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1 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved1 ©	Hortonworks	Inc.	2011	–2017.	All	Rights	Reserved
2 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
How Customers are Optimizing their EDW
for Fast, Secure, Cost Effective Actionable Insights
Wei Wang
Sr. Director, Product Marketing
Paige Roberts
Big Data Product Manager
Speakers:
3 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Actionable	
Insights
Human	
Connections
Balanced
Supply	Chains
New	Products
&	Services
Operational
Efficiencies
Data	Is	The	New	Currency
4 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
The	Old	Way
Operational	Efficiencies
Hierarchical
Historical
Highly Aggregated
One-size-fits-all
The	New	Way
Engagement	and	Innovation
Multi-structured
Predictive
Agile &	Real-time
Context Sensitive
Transforming	The	Enterprise	Data	Warehouse	(EDW)
5 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Hortonworks	Connected	Data	Platforms	and	Solutions
Hortonworks
Connection
Hortonworks	Solutions
Enterprise	Data
Warehouse	Optimization
Cyber	Security	and
Threat	Management
Internet	of	Things
and	Streaming	Analytics
Hortonworks	Connection
Subscription	Support
SmartSense
Premier	Support
Educational	Services
Professional	Services
Community	Connection
Cloud
Hortonworks	 Data	Cloud
AWS HDInsight
Data	Center
Hortonworks	 Data	Suite
HDFHDP
6 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
The	Solution	for	Enterprise	Data	Warehouse	Optimization
Dramatic	Cost	Reductions	Through	the
Power	of	Open	Source	and	Apache	Hadoop
Reduce	cost	of	your	EDW	Implementation	by	
augmentation	or	replacement	of	ETL	processes
Deploy	Business	Intelligence	on	Hadoop
Enablement	Business	users	(via	a	desktop	
solution)	see	aggregate	data,	drill	up,	drill	down	
through	business	data	models.
Enrich	with	Unstructured	Data	Support
Deliver	support	for	photos,	video,	text	files	to	
enrich	your	analytics
7 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Legacy	EDW	vs.	EDW	Optimization	Solution	with	Connected	Data	Platforms
910
920
930
940
950
960
970
980
990
1,000
Difficulty
transforming data
into a suitable
form for analysis.
Difficulty
integrating
Big Data with
existing
infrastructure.
Difficulty
merging
multiple,
disparate
data sources.
Lack of skilled
Big Data
practitioners.
Difficulty
maintaining
application
performance for
large volume of
concurentusers.
Implementing	the	Modern	Data	Architecture	Isn’t	Easy
8
Source:	Wikibon Big	Data	Analytics	Adoption	 Survey,	2014-2015
Syncsort Confidential andProprietary - do not copy or distribute
Of	the	selected	technology-related	
barriers	to	realizing	the	full	value	of	your	
Big	Data	Analytics,	please	rank	the	top	3.
910
920
930
940
950
960
970
980
990
1,000
Difficulty
transforming data
into a suitable
form for analysis.
Difficulty
integrating
Big Data with
existing
infrastructure.
Difficulty
merging
multiple,
disparate
data sources.
Lack of skilled
Big Data
practitioners.
Difficulty
maintaining
application
performance for
large volume of
concurentusers.
Implementing	the	Modern	Data	Architecture	Isn’t	Easy
9
Additional	Challenges
• Long,	drawn-out	development	cycles
Source:	Wikibon Big	Data	Analytics	Adoption	 Survey,	2014-2015
9Syncsort Confidential andProprietary - do not copy or distribute
Of	the	selected	technology-related	
barriers	to	realizing	the	full	value	of	your	
Big	Data	Analytics,	please	rank	the	top	3.
910
920
930
940
950
960
970
980
990
1,000
Difficulty
transforming data
into a suitable
form for analysis.
Difficulty
integrating
Big Data with
existing
infrastructure.
Difficulty
merging
multiple,
disparate
data sources.
Lack of skilled
Big Data
practitioners.
Difficulty
maintaining
application
performance for
large volume of
concurentusers.
Implementing	the	Modern	Data	Architecture	Isn’t	Easy
10
Additional	Challenges
• Long,	drawn-out	development	cycles
• Rapidly	changing	technology	presents	a	
moving	target,	forcing	constant	re-design
Source:	Wikibon Big	Data	Analytics	Adoption	 Survey,	2014-2015
10Syncsort Confidential andProprietary - do not copy or distribute
Of	the	selected	technology-related	
barriers	to	realizing	the	full	value	of	your	
Big	Data	Analytics,	please	rank	the	top	3.
910
920
930
940
950
960
970
980
990
1,000
Difficulty
transforming data
into a suitable
form for analysis.
Difficulty
integrating
Big Data with
existing
infrastructure.
Difficulty
merging
multiple,
disparate
data sources.
