apache Falcon – Background !  Pipeline Designer – Overview !  Pipeline Designer – Internals
  • 15. Pipeline Designer – Basics !  Feed !  Is a data entity that Falcon manages and is physically present in a cluster. !  Data present in this feed conforms to a schema and partitions of the same are registered with Hcatalog !  Data Management functions such as eviction, archival etc are declaratively specified through Falcon Feed definitions
  • 17. Pipeline Designer – Basics !  Process !  Workflow that defines various actions that needs to be performed along with control flow !  Executes at a specified frequency on one or more clusters !  Pipelines !  Logical grouping of Falcon processes owned and operated together
  • 19. Pipeline Designer – Basics !  Actions !  Actions in designer are the building blocks for the process workflows. !  Actions have access to output variables earlier in the flow and can emit output variables !  Actions can transition to other actions !  Default / Success Transition !  Failure Transition !  Conditional Transition !  Transformation action is a special action that further is a collection of transforms
  • 21. Pipeline Designer – Basics !  Transforms !  Is a data manipulation function that accepts one or more inputs with well defined schema and produces ore or more outputs !  Multiple transform elements can be stitched together to compose a single transformation action which can further be used to build a flow !  Composite Transformations !  Transforms that are built through a combination of multiple primitive transforms !  Possible to add more transforms and extend the system
  • 22. Pipeline Designer – Basics !  Deployment & Monitoring !  Once a process and the pipeline is composed, the same is deployed in Falcon as a standard process
  • 23. Agenda !  ETL & Challenges with Big Data !  Apache Falcon – Background !  Pipeline Designer – Overview !  Pipeline Designer – Internals
  • 24. Pipeline Designer Service Pipeline Designer Pipeline Designer Service REST API Versioned Storage Flow / Action / Transforms Compiler + Optimizer Falcon Server Hcatalog Service DesignerUI FalconDashboard Process Feed Schema
  • 25. Pipeline Designer – Internals !  Transformation actions are compiled into PIG scripts !  Actions and Flows are compiled into Falcon Process definitions
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  • 39. Q & A