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Democratizing	Big	Data	with	
Microsoft	Azure	HDInsight
Saptak	Sen
Solution	Engineering	Manager
Hortonworks
@saptak
NishantThacker
Technical	Product	Manager	–Big	Data
Microsoft
@nishantthacker
Hortonworks	+	Microsoft:	Together	Since	2012
"At	Hortonworks	we	have	seen	more	and	more	Hadoop	related	work	loads	and	
applications	move	to	the	cloud.	Starting	in	HDP	2.6,	we	are	adopting	a	“Cloud	
First”	strategy	in	which	our	platform	will	be	available	on	our	cloud	platforms	–
Azure	HDInsight	at	the	same	time	or	even	before	it	is	available	on	traditional	
on-premises	settings.	With	this	in	mind,	we	are	very	excited	that	Microsoft	and	
Hortonworks	will	empower	Azure	HDInsight	customers	to	be	the	first	to	
benefit	from	our	HDP	2.6	innovation	in	the	near	future."
- Arun Murthy,	co-founder,	Hortonworks	(February,	2017)
“Operating	a	fully	managed	cloud	service	like	Azure	HDInsight,	which	is	
backed	by	an	enterprise	grade	SLA,	requires	that	we	can	deploy	the	latest	bits	
of	Hadoop	&	Apache	Spark	on	demand.	To	that	end,	we	are	excited	that	the	
latest	Hortonworks	Data	Platform	2.6	will	be	continuously	available	to	Azure	
HDInsight	even	before	its	on-premise release.	Hortonworks’	commitment	to	
being	cloud	first	is	especially	 significant	given	the	growing	importance	of	
cloud	with	Hadoop	and	Spark	workloads.”
- Dharma	Shukla,	Distinguished	Engineer	and	General	 Manager	at	Microsoft.	
(February,2017)
Big	Data	in	the	
Cloud
3
Big	Data	in	the	
Cloud
4
Traditional	Clusters
5
Challenges	with	implementing	clusters
Hadoop	Clusters	in	the	Cloud
7
Why	Hadoop	in	the	cloud?
Distributed Storage
• Files	split	across	storage
• Files	replicated
• Nearest	node	responds
• Abstracted	Administration
Hadoop/Spark	Clusters
Extensible
• APIs	to	extend	functionality
• Add	new	capabilities
• Allow	for	inclusion	in	custom	
environments
Automated Failover
• Unmonitored	failover	to	replicated	data
• Built	for	resiliency
• Metadata	stored	for	later	retrieval
Hyper-Scale
• Add	resources	as	desired
• Built	to	include	commodity	configs
• Direct	correlation	of	performance	and	
resources
Distributed Compute
• Distributed	processing
• Resource	Utilization
• Cost-Efficient	method	calls
9
Distributed Storage
• Files	split	across	storage
• Files	replicated
• Nearest	node	responds
• Abstracted	Administration
Cloud
Extensible
• APIs	to	extend	functionality
• Add	new	capabilities
• Allow	for	inclusion	in	custom	
environments
Automated Failover
• Unmonitored	failover	to	replicated	data
• Built	for	resiliency
• Metadata	stored	for	later	retrieval
Hyper-Scale
• Add	resources	as	desired
• Built	to	include	commodity	configs
• Direct	correlation	of	performance	and	
resources
Distributed Compute
• Distributed	processing
• Resource	Utilization
• Cost-Efficient	method	calls
10
Distributed Storage
• Files	split	across	storage
• Files	replicated
• Nearest	node	responds
• Abstracted	Administration
Big	Data	in	the	Cloud
Extensible
• APIs	to	extend	functionality
• Add	new	capabilities
• Allow	for	inclusion	in	custom	
environments
Automated Failover
• Unmonitored	failover	to	replicated	data
• Built	for	resiliency
• Metadata	stored	for	later	retrieval
Hyper-Scale
• Add	resources	as	desired
• Built	to	include	commodity	configs
• Direct	correlation	of	performance	and	
resources
Distributed Compute
• Distributed	processing
• Resource	Utilization
