The Briefing Room: Sybase IQ
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The Briefing Room: Sybase IQ

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The analyst on this was Rajeev Rawat, the focus was Sybase IQ.

The analyst on this was Rajeev Rawat, the focus was Sybase IQ.

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The Briefing Room: Sybase IQ The Briefing Room: Sybase IQ Presentation Transcript

  • Eric Kavanagh, director and host of The Briefing Room is living in New Orleans these days. He is the Host of Information Management’s DM Radio, as well as the Expresso Webcast series. As CEO of the Bloor Group, he consults with technology companies and consultancies, helping them understand, define and deliver their message to the world of information managers. Formerly, he helped design and launch the TDWI Webcast series. He can be reached at dmradio@sourcemedia.com Twitter Tag: #brief
  • Mission Reveal the essential characteristics of enterprise software, good and bad Provide a forum for detailed analysis of today’s innovative technologies Give vendors a chance to explain their product to savvy analysts Allow audience members to pose serious questions... and get answers! Twitter Tag: #brief
  • Rules of Engagement Vendors submit 5-7 slides; one must show architecture. Analyst gets one week to review slides, prepare 1-4 slide response Host provides context: What is this and why should you care? Vendor gives detailed presentation of the technology Analyst presents response, then the formal briefing begins At 40 mins, the host begins a full Q&A with audience & Tweeters Twitter Tag: #brief
  • Topic of the Month: Database Next Month’s Focus! Data Integration Twitter Tag: #brief
  • Consolidation in the BI Market Total BI Market SAP acquires Business (including the database Objects and Sybase market): $100B IBM acquires Cognos, Strong competition SPSS and now appliance between SAP, IBM, vendor Netezza Microsoft & Oracle Should BI databases Oracle acquires SUN and /data warehouses be trumpets H/W based appliances? solution Or is the better Microsoft acquires solution to provided by DATAllegro. software alone? Twitter Tag: #brief
  • Rajeev Rawat is the founder and CEO of BI Results. His career has involved leading large cross-functional teams at both IBM and Xerox, where he was involved in direct customer facing roles as well as taking part in headquarters assignments. His headquarters positions with worldwide responsibility included strategic assignments for alliances and relationships with technology partners, product management and product marketing. Other responsibilities include restructuring business models, test of new technology platforms, and sales coverage plans. Rajeev led the introduction of new technologies and solutions for Xerox and IBM. www.biresults.com, biresult@gmail.com LinkedIn: Rajeev Rawat Twitter Tag: #brief
  • Tom Traubitz is Sybase’s Director of IQ Product Marketing with SAP Sybase’s Infrastructure Platform Group, specializing in enterprise transaction processing and data analytics.  He has spent the past 25 years designing, engineering, testing, and marketing large scale, networked information management systems for a wealth of clients throughout the United States and the world.   Joydeep Das is a Director at Sybase, responsible for Analystics Product Management. He manages Sybase’s Data Warehousing and Analytics product line, which includes Database, Data Movement and Application Development Technologies. He was awarded a patent for efficient I/O estimation and access techniques developed for Sybase’s database products.  Twitter Tag: #brief
  • Sybase, Inc. •One of the database industry’s primary innovators •A dominant player in mobile and embedded database market •Was the first to introduce the columnar database to the BI market. •With Sybase IQ, it has the most popular and most mature columnar database product •Recently Acquired by SAP Twitter Tag: #brief
  • BI/DW
MARKET:
SHIFT
HAPPENS From
rear‐view
mirror… …to
windshield Changing
Business
Requirements Changing
Technical
Requirements Changing
Organiza6onal
Reality Track
sub‐markets
and
sales
 opportuni6es
emerging
in
hours
or
 minutes
 Monitor
and
assess
behavior
of
all
 More
Concurrent
Users
 website
visitors
daily DemocraBzaBon
of
Data Calculate
fraud
risk
for
millions
of
 Big
Data customers
hourly Deeper
Analysis Pervasive
BI Calculate
global
risk
exposure
hourly Shorter
Response
Windows Create
personalized
offers
on
the
fly
 for
thousands
of
customers Ubiquitous
AnalyBcs 
–
September
27,
2010
  • THREE
MAJOR
CHALLENGES
FOR
BI/DW Inadequate
Performance Technology
Lock‐in Inflexible
Scalability (Choose
one) Unanswered
Ques6ons Single
Vendor Growing
Data 
Missed
Deadlines 
Complete
Control 
Growing
Complexity Badly
Informed
Decisions All
or
Nothing Growing
User
Base Row‐Based
Solu6ons Appliances MPP
Shared‐Nothing 
–
September
27,
2010
  • SYBASE
IQ
COLUMN
STORE
ANALYTICS
SERVER PRODUCT
PROFILE Standard Language: ANSI
SQL 
Grid
Based
Column
Store
 Fast
Complex
Queries Standard
 Schema
 High
Performance
Data
Loads Connec6vity: Independent: Storage,
User
Scalability ODBC,
JDBC, 3NF,
Star,
Flat Non‐rela6onal
Data
Support 





