1.
© Hortonworks Inc. 2013
ETL 2.0
Reference Architecture
Page 1
George Vetticaden - Hortonworks: Solutions Engineer
George Trujillo - Hortonworks: Master Principal Big Data Specialist

2.
© Hortonworks Inc. 2013
George Vetticaden
• Solutions Engineer – Big Data at Hortonworks
• Chief Architect and Co-Founder of eScreeningz
§ Enterprise Architect vFabric Cloud App Platform – VMware
§ Specialties:
§ Big Data and Cloud Computing
§ Hadoop
§ Cloud Application Platforms (PAAS) – Cloud Foundry, Heroku
§ Infrastructure as Service Platforms – vCloud Director, AWS
§ Virtualization – vSphere, vCenter
§ J2EE
§ Hibernate, Spring
§ ESB and Middleware Integration
§ SOA Architecture

3.
© Hortonworks Inc. 2013
George Trujillo
• Master Principal Big Data Specialist - Hortonworks
• Tier One BigData, Oracle and BCA Specialist - VMware
• 20+ years Oracle DBA: DW, BI, RAC, Streams, Data
Guard, Perf, B/R
§ Oracle Double ACE
§ Sun Microsystem's Ambassador for Application Middleware
§ Oracle Fusion Council & Oracle Beta Leadership Council
§ Two terms Independent Oracle Users Group Board of
Directors
§ Recognized as one of the “Oracles of Oracle” by IOUG
§ MySQL Certified DBA
§ VMware Certified Instructor (VCI)
Sun Ambassador

4.
© Hortonworks Inc. 2013
Challenges with a Traditional ETL Platform
Page 4
Incapable/high
complexity when
dealing with loosely
structured data
Data discarded
due to cost and/or
performance
-Lot of time spent understanding
source and defining destination data
structures
-High latency between data generation
and availability
No visibility into
transactional data
-Doesn’t scale linearly.
-License Costs High
EDW used as an ETL
tool with 100s of
transient staging tables

5.
© Hortonworks Inc. 2013
Hadoop Based ETL Platform
Page 5
-Support for any type
of data: structured/
unstructured
-Linearly scalable on
commodity hardware
-Massively parallel
storage and compute
-Store raw transactional data
-Store 7+ years of data with no archiving
-Data Lineage: Store intermediate stages of data
-Becomes a powerful analytics platform
-Provides data for use with
minimum delay and latency
-Enables real time capture
of source data
-Data warehouse can
focus less on storage
& transformation and
more on analytics

6.
© Hortonworks Inc. 2013
Key Capability in Hadoop: Late binding
Page 6
DATA
SERVICES
OPERATIONAL
SERVICES
HORTONWORKS
DATA
PLATFORM
HADOOP
CORE
WEB
LOGS,
CLICK
STREAMS
MACHINE
GENERATED
OLTP
Data
Mart
/
EDW
Client
Apps
Dynamically
Apply
Transforma8ons
Hortonworks
HDP
With
tradi=onal
ETL,
structure
must
be
agreed
upon
far
in
advance
and
is
difficult
to
change.
With
Hadoop,
capture
all
data,
structure
data
as
business
need
evolve.
WEB
LOGS,
CLICK
STREAMS
MACHINE
GENERATED
OLTP
ETL
Server
Data
Mart
/
EDW
Client
Apps
Store
Transformed
Data

7.
© Hortonworks Inc. 2013
Organize Tiers and Process with Metadata
Page 7
Work
Tier
Standardize, Cleanse, Transform
MapReduce
Pig
Raw
Tier
Extract & Load
WebHDFS
Flume
Sqoop
Gold/
Storage
Tier
Transform, Integrate, Storage
MapReduce
Pig
Conform, Summarize, Access
HiveQL
Pig
Access
Tier
HCat
Provides unified
metadata access
to Pig, Hive &
MapReduce
• Organize data
based on
source/derived
relationships
• Allows for fault
and rebuild
process

8.
© Hortonworks Inc. 2013
ETL Reference Architecture
Page 8
Model/
Apply Metadata
Extract &
Load
Publish
Exchange
Explore
Visualize
Report
Analyze
Publish Event
Signal Data
Transformation
Transform &
Aggregate

