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1 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
Making	Enterprise	Big	Data	
Small	with	Ease
Nagapriya Tiruthani,	Offering	Manager,	IBM	Db2	Big	SQL
Roni	Fontaine,	Director,	Product	Marketing,	Hortonworks
March	28,	2018
2 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
Presenters
Nagapriya Tiruthani
Offering	Manager
IBM	Db2	Big	SQL
Roni	Fontaine
Director	of	Product	Marketing
HDP,	Operations	and	Cloud
3 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
Agenda
ÃData	today	and	its	benefits
ÃDb2	Big	SQL	Use	cases
ÃDb2	Big	SQL’s	Federation	and	how	it	works
ÃHow	Hive	can	Help
ÃDb2	Big	SQL	and	Hive	Working	Together
ÃResources	/	Q	&	A
Need to accommodate
volume, variety and
velocity of data
Mobile and IoT are
driving need
for more real-time,
accurate information
Increased adoption of
advanced analytics and
self-service discovery
Need for agile data
services with high
levels of security
Rules	of	the	Game	have	Changed
© 2018 IBM Corporation
• Business	Intelligence
• Extending	a	warehouse	to	include	outside	data
• Prototyping	new	applications	using	disparate	data
• Handling	mergers	and	acquisitions	as	well	as	staged	migrations	between	databases
• Warehouse	or	ODS	maintenance	(trickle-feed)
• Business	Integration
• Real-time,	targeted	access	to	multiple	operational	databases
• Replicate/consolidate	distributed	data	to	a	central	store
• Portal	development	–access	disparate	data	and	make	available	for	Web	deployment
• Warehouse	Augmentation
• Capacity	Relief	
• Queryable archive
• Multi-temperate	business	models
• Migrate	dimensioned	data	from	3rd party	RDBMS
• Discovery	&	Exploration
5© 2018 IBM Corporation
Benefits	of	Bringing	all	the	Data	Together
SQL
Ad-hoc	
queries,	data	
preparation
Federation
Transactional	
with	fast	
lookups
High	
performance
Machine	
learning
Complex	SQL,	
Deep	
analytics,
Many	users
Application	
portability
Db2	Big	SQL	Use	Cases
©	2018	IBM	Corporation
Db2 Big SQL
Data lakes Object stores NoSQL RDBMS
Federation:		Eliminating	the	Information	Bottleneck
© 2018 IBM Corporation
Transparent
§ Appears to be one source
§ Programmers don’t need to know how /
where data is stored
Heterogeneous
§ Accesses data from diverse sources
High Function
§ Full query support against all data
§ Capabilities of sources as well
Autonomous
§ Non-disruptive to data sources, existing
applications, systems.
High Performance
§ Optimization of distributed queries
SQLtools&applications
Datasources
Db2 Big SQL
Oracle
SQL	
Server
Teradata
DB2
Others..
SAP Hana
WebHDFS
S3
What	does	Db2	Big	SQL	Federation	get	you?
© 2018 IBM Corporation
Federation	– Virtualize	Heterogeneous	Data	
9
MS	SQL		Server Netezza	(PDA) Oracle PostgreSQL Teradata
DB2
LUW,	Db2z,	DB2	on	i Informix
WebHDFS Object	Store	(S3) Hive HBase HDFS
Hortonworks	Data	Platform	(HDP)
Db2	Big	SQL NoSQL
ML
Model
Db2	Big	SQL	queries	heterogeneous	systems	in	a	single	query
Only	SQL-on-Hadoop that	virtualizes	more	than	10	different	data	sources:	RDBMS,	NoSQL,	HDFS	or	Object	
Store
© 2018 IBM Corporation
Optimizer	Flow	for	federated	queries
10
© 2018 IBM Corporation
Pushdown analysis is only part of the optimizer’s job.
1. Rewrite user queries (syntactic transformations)
2. Select best local execution plan
3. Generate correct SQL for remote query fragments
(relational sources)
Parse Query
Rewrite Query
Cost-based Plan
Selection
Remote SQL
Generation
Pushdown Analysis
(relational nicknames)
Code Generation, local
query portions
• Db2	Big	SQL	decides	whether	some	or	all	parts	of	a	query	can	be	"pushed-down",	
i.e.	processed	at	the	remote	data	source(s).	Pushdown-ability	depends	on
• availability	of	needed	functionality	at	remote	source
• server	options	(example:		is	collating	sequence	at	Federated	server	and	remote	source	the	
same?)	