• Cost-Efficient	method	calls
11
HDInsight	Provides	Purpose-built	Cluster	Types
Cluster	Type Components	
Hadoop HDFS,	MapReduce2,	YARN,	Tez,	Hive,	Pig,	Sqoop,	Oozie,	Zookeeper,	
Ambari Metrics,	Slider
HBase HDFS,	MapReduce2,	YARN,	Tez,	Hive,	HBase, Phoenix	Query	Server,	
Pig,	Sqoop,	Oozie,	Zookeeper,	Ambari Metrics
Storm HDFS,	MapReduce2,	YARN,	Tez,	Hive,	Pig,	Sqoop,	Oozie,	Zookeeper,	
Storm,	Ambari Metrics,	Kafka,	
Spark HDFS,	MapReduce2,	YARN,	Tez,	Hive,	Pig,	Sqoop,	Oozie,	Zookeeper,
Ambari Metrics, Spark,	Zeppelin,	Livy
Interactive	Hive HDFS,	MapReduce2,	YARN,	Tez,	Hive	2	LLAP,	Pig,	Sqoop,	Oozie,	
Zookeeper,	Ambari	Metrics,	Slider
R	Server	 HDFS,	MapReduce2,	YARN,	Tez,	Hive,	Pig,	Sqoop,	Oozie,	Zookeeper,
Ambari Metrics, Spark,	Livy
Kafka HDFS,	MapReduce2,	YARN,	Tez,	Hive,	Pig,	Sqoop,	Oozie,	Zookeeper,	
Ambari Metrics,	Kafka
• Components	marked	in	RED	are	the	components	that	
drive	the	cluster	type	use	case
• Spark	clusters	also	have	Jupyter installed
• All	clusters	come	HA	enabled	by	default
Big	Data	in	the	
Cloud
13
Big	Data	in	the	Cloud	- Options
Scenarios	for	deploying	as	hybrid
Traditional	Clusters	– On	Prem
16
Hadoop	Cluster
Worker	Node	
HDFS
HDFS HDFS
Tasks Tasks Tasks Tasks Tasks Tasks
Task	Tracker
Master	Node
Client
Job			(jar)	file
Job			(jar)	file
Clusters	in	the	Cloud
Azure	
HDInsight
Hadoop	and	Spark	as	a	
Service	on	Azure
Fully	managed	Hadoop	and	Spark	for	the	cloud
100%	Open	Source	Hortonworks	Data	Platform
Clusters	up	and	running	in	minutes	
Managed,	monitored	and	supported	by	Microsoft	
with	the	industry’s	best	enterprise	SLA
Use	familiar	BI	tools	for	analysis,	or	open	source	
notebooks	for	interactive	data	science
63%	lower	total	cost	of	ownership	than	deploy	your	
own	Hadoop	on-premises*
*IDC	study	“The	Business	Value	and	TCO	Advantage	of	Apache	Hadoop	in	the	Cloud	with	Microsoft	Azure	HDInsight”
HDInsight	Cluster
Azure	Data	Lake	Storage
HDInsight	cluster
Domain	credentials
Azure	Storage	Blob	
Head	node
Back-up
Data	node
HDInsight	Cluster	Security
AAD	tenant
Azure	VNET	to	
VNET	peering
HDInsight	Cluster
Azure	Data	Lake	Storage
Domain	credentials
Azure	Storage	Blob	
Head	node
Back-up
Data	node
Decoupling	- Benefits
What’s	New	in	HDInsight	3.6
• HDInsight	3.6	GA	announced	during	DataWorks
Summit	Munich
• “HDInsight	3.6	has	the	latest	Hortonworks	Data	
Platform	(HDP)	2.6	platform,	a	collaborative	effort	
between	Microsoft	and	Hortonworks	to	bring	HDP	to	
market	cloud-first. ”
• https://azure.microsoft.com/en-us/blog/announcing-
general-availability-of-azure-hdinsight-3-6/
What’s	New	in	HDInsight	3.6
• Interactive	Hive	improvements
• Spark	2.1	GA*
• Zeppelin	added	to	Spark	Cluster	Type
• Improved	cluster	creation	time
*	GA	means	clusters	are	backed	by	Azure	SLA
Big	Data	in	the	
Cloud
24
25
Big	Data	Application	Architecture
The	Azure	Architecture
Source	A
Source	B
Source	C
Data	Factory
Azure	Data	Lake	Store
Source	D
Powershell
Stream	
Analytics
HDInsight
Azure	Data	Lake	Analytics
Azure	SQL	Data	
Warehouse
Azure	Analysis	
Services
Ingestion Backend Frontend
Push	
Stream
DAX
T-SQL
HiveQL
Analyst
Analyst
Analyst
Analyst
The	Azure	Architecture	- Detailed
27
Example:	Big	Data	in	Telco
Telarix uses	big	data	to	help	maintain	call	
quality
“Carriers	are	going	to	create	new	wireless	applications	and	
offerings—voice,	video,	MMS,	or	whatever	the	next	great	
application	is—and	our	customers’	networks	need	to	be	able	
to	support	this.”