9.
© Hortonworks Inc. 2012: DO NOT SHARE. CONTAINS HORTONWORKS CONFIDENTIAL & PROPRIETARY INFORMATION
ETL Reference Architecture
Page 9
Organize/Model
Create Metadata
Extract &
Load
Publish
Exchange
Explore
Visualize
Report
Analyze
Publish Event
Signal Data
Transformation
Transform &
Aggregate

10.
HCatalog

11.
© Hortonworks Inc. 2013
HCatalog
Table access
Aligned metadata
REST API
• Raw Hadoop data
• Inconsistent, unknown
• Tool specific access
Apache HCatalog provides flexible metadata
services across tools and external access
Metadata Services with HCatalog
• Consistency of metadata and data models across tools
(MapReduce, Pig, Hbase, and Hive)
• Accessibility: share data as tables in and out of HDFS
• Availability: enables flexible, thin-client access via REST API
Shared table
and schema
management
opens the
platform
Page 11

12.
© Hortonworks Inc. 2013
• Best Practice: Use HCatalog to manage metadata
– Schema/structure when needed via tables and partitions
– Late binding at work: Multiple/changing bindings supported
– Abstract Location of data, scale and maintain over time easily
– Abstract format of data file (e.g.: compression type, HL7 v2, HL7 v3)
• Cope with change of source data seamlessly
– Heterogeneous schemas across partitions within HCatalog as source
system evolves, consumers of data unaffected
– E.g.: Partition ‘2012-01-01’ of Table X has schema with 30 fields and
HL7 v2 format. Partition ‘2013-01-01’ has 35 fields with HL7 v3 format
• RESTful API via WebHCat
Page 12
Step 2 – HCatalog, Metadata

13.
© Hortonworks Inc. 2013
Sample Tweet data as JSON
{
"user":{
"name":"George Vetticaden - Name",
"id":10000000,
"userlocation":"Chicago",
"screenname":"gvetticadenScreenName",
"geoenabled":false
},
"tweetmessage":"hello world",
"createddate":"2013-06-18T11:47:10",
"geolocation":{
"latitude":1000.0,
"longitude":10000.0
}
}

14.
© Hortonworks Inc. 2013
Hive/HCat Schema for the Twitter Data
create external table tweet (
user struct <
userlocation:string,
id:bigint,
name:string,
screenname:string,
geoenabled:string
>,
geoLocation struct <
latitude:float,
longitude:float
>,
tweetmessage string,
createddate string
)
ROW FORMAT SERDE 'org.apache.hcatalog.data.JsonSerDe'
location "/user/kibana/twitter/landing"

15.
© Hortonworks Inc. 2013
Pig Example
Page 15
Count how many time users tweeted an url:
raw = load '/user/kibana/twitter/landing' as (user,
tweetmessage);
botless = filter raw by myudfs.NotABot(user);
grpd = group botless by (url, user);
cntd = foreach grpd generate flatten(url, user), COUNT(botless);
store cntd into '/data/counted/20120530';
Using HCatalog:
raw = load ’tweet' using HCatLoader();
botless = filter raw by myudfs.NotABot(user) and ds == '20120530’;
grpd = group botless by (url, user);
cntd = foreach grpd generate flatten(url, user), COUNT(botless);
store cntd into 'counted' using HCatStorer();
No need to know
file location
No need to
declare schema
Partition filter

16.
© Hortonworks Inc. 2012: DO NOT SHARE. CONTAINS HORTONWORKS CONFIDENTIAL & PROPRIETARY INFORMATION
ETL Reference Architecture
Page 16
Organize/Model
Create Metadata
Extract &
Load
Publish
Exchange
Explore
Visualize
Report
Analyze
Publish Event
Signal Data
Transformation
Transform &
Aggregate

17.
© Hortonworks Inc. 2013
Step 3&4 – Transform, Aggregate, Explore
• MapReduce
– For Programmers
– When control matters
• Hive
– HiveQL (SQL-like) to ad-hoc query and explore data
• Pig
– Pig for declarative data crunching and preprocessing (the T in ELT)
– User Defined Functions (UDF) for extensibility and portability. Ex:
Custom UDF for calling industry-specific data format parsers
(SWIFT, X12, NACHA, HL7, HIPPA, etc.)
• HCatalog
– Consistent metadata, consistent data Sharing across all tools
Page 17

