Apache Beam is an open-source unified model for batch and stream data processing. It provides portable pipelines that can run on multiple execution engines like Apache Spark, Apache Flink, and Google Cloud Dataflow. Beam uses a model that focuses on what results are being calculated, where in event time they are calculated, when in processing time they are materialized, and how refinements relate. This allows for portability across execution engines and languages as well as unified pipelines for both batch and stream processing.

1. PORTABLE BATCH AND
STREAM DATA PROCESSING
WITH APACHE BEAM
William Vambenepe, Google
@vambenepe

2. SPEAKERS INFO
WILLIAM VAMBENEPE
Group Product Manager
Data Processing and Analytics
Google Cloud Platform
@vambenepe

3.  Open source (top-level Apache project)
 Portable
 Unifies batch and stream
 Cloud-native
 Built on 15 years of large scale data processing at Google
You don’t need to be a developer to benefit from Beam
APACHE BEAM: THE KEY TO MODERN DATA PROCESSING

4. MapReduce
Apache
Beam
Cloud
Dataflow
BigTable Dremel
Colossus
Flume
Megastore Spanner
PubSub
Millwheel
THE EVOLUTION OF DATA PIPELINES

5. BEAM = Batch + StrEAM

6. Progressive evolution from batch to stream
- Stream as the new default
Cost/perf trade-offs without re-architecting
- Just turn the knob
ML: data preparation consistency between training & scoring
- Same pipeline to train in batch and score in stream
BENEFIT OF BATCH / STREAM UNIFICATION

7. PROCESSING
TIME VS.
EVENT TIME

8. What results are calculated?
Where in event time are results calculated?
When in processing time are results materialized?
How do refinements of results relate?
THE BEAM MODEL: ASKING THE RIGHT QUESTIONS

9. The Beam Model:
is being
computed?
WHAT

10. WHERE
The Beam Model:
in event
time ?

11. WHEN
The Beam Model:
in processing
time ?

12. HOW
The Beam Model:
do refinements
relate?

13. What results are calculated?
Where in event time are results calculated?
When in processing time are results materialized?
How do refinements of results relate?
THE BEAM MODEL: ASKING THE RIGHT QUESTIONS

14. PORTABLE
Write once, run anywhere

15.  The Beam Model: the abstractions at the
core of Apache Beam
 Choice of API: Users write their pipelines in
a language that’s familiar and integrated with
their other tooling
 Choice of Runtime: Users choose the right
runner for their current needs -- on-prem /
cloud, open source / not, fully managed / not
 Scalability for Developers: Clean APIs allow
developers to contribute modules independently
Language B
SDK
Language A
SDK
Language C
SDK
Runner
1
Runner
3
Runner
2
The Beam Model
Language A
Language
C
Language B
The Beam Model
BEAM VISION: MIX AND MATCH SDKS AND RUNTIMES

16. APACHE SPARK
 Open-source cluster-
computing framework
 Large ecosystem of
APIs and tools
 Runs on premise or
in the cloud
APACHE FLINK
 Open-source distributed data
processing engine
 High-throughput and
low-latency stream processing
 Runs on premise or in the cloud
EXAMPLE BEAM RUNNERS
GOOGLE CLOUD DATAFLOW
 Fully-managed service for batch and
stream data processing
 Provides dynamic auto-scaling,
monitoring tools, and tight integration
with Google Cloud Platform

17. GA 360
Cloud
Pub/Sub
BigQuery Storage
(tables)
Cloud Bigtable
(NoSQL)
Cloud Storage
(files)
Cloud Dataflow
Capture Store Analyze
Stackdriver
Process
Stream
Use
Cloud Dataproc
Cloud Datalab
Real-time analytics
Real-time
dashboard
Real-time
alerts
ML Engine
Batch
Firebase
Storage
Transfer
Service
Cloud
Dataflow
etc...
SQL
BigQuery Analytics
Adwords
DoubleClick
YouTube
BEAM ON GOOGLE CLOUD: SERVERLESS DATA PROCESSING

18. BEAM
MORE INFO
 Apache Beam: https://beam.apache.org
 Google Cloud Platform: https://cloud.google.com
The Dataflow Model paper from VLDB 2015
http://www.vldb.org/pvldb/vol8/p1792-Akidau.pdf
Streaming 101 and 102: The World Beyond Batch
https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-101
https://www.oreilly.com/ideas/the-world-beyond-batch-streaming-102

19. THANK YOU!