• Example:		A	remote	source	that	can	handle	an	equality	predicate,	but	not	
count(*)....	
11
“Pushdown”	of	Query	Operations
© 2018 IBM Corporation
Select count(*) from t1
where col = 27
Select count(*) from t1
where col = 27
Application IBM database Non-IBM database
Select ‘1’ from t1
where col = 27
• Just	because	processing	can	be	pushed	down	doesn't	mean	it	will	be.	Decision	influenced	by	estimates	of	rows	
processed/returned.	
• Consider	a	join	of	two	nicknames	ORA.T1	and	ORA.T2	on	a	single	remote	source	that	is	"nearly"	a	Cartesian	product.	
May	be	better	to	do	the	join	at	the	Db2	Big	SQL	engine	to	avoid	retrieval	of	many	rows.	
• Retrieving	(10,000	+	25)	rows	to	do	a	local	join	is	probably	faster	than	retrieving		(10,000	*	25)	=	250,000-row	remote	
join	result	
12
Actual	Pushdown	is	Cost	Based
© 2018 IBM Corporation
ORA.T1 (25 rows)
ORA.T2 (10,000 rows)
Select … from ORA.T1, ORA.T2
where T1.a = T2.b;
✓ Easily access information on demand
✓ Combine data in Hadoop with
disparate sources to form a data lake
✓ Quickly extend your data warehouse
by enriching it
Connect Query Monitor Data	Placement
§ Quick	access	to	Data	value
§ Common	Framework	
§ ODBC/JDBC
§ Spark integration	enables	new	
data	sources
§ Connect	all	data	sources	in	single	
query
§ Intelligent	Query	Routing
§ Cost-based optimizer
§ SQL	pushdown
§ Local	data	caching
§ ANSI-compliant	SQL
§ Easily	define	&	manage	
through	a	common	UI
§ Simple	point	&	click	to
discover	and	query
§ Monitor	and	visualize	active	
queries
§ Schema	conversion	when	
moving	data
§ Bulk	data	copy to	Hadoop
§ Filtered	subsets	of	data	
Rich	Capabilities	that	Brings	Data	Together
© 2018 IBM Corporation
Query Augment Monitor Security Performance
§ Intelligent	Query	
Routing
§ Cost-based optimizer
§ SQL	pushdown
§ ANSI-compliant	SQL
§ Quick	access	to	Data	
value
§ Query	with	open	
source	Hadoop	file	
formats
§ Access data	in	RDBMS	&	
NoSQL	data	sources
§ Operationalize	ML	
models
§ Spark	integration	for	in-
memory	data	exchange
§ Local	data	caching	for	
federated	queries	using	
MQTs
§ Automatic	memory	
manager	manages	
queries	to	completion
§ Manage	workloads	and	
prioritize
§ Audit	queries	
§ Simple	point	&	click	to
discover	and	query
§ Monitor	and	visualize	
active	queries
§ Granular SQL	level	
access	control	for	row
filtering	and	column	
masking
§ Define	policies	in	Ranger	
for	centralized	security	
management
§ Create	MQTs	to	cache	
data	aggregate	for	fast	
response
§ Enable	elastic	boost	to	
maximize	resources	
consumption	and	parallel	
execution
✓ Executes complex queries with high performance
✓ Combine data in Hadoop with disparate sources to form
a data lake
✓ Enables reusing application and skills
IBM	Db2	Big	SQL
One engine for all enterprise needs on Hadoop
©	2018	IBM	Corporation
• With	Db2	Big	SQL,	users	can	focus	on	what	they	want	to	do,	and	not	
worry	about	how	it	is	executed
• Data	Scientists/Business	Analysts	can	be	3-4	times	more	productive	using	
Db2	Big	SQL	compared	to	Spark	SQL.	