18.
Common Processing Patterns

19.
© Hortonworks Inc. 2013
Common ETL Processing Patterns
• Long-term data retention
• Staging for Data Exploration
• Data Cleansing
• Data Enrichment
Page 19

20.
© Hortonworks Inc. 2013
Important Dimensions to Consider..
• Compression
• Buffering
• Data Format Containers
• Logical Processing Tiers (Raw, Work, Gold, Access)
Page 20

21.
© Hortonworks Inc. 2013
Compression in Hadoop is Important
• Biggest performance bottleneck in Hadoop: Read/Write IO
• Compression formats supported in HDP include gzip, bzip2, LZO, LZ4
and Snappy
• Type of compression to use is based on a number of factors like:
– Size of the data
– Is faster compression/decompression or compression effectiveness more
important (space/time trade-off)? Faster compression/decompression
speeds usually come at the expense of smaller space savings.
– Do compressed files need to be split-able for parallel MapReduce
processing of a large file
Page 21

22.
© Hortonworks Inc. 2013
Suitcase Pattern: Buffering and Compression
• Suitcase Pattern
– Before we travel, we take our clothes off the rack and pack them
(easier to store)
– We then unpack them when we arrive and put them back on the
rack (easier to process)
– Consider event data “traveling” over the network to Hadoop
– we want to compress it before it makes the trip, but in a way that
facilitates how we intend to process it once it arrives
• Suitcase Pattern Implementation
– In Hadoop, generally speaking, several thousand bytes to several
hundred thousand bytes is deemed important
– Buffering records during collection also allows us to compress the
whole block of records as a single record to be sent over the
network to Hadoop – resulting in lower network and file I/O
– Buffering records during collection also helps us handle bursts

23.
© Hortonworks Inc. 2013
Time Series: The Key to MapReduce
• Event data has a natural temporal ordering
– Observations close together in time will be more closely related
than observations further apart
– Time series analysis of events often makes use of the one-way
ordering of time
• Batching by time is a composite pattern
– Batches of records from a single event source (compressed and
written as a single physical record in HDFS) are organized by time
– Physical records in HDFS are organized into files by time
– Metadata can be associated with both to support queries with time-
range predicates
– A sequence of files can be indexed based on the highest timestamp
inside of HCatalog to avoid MapReduce from having to open the file
– A sequence of physical records in a file can be partitioned based on
the highest timestamp (record-level metadata inside a SequenceFile)
to avoid Mappers from having to de-compress the batch

24.
© Hortonworks Inc. 2013
Different Data Format Containers
Page 24
Data Format Description Key Advantages
Sequence File Persistent data structure for binary key-value
pairs.
Row-oriented. This means that fields in each
row are stored together as the contents of as
single sequence-file record
• Split-able
• Compress-able at Block and Row
Level
• Work well as contains for small
files. HDFS and MapReduce are
optimized for large files, so packing
files into a Sequence file makes
storing and processing the smaller
files more efficient
Avro File Similar to sequence files (split-able,
compressible, row-oriented) except they
have support schema evolution and binding
in multiple language
Schema stored in the file itself
• Split-able
• Compress-able at Block and Row
Level
• Ideally suited for unstructured data
sets with constantly changing
attributes/schema
RC File Similar to sequence and avro file but are
column-oriented
• Provides faster access to subset of
columns without doing a full table
scan across all columns
Optimized RC
File
Optimized RC Fileformat supporting sql like
types and has more efficient serialization/
deserialization
• Provides faster access in Next
Generation MR
• HIVE-3874

25.
© Hortonworks Inc. 2013
Best Practices for Processing Patterns
Page 25
Processing
Pattern
Tier
Path
Data
Format
Compres
sion
Description
Long-term data
retention
Raw à
Gold
Avro
Sequence
Gzip/bzip2 Conversion of all raw data into sequence/avro
files with block compression, a useable but
compressed data format. This can also
involve the aggregation of smaller files from
ingestion into large sequence or avro formats.
Staging for Data
Exploration
Raw à
Access
RC, ORC LZO A conversion of subset of raw input
normalized tables into an access-optimized
data structure like RC file.
Data Cleansing Raw à
Work
Txt(Raw
format)
None Common ETL cleansing operations (e.g:
discarding bad data, scrubbing, sanitizing)
Data Enrichment Raw à
Work
Sequence LZO, None Aggregations or calculation of fields based on
analysis of data within Hadoop or other
information pulled from other sources
ingested into Hadoop.