Lack of skilled
Big Data
practitioners.
Difficulty
maintaining
application
performance for
large volume of
concurentusers.
Implementing	the	Modern	Data	Architecture	Isn’t	Easy
11
Additional	Challenges
• Long,	drawn-out	development	cycles
• Rapidly	changing	technology	presents	a	
moving	target,	forcing	constant	re-design
• Difficulty	accessing	and	integrating	legacy	
and	new	data	sources	including	the	
mainframe
Source:	Wikibon Big	Data	Analytics	Adoption	 Survey,	2014-2015
11Syncsort Confidential andProprietary - do not copy or distribute
Of	the	selected	technology-related	
barriers	to	realizing	the	full	value	of	your	
Big	Data	Analytics,	please	rank	the	top	3.
910
920
930
940
950
960
970
980
990
1,000
Difficulty
transforming data
into a suitable
form for analysis.
Difficulty
integrating
Big Data with
existing
infrastructure.
Difficulty
merging
multiple,
disparate
data sources.
Lack of skilled
Big Data
practitioners.
Difficulty
maintaining
application
performance for
large volume of
concurentusers.
Implementing	the	Modern	Data	Architecture	Isn’t	Easy
12
Additional	Challenges
• Long,	drawn-out	development	cycles
• Rapidly	changing	technology	presents	a	
moving	target,	forcing	constant	re-design
• Difficulty	accessing	and	integrating	legacy	
and	new	data	sources	including	the	
mainframe
• Functionality	gaps	in	security,	governance,	
and	compliance
Source:	Wikibon Big	Data	Analytics	Adoption	 Survey,	2014-2015
12Syncsort Confidential andProprietary - do not copy or distribute
Of	the	selected	technology-related	
barriers	to	realizing	the	full	value	of	your	
Big	Data	Analytics,	please	rank	the	top	3.
910
920
930
940
950
960
970
980
990
1,000
Difficulty
transforming data
into a suitable
form for analysis.
Difficulty
integrating
Big Data with
existing
infrastructure.
Difficulty
merging
multiple,
disparate
data sources.
Lack of skilled
Big Data
practitioners.
Difficulty
maintaining
application
performance for
large volume of
concurentusers.
Implementing	the	Modern	Data	Architecture	Isn’t	Easy
13
Additional	Challenges
• Long,	drawn-out	development	cycles
• Rapidly	changing	technology	presents	a	
moving	target,	forcing	constant	re-design
• Difficulty	accessing	and	integrating	legacy	
and	new	data	sources	including	the	
mainframe
• Functionality	gaps	in	security,	governance,	
and	compliance
Source:	Wikibon Big	Data	Analytics	Adoption	 Survey,	2014-2015
13Syncsort Confidential andProprietary - do not copy or distribute
Big	data	integration	
is	complicated!
Of	the	selected	technology-related	
barriers	to	realizing	the	full	value	of	your	
Big	Data	Analytics,	please	rank	the	top	3.
14 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Hortonworks	EDW	Optimization	Solution	Components
Hadoop
Scalable	Storage	and	Compute
Hive	LLAP
High	Performance	SQL	Data	Mart
Fast,	scalable	SQL	analytics
Intelligent	in-memory	caching
15 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Hortonworks	EDW	Optimization	Solution	Components
Syncsort
High-Performance	
Data	Movement
Hadoop
Scalable	Storage	and	Compute
Hive	LLAP
High	Performance	SQL	Data	Mart
Source	Data	
Systems
Fast,	scalable	SQL	analytics
Intelligent	in-memory	caching
High	performance	data	import
from all	major	EDW	platforms
16 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Hortonworks	EDW	Optimization	Solution	Components
Syncsort
High-Performance	
Data	Movement
Hadoop
Scalable	Storage	and	Compute
Hive	LLAP
High	Performance	SQL	Data	Mart
AtScale	Intelligence	Platform
OLAP	Cubes	for	Higher	Performance
Source	Data	
Systems
Fast,	scalable	SQL	analytics
Intelligent	in-memory	caching
Define	OLAP	cubes	for	10x	faster	queries
Unified	semantic	layer	for	all	BI	tools
High	performance	data	import
from all	major	EDW	platforms
Pre-aggregated
data
...	Or,	full-fidelity
data
17 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
EDW	Optimization:	Fast	BI	on	Hadoop
à The	Problem:
– Proprietary	EDW	systems	were	adopted	for	
Fast	BI	and	deep	slice-and-dice	analytics,	but	
EDW	prices	are	unsustainably	high.
à The	Solution:
– Interactive	SQL	is	a	reality	on	Hadoop	today.
– AtScale	Intelligence	Platform	adds	OLAP	
capabilities	for	deep	drilldown	at	scale.
à The	Result:
– Query	terabytes	of	data	in	seconds.
