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1 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved1 ©	Hortonworks	Inc.	2011	–2017.	All	Rights	Reserved
Scott	Gnau CTO,	Hortonworks	@Scott_Gnau
David	Loshin,	President,	Knowledge	Integrity
loshin@knowledge-integrity.com
Legacy	Architectures	Impede	Performance
EDW
Capital	
Costs
Operations	
Costs
Scalability
Analytic	
Flexibility
Time	to	
Value
Data	
Quality
Data	
Variety
©	2017	Knowledge	Integrity,	Inc	loshin@knowledge-integrity.com	 (301)	754-6350	 2
• Data	warehouse	
performance	is	no	longer	
solely	defined	in	terms	of	
computation	speed
• Optimal	performance	
reflects	the	ability	to	
maximize	value	across	a	
range	of	dimensions	
• The	static	design	of	legacy	
platforms	has	not	kept	
pace	with	growing	desire	
for	business	intelligence	
and	analytics
Step	1:	Leverage	Horizontal	Scalability
• DW	appliances	require	
significant	capital	investment
– System	must	be	sized	to	meet	
anticipated	needs
– Allows	for	unused	capacity	at	
beginning
– Requires	increased	“step-up”	
investments	on	regular	intervals
• Hadoop	finesses	this	challenge
– Relies	on	commodity	
components
– Start	with	what	you	need,	grow	
with	increased	demand
– Introduce	newer	hardware	
seamlessly
– Exploit	innovations	to	speed	
performance	(e.g.,	Stinger.next,	
Low	Latency	Analytical	
Processing)
©	2017	Knowledge	Integrity,	Inc	loshin@knowledge-integrity.com	 (301)	754-6350	 3
Rack	switch
NameNode
DataNode	&	
TaskTracker
DataNode	&	
TaskTracker
DataNode	&	
TaskTracker
DataNode	&	
TaskTracker
Rack	switch
NameNode
DataNode	&	
TaskTracker
DataNode	&	
TaskTracker
DataNode	&	
TaskTracker
DataNode	&	
TaskTracker
Rack	switch
NameNode
DataNode	&	
TaskTracker
DataNode	&	
TaskTracker
DataNode	&	
TaskTracker
DataNode	&	
TaskTracker
Rack	switch
NameNode
DataNode	&	
TaskTracker
DataNode	&	
TaskTracker
DataNode	&	
TaskTracker
DataNode	&	
TaskTracker
Step	2:	Augment	EDW	Storage	with	Hive
• The	value	of	existing	EDW	
investments	can	be	extended	
using	a	Hybrid	Architecture
• Hive	continues	to	evolve	with	
innovative	performance	
improvements:
– In-memory	caching	and	
persistent	query	executors
– Column-oriented	distributed	
data	organization
– Improved	security	using	
Apache	Ranger
– SQL	ACID	Merge
©	2017	Knowledge	Integrity,	Inc	loshin@knowledge-integrity.com	 (301)	754-6350	 4
Hadoop	
Cluster
EDW
Step	3:	Increase	Data	Flexibility	
• Conventional	data	warehouse	architectures	are	organized	using	a	dimensional	model
– Facts	represent	events
– Dimensions	characterize	the	facts
• The	dimensional	model	is	suited	to	typical	DW	operations
– Aggregation	and	rolled-up	reporting
– “Slice	and	dice”
• However,	this	model	forces	all	data	into	predetermined	schema	(“schema-on-write”)
– Introduces	bias,	creates	constraints	and	limits	data	flexibility
• Alternative:	schema-on-read
– Data	sets	are	captured	in	their	source	formats
– Frees	data	consumers	to	apply	their	own	organization
– Allows	logical	structure	to	be	layered	on	top	of	data	in	source	format
– Enables	use	of	creative	algorithms	for	analytics,	text	mining,	and	machine	learning
©	2017	Knowledge	Integrity,	Inc	loshin@knowledge-integrity.com	 (301)	754-6350	 5
Step	4:	Use	Unstructured	Data
• Data	warehouses	are	engineered	around	structured	data
• Many	sources	of	increasing	volume	of	unstructured	data
– Apps	running	on	Internet-connected	devices	generate	text	streams
– Machine-generated	unstructured	content
– Semi-structured	sources
• Applications	that	consume	both	structured	and	unstructured	
data	provide	fuller	visibility	into	analytical	results
• Tools	like	Lucene,	Solr,	Mahout,	and	other	text	analytics	
libraries	help	to	parse	and	tag	unstructured	text
©	2017	Knowledge	Integrity,	Inc	loshin@knowledge-integrity.com	 (301)	754-6350	 6
Ingest
Parse
Tag
Organize
Lucene
Solr
Mahout
Step	5:	Data	Discovery
©	2017	Knowledge	Integrity,	Inc	loshin@knowledge-integrity.com	 (301)	754-6350	 7
Data	Ingestion	
&	
Transformation