26.
© Hortonworks Inc. 2013
The Question that You are dying to Ask..
What Tooling do I have to do orchestrate
these ETL flows?
Page 26

27.
© Hortonworks Inc. 2013
Falcon: One-stop Shop for Data Lifecycle
Apache Falcon
Provides Orchestrates
Data Management Needs Tools
Multi Cluster Management Oozie
Replication Sqoop
Scheduling Distcp
Data Reprocessing Flume
Dependency Management Map / Reduce
Hive and Pig Jobs
Falcon provides a single interface to orchestrate data lifecycle.
Sophisticated DLM easily added to Hadoop applications.

28.
© Hortonworks Inc. 2013
Falcon Usage At A Glance.
> Falcon provides the key services data processing applications need.
> Complex data processing logic handled by Falcon instead of hard-coded in apps.
> Faster development and higher quality for ETL, reporting and other data
processing apps on Hadoop.
Hortonworks Data Management Product (Herd, Continiuum)
(or Data Processing Applications, Customer Management Software)
Spec Files or
REST APIs
Data Import
and
Replication
Scheduling
and
Coordination
Data Lifecycle
Policies
Multi-Cluster
Management
SLA
Management
Falcon Data Lifecycle Management Service

29.
© Hortonworks Inc. 2013
Falcon Example: Multi-Cluster Failover
> Falcon manages workflow, replication or both.
> Enables business continuity without requiring full data reprocessing.
> Failover clusters require less storage and CPU.
Staged
Data
Cleansed
Data
Conformed
Data
Presented
Data
Staged
Data
Presented
Data
BI and Analytics
Primary Hadoop Cluster
Failover Hadoop Cluster
Replication

30.
© Hortonworks Inc. 2013
Example – Data Lifecycle Management
• User creates entities using DSL
– Cluster for Primary, Cluster for Secondary (BCP)
– Data Set
– Submits to Falcon (RESTful API)
• Falcon orchestrates these into scheduled workflows
– Maintains the dependencies and relationships between entities
– Instruments workflows for dependencies, retry logic, Table/
Partition registration, notifications, etc.
– Creates a scheduled recurring workflow for
– Copying data from source to target(s)
– Purging expired data on source and target(s)
<cluster colo=”colo-1" description="test cluster" name=”cluster-primary"
xmlns="uri:ivory:cluster:0.1”>
<interfaces>
<interface type="readonly" endpoint="hftp://localhost:50070"
version="1.1.1"/>
<interface type="write" endpoint="hdfs://localhost:54310” version="1.1.1"/>
<interface type="execute" endpoint="localhost:54311" version="1.1.1"/>
<interface type="workflow" endpoint="http://localhost:11000/oozie/"
version="3.3.0"/>
<interface type="messaging" endpoint="tcp://localhost:61616?daemon=true"
version="5.1.6"/>
</interfaces>
</cluster>
<feed description="TestHourlySummary" name="TestHourlySummary” xmlns="uri:ivory:feed:0.1">
<partitions/>
<groups>bi</groups>
<frequency>hours(1)</frequency>
<late-arrival cut-off="hours(4)"/>
<clusters>
<cluster name=”cluster-primary" type="source">
<validity start="2012-01-01T00:00Z" end="2099-12-31T00:00Z"/>
<retention limit="days(2)" action="delete"/>
</cluster>
<cluster name=”cluster-BCP" type="target">
<validity start="2012-01-01T00:00Z" end="2099-12-31T00:00Z"/>
<retention limit="days(2)" action="delete"/>
</cluster>
</clusters>
<locations>
<location type="data” path="/projects/test/TestHourlySummary/${YEAR}-${MONTH}-${DAY}-${HOUR}"/>
<location type="stats" path="/none"/>
<location type="meta" path="/none"/>
</locations>
<ACL owner=”venkatesh" group="users" permission="0755"/>
<schema location="/none" provider="none"/>
</feed>

31.
© Hortonworks Inc. 2013
Thanks/Questions…
Page 31