– Connect	your	favorite	BI	tools	like	Tableau	and	
Excel	through	SQL	and	MDX	interfaces.
– The	EDW	Optimization	Solution	is	tailor-made	
to	deliver	Fast	BI	on	Hadoop.
ETL/ELT
DATA
MART
DATA
LANDING	&
DEEP
ARCHIVE
CUBE
MART
END	USER
APPLICATIONS
APPLICATIONS
APPLICATIONS
END	USERS
AND	APPS
EDW	OPTIMIZATION	SOLUTION
18 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
EDW	Optimization:	ETL	Offload
à The	Problem:
– EDWs	consume	between	50%	and	90%	of	
CPU	just	on	ETL/ELT	tasks.
– These	jobs	interfere	with	more	business-
critical	tasks	like	BI	and	advanced	analytics.
à The	Solution:
– Hive	and	HDP	deliver	ETL	that	scales	to	
petabytes.
– Syncsort	DMX-h	for	simple	drag-and-drop	ETL	
workflows.
– Economical	scale-out	processing	on	
commodity	servers.
à The	Result:
– Better	SLAs	for	mission-critical	analytics.
– Limit	EDW	expansion	or	retire	old	systems.
ETL/ELT
DATA
MART
DATA
LANDING	&
DEEP
ARCHIVE
CUBE
MART
END	USER
APPLICATIONS
APPLICATIONS
APPLICATIONS
END	USERS
AND	APPS
EDW	OPTIMIZATION	SOLUTION
19 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
EDW	Optimization:	Active	Archive
à The	Problem:
– Increasing	data	volumes	and	cost	pressure	
force	data	to	be	archived	to	tape.
– Archived	data	not	available	for	analytics,	or	
must	be	retrieved	at	great	expense.
à The	Solution:
– Adopting	Hadoop	delivers	cost	per	terabyte	
on	par	with	tape	backup	solutions.
– Data	in	Hadoop	can	be	analyzed	by	all	major	
BI	tools,	allowing	analytics	on	archive	data.
à The	Result:
– Data	always	available	for	analytics.
– Store	years	of	data	rather	than	months.
ETL/ELT
DATA
MART
DATA
LANDING	&
DEEP
ARCHIVE
CUBE
MART
END	USER
APPLICATIONS
APPLICATIONS
APPLICATIONS
END	USERS
AND	APPS
EDW	OPTIMIZATION	SOLUTION
Powering	the	Connected	Data	
Platform	With	EDW	Optimization
Paige Roberts
Big Data Product Manager
@RobertsPaige
Goals	of	the	Modern	Data	Architecture
•Centralize	all	your	data
•Turn	raw	data	into	insights
•Maintain	governance,	compliance	and	
security	standards
•Eliminate	complexities	within	IT
21
Syncsort	Strategic	Focus	on	Big	Data	&	Hadoop
Light	footprint
Self-tuning	
engine
Single	install.	No	
3rd party	
dependencies
World-class	data	processing,		
mainframe	expertise
JIRA:
MAPREDUCE-2454
MAPREDUCE-4807
MAPREDUCE-4049
MAPREDUCE-5455
HIVE-8347
SQOOP-1272
PARQUET-134
Spark-packages
and more!
|
22
Syncsort Confidential andProprietary - do not copy or distribute
Ongoing	Contributions	to	the
Open	Source	Community1
Leverage	Syncsort	Technology
Innovations	&	Mainframe	Heritage2
Strong	Partnerships	with	Strategic	
Big	Data	&	Hadoop	Players
1
3
22
à Connect	to	virtual	any	data	source,	including	
mainframe	and	MPP	databases.
à Move	data	into	and	out	of	Hadoop	up	to	6x	
faster	without	the	need	for	manual	scripts.
à Develop	ETL	processes	without	writing	code.
à Automatically	optimize	Hadoop	performance	
and	scalability	for	ETL	operations.
à Fully	certified	and	integrated	with	
Hortonworks	Data	Platform	and	HDF
à Secure	– Kerberos,	Ranger,	Sentry
Syncsort	Benefits
Syncsort: High Performance Import from Existing Sources
Bring	ALL	Enterprise	Data	Securely	to	the	Data	Lake
24
• Collect	virtually	any	data	from	mainframe	to	
relational,	cloud	and	NoSQL	sources
• Access,	re-format	and	load	data	directly	into	Hive	&	
Hadoop	file	formats.	No	staging	required!