• Data	imported	into	the	data	warehouse	is	
homogenized	and	organized	within	predefined	
data	models
• This	constrains	downstream	consumers
Step	5:	Data	Discovery
©	2017	Knowledge	Integrity,	Inc	loshin@knowledge-integrity.com	 (301)	754-6350	 8
Data	Discovery	
&	Preparation
Data	Discovery	
&	Preparation
Data	Discovery	
&	Preparation
Data	Discovery	
&	Preparation
Data	Discovery	
&	Preparation
• Data	discovery	allows	each	user	to	configure	the	
data	for	their	specialized	purposes
Step	6:	Offload	ETL	to	Hadoop
• 60-70%	of	the	effort	of	data	warehousing	is	attributed	to	extraction,	transformation,	
and	loading	(ETL)
• Hadoop	is	a	natural	platform	for	ETL	processing:
– ETL	is	inherently	data	parallel,	enabling	faster	execution
– Development	time	can	be	drastically	reduced	with	faster	dev/test/debug	cycle
– Resources	can	be	dynamically	apportioned	and	released	when	ETL	processing	is	completed,	
lowering	costs
• Apache	Hive	supports	SQL	ACID	Merge	which	handles	inserts,	updates,	and	deletes	
in	a	single	pass
• Allows	for	in-database	transformations	without	need	for	massive	refreshes
©	2017	Knowledge	Integrity,	Inc	loshin@knowledge-integrity.com	 (301)	754-6350	 9
Step	7:	Operational	Data	Governance
• Delegating	more	responsibility	to	the	consumer	community	poses	a	risk	of	
inconsistent	interpretation	and	use
• Institute	operational	data	governance	to	support	versioning,	lineage,	and	
provenance
– Metadata	management
– Data	lineage
– Archiving	policies
– Versioning	policies
– Data	security	and	protection
• Apache	Atlas	is	an	open	source	component	of	the	Hadoop	ecosystem	that	captures	
data	definitions,	hierarchical	taxonomies,	data	elements	and	their	relationships,	and		
lineage
©	2017	Knowledge	Integrity,	Inc	loshin@knowledge-integrity.com	 (301)	754-6350	 10
Modernization:	Evolving	the	Hybrid	EDW
• Conventional	RDBMS-based	data	warehouses	have	served	organizations	well,	but	
are	being	eclipsed	by	newer	technologies
• Scalable	systems	built	on	commodity	components	are	rapidly	being	adopted	for	
business	intelligence	and	analytics	applications
• Optimize	the	EDW	using	an	evolutionary	approach	to	embracing	Hadoop:
– Expand	the	storage	footprint
– Increase	computational	power
– Broaden	the	scope	of	application	support
– Lower	costs
©	2017	Knowledge	Integrity,	Inc	loshin@knowledge-integrity.com	 (301)	754-6350	 11
Questions	&	Suggestions
• www.knowledge-integrity.com
• www.dataqualitybook.com
• www.decisionworx.com
• If	you	have	questions,	comments,	
or	suggestions,	please	contact	me
David	Loshin
301-754-6350
loshin@knowledge-integrity.com
©	2017	Knowledge	Integrity,	Inc	loshin@knowledge-integrity.com	 (301)	754-6350	 12
13 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
The	Next	Gen	EDW	is	the	Big	Data	Warehouse
à In	Forrester’s	2016	global	survey,	59%	of	respondents	stated	that	leveraging	big	data	
and	analytics	was	a	critical	or	high	priority.
14 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Companies	Are	Looking	to	Big	Data	for	EDW	Optimization	
à 82%	of	2550+	respondents	are	looking	to	Big	Data	for	EDW	Optimization	rather	than	a	
straight	replacement.	– 2016	Big	Data	Maturity	Survey
15 ©	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
16 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Drivers	of	a	Modern	BI	Infrastructure
Deeper	and	
Broader	Data	Sets	
Complete	Data	
‘Provenance’
Leading	Analytics	
and	Tools
Integrate	non-EDW	
data	and	EDW	data
Total	Cost	of	
Ownership
17 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Open	Source	Transformational	Impact	to	EDW
Unmatched	Economics
support	low	cost	data-center	and	cloud	
architectures	for	Enterprise	Apache	
Hadoop
Eliminates	Risk	and	Ensures	Integration
prevents	vendor	lock-in	and	speeds	
ecosystem	adoption	of	ODPi-compliant	
core
COST
EFFICIENCY
DATA
VARIETY
EDW
PROPRIETARY
HADOOP
HORTONWORKS	
OPEN	SOURCE	
RDBMS
18 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
But,	why	aren’t	more	companies	running	to	this	solution?