Vic	Bozzo,	Senior	VP	of	Worldwide	Sales	and	Marketing
Scenario
Telarix helps	telecommunications	carriers	worldwide	maintain	
call	quality,	manage	costs,	and	streamline	traffic.	Telarix’s suite	
handles	traffic	and	quality	management,	trading,	routing,	
billing,	and	settlement	for	more	than	300	billion	 voice,	SMS,	
content,	and	data	minutes	each	year.	
Solution
Telarix used	SQL	Server	and	Azure	HDInsight	with	the	ability	to	
analyze	large	volumes	of	structured	and	unstructured	data	in	
real	time.	
Result
• Keep	up	with	Carriers	who	are	creating	new	wireless	
applications	and	offerings,	such	as	voice,	video,	MMS.	Telarix
will	provide	these	carriers	the	same	business	 process	to	
trade,	route,	settle,	manage,	invoice,	bill,	and	collect,	across	
all	of	their	services
Linkury uses	big	data	to	make	online	
content	discovery	profitable	for	search	and	
social	engines,	publishers,	and	marketers
Scenario
Linkury is	a	tool	to	help	monetization	of	the	online	advertising	
market. They	needed	to	analyze	hundreds	of	millions	 of	web	
traffic	events	each	day	to	help	build	targeted	advertising based	
on	customer	behavior
Solution
Azure	HDInsight (Hadoop-as-a-service)	 with Storm	for	HDInsight	
to	analyze	real-time	data	in	Hadoop.
Result
• Linkury now	captures	hundreds	 of	millions	 of	web	traffic	
events	in	real-time	including	how	users	browse/actions,	
interact	with	the	device,	products,	etc.	to	display targeted	
online	advertisements.
• Can	now	show	advertising	effectiveness	 through	third	party	
BI	tools	that	show	key	metrics
“We	had	gained	a	lot	of	traffic,	but	we	couldn’t	really	manage	
and	analyze	the	data	in	real	time.	Now	we	have	regained	
control,	which	means,	for	example,	that	we	can	spend	more	
time	analyzing	fraud	or	ad	campaigns	that	are	performing	
poorly”	
Kobi	Eldar,	CTO
Example:	Big	Data	used	for	
targeted	customer	advertisement
Example:	Big	Data	used	for	
connected	cars
Delphi	Automotive	uses	big	data	for	car	
owners	to	keep	tabs	on	their	cars		
“With	Delphi	Connect,	car	owners	can	find	out	how	close	to	
home	their	spouse	is	so	they	can	put	the	finishing	touches	on	
dinner.	They	can	keep	tabs	on	teenage	drivers	by	setting	up	
geo-fences.	If	the	car	goes	outside	of	a	geo-fence	or	drives	
faster	than	a	specified	speed	limit,	mom	or	dad	receives	an	
email	or	text	message.”
Victor	Canseco,	Managing	Director	
Scenario
Delphi	is a	leading	global	supplier	of	technologies	for	the	
automotive	industry,	introduced	Delphi	Connect,	an	after-
market	connected-car	product	that	lets	drivers	digitally	interact	
with	their	cars	through	smartphones,	 tablets,	and	PCs.	
Solution
Azure	HDInsight	and	SQL	Server	in	an	Internet	of	Things	(IoT)	
scenario	for	capturing	and	analyzing	data	from	cars	(vehicle	
diagnostics,	geo-fencing,	geo-location,	mileage	tracking,	
bluetooth).	Also	use	Azure	Service	Bus,	and	SQL	Database	to	
understand	geo-fencing	around	a	map.
Result
• Drivers	can	now	understand	information	on	their	cars	like	
how	they	were	driven,	whey	they	parked,	route	they	took,	
duration,	and	mileage.	They	also	get real-time	information on	
what	other	drivers	are	doing	with	their	car.
Summary
31
Call	to	Action
Points	to	remember
CONNECT
• Contacts:
• sales@hortonworks.com
• Docs	and	Forums:
• https://docs.microsoft.com/en-
us/azure/hdinsight/
• https://azure.microsoft.com/en-
us/support/forums/
Connect	and	voice	your	
customers’	opinion
Ramp	up	on	our	new	services	
NOW!!
32
EVOLVE
• Know	more
• http://www.microsoft.com/hdinsight
• Leverage	free	trial	on	Azure
• https://azure.microsoft.com/en-
us/free/
• Try	Hortonworks	Sandbox	on	Azure
• http://hortonworks.com/sandbox
LEARN
• http://learnanalytics.microsoft.com/
• Trainings	on	
• Spark	in	Azure	HDInsight
• Azure	HDInsight	Administration	and	
Security
• R	Server	on	Azure	HDInsight
©	2016	Microsoft	Corporation.	All	rights	reserved.

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