• Batch	&	streaming	sources
• Pull	hundreds	of	tables	at	once	into	your	data	hub,	
whole	DB	schemas	in	one	invocation
24Syncsort Confidential andProprietary - do not copy or distribute
•Load	more	data	into	Hadoop	in	less	time
Get	Your	Database	data	into	Hadoop,	At	the	Press	of	a	Button
25
• Pull	multiple	data	sources	and	funnel	into	your	data	lake	
• Extract	and	map	whole	DB	schemas	in	one	invocation
• Extract	from	multiple	data	sources:	DB2/z,	Netezza,	Oracle,	
Teradata,…
• One-step	data	movement,	auto-generating	jobs,	auto-generating	
Hive	target	tables,	and	update	Hive	statistics
• Process	multiple	funnels	in	parallel	on	your	edge	node	or from	
data	nodes
‒ Leverages	DMX-h	high	speed	data	engine	via	DTL
‒ Generated	applications	can	be	imported	into	GUI
• In-flight	transformations
‒ Filtering,	funnel	dependency	ordering,	mixed	source/target,	data	type	
filtering,	table	exclusion/inclusion
25Syncsort Confidential andProprietary - do not copy or distribute
DMX							
DataFunnel™
Intelligent	
Execution	
Layer
Design	Once,	Deploy	Anywhere
26
Intelligent	Execution	- Insulate	your	people	from	underlying	complexities	of	Hadoop.	
One	interface	to	design	jobs	to	run	on:
Single	Node,	Cluster
MapReduce,	Spark,	Future	Platforms
Windows,	Unix,	Linux	
On-Premise,	Cloud
Batch,	Streaming
• Use	existing	ETL	skills.
• No	worries	about	mappers,	reducers,	big	side,	small	side,	and	so	on.
• Automatic	optimization	for	best	performance,	load	balancing,	etc.
• No	changes	or	tuning	required,	even	if	you	change	execution	frameworks
• Future-proof	job	designs	for	emerging	compute	frameworks,	e.g.	Spark
Insurance:	Easy	Access	to	ALL	Data	for	Better	Analytics
27
• Challenge: Needed	hard-to-access	operational	data	for	advanced	
analytics
• Solution:
• Quickly	load	~1000	database	tables	into	HDP	with	the	click	of	a	
button
• Access	&	integrate	complex	Mainframe	VSAM	files,	data	from	
DB2/z,	Oracle	&	SQL	Server
• Track	changes	&	keep	data	up	to	date
• Benefits:
• Insight: Better	and	faster	analytics
• Agility: Reclaim	development	time;	single	tool	to	ingest,	detect	changes	and	populate	the	data	lake
• Compliance: Build	audit	trails,	keep	HDP	data	lake	current
• Productivity:	No	need	for	deep	understanding	of	Hadoop
Hotel	Chain:	Ease	of	Use,	Timely	&	Up-to-Date	Reporting
28
• Challenge: More	timely	collection	&	reporting	on	room	
availability,	event	bookings,	inventory	and	other	hotel	data	from	
4,000+	properties	globally
• Solution:	
• Near	real-time	reporting
• DMX-h	consumes	property	updates	from	Kafka	every	10s
• DMX-h	processes	data	on	HDP,	loading	to	TD	every	30	min
• Deployed	on	Google	Cloud	Platform
• Benefits:
• Time	to	Value:	DMX-h	ease	of	use	drastically	cut	development	time
• Agility:	Reports	updated	every	30	minutes	vs	every	24	hours
• Productivity:	Leveraging	ETL	team	for	Hadoop	(Spark),	visual	understanding	of	data	pipeline
• Insight: Up-to-date	data	=	better	business	decisions	=	happier	customers
Leading	Media	Company:	Accelerate	New	Business	Initiatives
29
• Challenge: Build	scalable	platform	to	support	new	business	
initiatives	&	scale	for	double-digit	data	growth,	while	reducing	
escalating	EDW	&	ELT	Costs
• Solution:
• Shift	data	storage	&	processing	out	of	the	EDW	into	Hadoop
• Migrate	500+	SQL	ELT	workloads	to	DMX-h	on	HDP
• Benefits:
• Agility: Scalable	architecture	to	deploy	new	business	initiatives	– analyze	more	set	top	box	data,	blend	
website	user	activity	data,	etc.
• Cost: Millions	of	dollars	in	savings	from	EDW,	including	SQL	tuning	&	maintenance	costs
• Productivity:	ETL	developers	can	stop	coding	&	tuning,	and	get	up	&	running	on	Hadoop	quickly
30 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Q	/	A
Learn	More:
à EDW	Optimization	with	HDP
– http://hortonworks.com/solutions/edw-optimization/
– EDW	Optimization	7	min	video	
à Try	Syncsort DMX-h:	syncsort.com/try
– Your	existing	cluster	or use	our	fully	functional	Hortonworks	Sandbox
– Get	a	jump-start	with	our	library	of	pre-built	jobs,	including	Use	Case	Accelerators	for	Hadoop	
ETL	and	HDFS	Extract	&	Load	
à Syncsort DMX-h
– http://hortonworks.com/partner/syncsort/
31 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
Thank You

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