Risky
Hadoop	requires	a	bunch	of	
new	skill	sets
It’ll	take	a	long	time
There’s	too	much	manual	coding	required
It’s	hard	to	integrate	to	
my	BI	tool	stack
19 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Legacy	EDW	Solution
20 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Using	Hadoop	to	Optimize	the	Data	Warehouse		
à Augment	EDW	with	Hive
à Offload	ETL	to	Hadoop
à Data	Governance
21 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Augment	current	EDW	with	Hive	
Hive	LLAP	GA:	Interactive	query	in	seconds,	10X	fast	join	performance
Ease	of	Use	and	Adoption	:	SQL	Standard	ACID	Merge		
Enterprise	Readiness:	Supports	all	TPC-DS	Queries	
Streamlined	Operations:	Hive	Views
22 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
0
5
10
15
20
25
30
35
40
45
50
0
50
100
150
200
250
Speedup	(x	Factor)
Query	Time(s)	(Lower	is	Better)
Hive	2	with	LLAP	averages	26x	faster	than	Hive	1
Hive	1	/	Tez	Time	(s) Hive	2	/	LLAP	Time(s) Speedup	(x	Factor)
Hive	2	with	LLAP:	26x	Performance	Boost	at	1TB	Scale
23 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Hive	LLAP	in	HDP	2.6:	Stable	Performance	with	High	Concurrency
4x	Queries,
2.8x
Runtime
Difference
5x	Queries,
4.6x
Runtime
Difference
Mark
Concurrent
Queries
Average
Runtime
5 7.76s
25 36.24s
100 102.89s
24 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Offload	ETL	to	Hadoop	
à The	Problem:
– EDWs	can	consume	between	50%	and	90%	of	
resources	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.
– 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
25 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Data	Governance	for	EDW	Optimization	
Classification
Prohibition
Time
Location
Policies
PDP
Resource
Cache
Ranger
Manage	Access	Policies	
and	Audit	Logs
Track	Metadata
and	Lineage
Atlas	Client
Subscribers
to	Topic
Gets	Metadata
Updates
Atlas
Metastore
Tags
Assets
Entitles
Streams
Pipelines
Feeds
Hive
Tables
HDFS
Files
HBase
Tables
Entities
in	Data
Lake
Industry	First:	Dynamic	Tag-based	Security	Policies
26 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Use	Case	1:	Multi-Channel	Behavioral	Analysis
à Industry:	Mass	Media
– Largest	broadcasting	and	cable	company	
in	the	world	by	revenue
– Multiple	channels:	Cable	(set-top-box),	
wireless	devices,	streaming	
programming,	
– 22	million+	subscribers	(internet	&	
video)
à Results:
– Scalability:	480B	rows,	500	nodes
– 60x	query	performance	improvement
– Insights:	New	info	improve	negations
– Loyalty:		Outreach	to	customers	viewing	
competitive	streams;	▼churn	▲
revenue
Before After
Leading	Media	Company
Hortonworks	HDP
AtScale	Intelligence	Server
Hortonworks	HDP
Netezza Data	Mart
Channel	Feeds
Tableau	+	MS	Excel	+	R
Channel	Feeds
Tableau	+	MS	Excel
27 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Use	Case	2:	Campaign	Paid-Search	Effectiveness
à Industry:	Retail	/	eCommerce
– Top	US	department	store	(by	rev)
– Online	sales	$4B+	&	growing	(11%+	total)
– 800+	department	stores	nationwide
à Results
– Scale:		Millions	paid	keywords		analyzed	
– Speed:		Eliminate	extract	step	
– Insight:	Operationalized		closed-loop	
analysis	à insight	à decision	à action		
– Impact:		Make	and	save	$	millions	w/	
instant	bid	decisions	over	6-week	season		
à that	drives	60%	annual	revenue
Before After
Hortonworks	HDP
AtScale	Intelligence	Server
Hortonworks	HDP
Vertica Data	Marts
Ad	&	Paid	Keywords
Cognos +	Tableau	+	Excel
Ad	&	Paid	Keywords
Tableau	+	Excel
Leading	Retailer
28 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Use	Case	3:	Client	and	Patient	Analysis
à Industry:	Managed	Health	Care
– Member	of	Fortune	100
– Health,	life	+	other	insurance	products
– ~	52	million	members;	
medical/dental/pharm
à Results
– Scalable:	BI	directly	on	264+	nodes	data
– Time:	 Eliminate	data	movement	 step
– 62x	query	performance	improvement
– Speed:		<2.2	second	average	query	time	
– Insight:		Tableau	on	Hadoop	for	1000+	
– Security:		Access	control	by	user;	HIPAA
Before After
Leading	Managed	Healthcare	Provider
Hortonworks	HDP
AtScale	Intelligence	Server
Hortonworks	HDP
Netezza Data	Mart
Client	/	Patient	Details
Tableau	+	MS	Excel
Client	/	Patient	Details
Tableau	+	MS	Excel
29 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Next	Step:	
à Everyone	will	receive	a	free	copy	of	Forrester	White	Paper	titled	”The	Next-Generation	
EDW	Is	The	Big	Data	Warehouse”		
à EDW	Optimization	with	HDP
– http://hortonworks.com/solutions/edw-optimization/
– EDW	Optimization	7	min	video
30 ©	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
31 ©	Hortonworks	Inc.	2011	–2016.	All	Rights	Reserved
Thank	You

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