• Proof	points:
• Able	to	successfully	run	all	99	TPC-DS	queries	@	100TB	in	4-concurrent	streams
• Performance	leadership
• Uses	fewer	cluster	resources
• Simpler	configuration	with	mature	self-tuning	and	workload	management	features
Db2	Big	SQL	is	the	best	SQL	over	Hadoop	engine	for	complex	analytical	
workloads
To	Summarize
©	2018	IBM	Corporation
16 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
How	Hive	Helps
17 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
HDP	2.6:	A	Major	Milestone	for	Apache	Hive
à Major	Improvements:
• Hive	LLAP	Now	GA
• ACID	MERGE
• SQL:	All	99	TPC-DS	out-of-the-box	with	only	
trivial	rewrites
• Hive	View	2.0:	Great	Features	for	DBAs
• Diagnostics:	Tez	UI	Total	Timeline	View
• Hive	OLAP	Indexes	powered	by	Druid
à At	a	High	Level:
• 1200+	features,	improvements	and	bug	fixes	
in	Hive	since	HDP	2.5.
• 400+	of	these	from	outside	of	Hortonworks.
820
413
From	Hortonworks
From	Community
HDP	2.6	Improvements
Hive	LLAP	GA+
SQL	MERGE+
All	TPC-DS	Queries+
18 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
HDP	2.6	Makes	Hadoop	Data	Management	a	Reality	with	SQL	MERGE
• Hive	implements	ANSI-standard	SQL	MERGE.
• MERGE	makes	data	maintenance	8x	simpler	with	5x	higher	performance.
• Legacy	Hive	or	Spark	approaches	don’t	protect	applications	against	dirty	reads	or	partial	
failures.
Complexity	of	a	Type	2	SCD	Update with	and	without	MERGE
Number	of
Queries
Number	of
Full	Table	Scans
Isolation
Applications
Protected	From
Partial Failures
Hive	MERGE 1 1 Yes Yes
Old	Techniques 8 5 No No
19 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
Using	Hive	&	Db2	Big	SQL
20 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
Application	Portability	&	Integration
Data	shared	with	Hadoop	ecosystem
Comprehensive	file	format	support
Superior	enablement	of	IBM	and	Third	Party	software
Performance
Modern	MPP	runtime
Powerful	SQL	query	rewriter
Cost	based	optimizer
Optimized	for	concurrent	user	throughput
Results	not	constrained	by	memory
Federation
Distributed	requests	to	multiple	data	sources	within	a	
single	SQL	statement
Main	data	sources	supported:
DB2,	Teradata,	Oracle,	Netezza,	
Informix,	SQL	Server
Enterprise	Features
Advanced	security/auditing
Resource	and	workload	management
Self	tuning	memory	management
Comprehensive	monitoring
Rich	SQL
Comprehensive	SQL	Support
IBM	SQL	PL	compatibility
Extensive	Analytic	Functions
How	Db2	Big	SQL	Complements	Hive
21 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
Breaking	Things	Down:	Where	IBM	Db2	Big	SQL	Shines
Run	Oracle,	DB2	or	Netezza Workloads	on	Hadoop+
Federate	Hadoop	and	Non-Hadoop	Data+
Complex	SQL	Workloads+
22 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
Breaking	Things	Down:	Where	Apache	Hive	Shines
Fast	SQL	That	Scales	from	Terabytes	to	Petabytes+
Easy	to	Keep	Data	Fresh	with	ACID	MERGE+
Join	Historical	and	Streaming	Data	in	Real	Time+
23 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
Resources
24 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
Resources
• HWX	Db2	Big	SQL	Web	Page:	lhttps://hortonworks.com/partners/ibm-bigsql/
• Db2	Big	SQL	Solutions	Sheet:	https://2xbbhjxc6wk3v21p62t8n4d4-wpengine.netdna-
ssl.com/wp-content/uploads/2017/08/IBM-Big-SQL-Solution-Sheet_final.pdf
• Db2	Big	SQL	Data	Sheet	https://2xbbhjxc6wk3v21p62t8n4d4-wpengine.netdna-
ssl.com/wp-content/uploads/2017/08/IBM-Big-SQL-Datasheet_final.pdf
• Db2	Big	SQL	Resources
• Db2	Big	SQL	Web	Page/Sandbox		https://www.ibm.com/us-en/marketplace/big-sql
• Db2	Big	SQL	Master	Class	Videos	
https://www.youtube.com/playlist?list=PL7FnN5oi7Ez9itAnZ6rs9A30YYjVB1wN_
• Db2	Big	SQL	Blog:	https://hortonworks.com/blog/big-sql-apache-hadoop-across-
enterprise/
25 ©	Hortonworks	Inc.	2011–2018.	All	rights	reserved
Thank	you

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