OLE‐DB
 
 PlaVorm
Agnos6c: Inside:
 Outside:
 Linux,
Unix, Architected
ground
up
for
 



Windows
 Standards
based
open
interfaces
 High
Performance
Analy6cs enables
best‐of‐breed
eco‐System 
–
September
27,
2010
  • SYBASE
IQ
SOLVES
THE
PERFORMANCE
PROBLEM Column
Store Indexing
Technology Load
and
Query
Processing  Data
is
stored
verBcally
–
Each
 TYPE USAGE  Highly
parallel
load
and
query
 column
is
stored
separately Fast Projection Compressed raw data for plans:
tuple
streams
  The
data
is
the
index result sets (Default) segregated,
data
flows
produce
 parallel
streams,
terminaBon
of
  Large
page
sizes
(128K
–
512K) parallel
streams Low Fast Low cardinality data (up  Persistent
Row
IdenBfiers to 1000 unique values)  Many
access
paths
to
the
  Bitmap
driven indexes
and
columns High Non- Aggregation on the fly  Significant
data
compression Group and range searches  Concurrent
querying
aware,
 elasBc
CPU/memory
usage,
 High Group Key fields and groupings delayed
projecBon
driven for cross-tabular Date, Time Date ranges, date part operations Ordering Multi-Column Concatenated indexes SYBASE
IQ 1 2 3 4 5 6 7 8 9 … Grouping r1 Word/Text Key words, terms, or phrase searches and r2 ranking r3 Joining Compare Column comparisons r4 r5 T1 T2 T3 T4 
–
September
27,
2010
  • SYBASE
IQ
SOLVES
THE
LOCK‐IN
PROBLEM Supports Schema
Independent Supports  Stored
Procedures  Star  Most
market
leading
BI
tools
  FuncBons  RelaBonal RelaBonal  
Views  Flat
(Rcubes)  Unix
(Solaris,
HP‐UX,
AIX)  Concurrent
Reads/Writes  Windows
XP
  ANSI
99
SQL
 Connect
Via  Linux
  T‐SQL  ODBC  
XML  JDBC Load
via  OLAP
funcBons  Any
leading
ETL  Open
Client  Full
Text
Searches  Dedicated
ETL  Oledb Storage  Sybase
ReplicaBon
Server  SSD  FC
SCSI  ATA Choose
Any Choose
Any Schema Opera6ng
System Storage Data
Source Processor Use
Case
 
–
September
27,
2010
  • SYBASE
IQ
SOLVES
THE
SCALABILITY
PROBLEM MULTIPLEX
GRID
–
INDEPENDENT
SCALING
WITH
HIGH
CONCURRENCY • Start
small
and
grow
HUGE • AddiBonal
CPUs
scale
linearly
when
added
to
exisBng
 IP
Load
Balancer nodes
–
no
limits
to
#
of
nodes/CPUs • Individual
nodes
can
have
different
configuraBons
 (CPUs,
memory) • Load
balancing
can
be
used
to
spread
out
users
 
Reader
Node across
available
nodes 
Writer
Node 
Writer
Node • DB
Size
is
a
funcBon
of
available
Storage
independent
 of
compute
power Fiber
Channel
Backbone • Single
copy
of
DB
shared
across
mulBple
computer
 nodes;
DB
object(s)
can
be
isolated

 







to
individual
nodes • No
data
redistribuBon
required
during
data
load.
 AutomaBcally
spreads
data
and
indexes
across
all
 Data
Store(SAN) storage
devices 
–
September
27,
2010
  • 

SYBASE
IQ
‐
COLUMN
STORE
ANALYTICS
PLATFORM 

POWERING
THE
NEXT
GENERATION
OF
DATA
WAREHOUSES Sybase
IQ
Engine 







Strong
Encryp6on
&

 
Open
Standards 






Query
and
Load
Parallelism
 











Authen6ca6on



 Connec6vity 









with

workload
balancer
 Administra6on
Framework 






GUI
administra6on
&





 Loading Query 


Scalable
grid
architecture
with Grid
Framework System Op6mizer 










Web
monitoring 













workload
par66on 





Modeling,
Design
for














 



Ver6cal,
Range,
Tablespace,





 





Schema

&
Meta‐data Column
Indexing 
















Par66oning Sub‐system 




Bulk

&
Trickle
Loads
with 







In‐database

Predic6ve
&
 Column
Storage 
















Text
Analy6cs 











ETL
Interface Processor Dic6onary
&
bitmap
compression 





Virtual
Backup
&
Recovery Storage
Area
Network 
–
September
27,
2010
  • THE
SYBASE
IQ
DIFFERENCE 
Performance Openness Scalability Eliminate
performance
 Adapt
to
new
technology
 Scale
Out
/
Scale
Up
 boclenecks. trends. efficiently. 
–
September
27,
2010
  • Perceptions and Questions Twitter Tag: #brief
  • Sybase
Technical
Briefing 1.
Analy6cs
on
"big
data"
is
a
new
reality,
as
well
data
are
doubling
(18
months). ‐

How
does
Sybase
IQ
address
this
“big
data”
addressability
requirement? ‐

How
does
IQ
handle
doubling
of
data
(performance,
HW,
SW,
I/O)? ‐

What
references/tools
can
provide
predictable
esBmates? 2.
Query
parallelism
is
limited
to
a
single
SMP
server.
One
server
can
update
a
table
at
a
6me. 
‐

What
is
the
impact
on
performance
as
data
scales? 
‐

What
tools,
automaBon,
alerts
help
manage
query
contenBon?
 
‐

Does
this
limit
Sybase
IQ
abiliBes
for
operaBonal,
or
real‐Bme
BI? 3.
Sybase
IQ
uses
sophis6cated
indexing
to
op6mize
known
access
paths 
‐
How
does
it
work
with
unanBcipated
or
ad
hoc
queries,
inserts,
and
mulB
column
analysis? 4.
Recent
columnar
RDBMS
entrants
(
Ver6ca,
ParAccel)
use
MPP
to
scale
to
large
data
volumes

 ‐
How
does
IQ
compare
as
data
scale? ‐
How
does
availability
of
data
models
for
IQ
(columnar)
compare
to
tradiBonal
DWs? 

‐
Is
there
an
impact
on
speed
of
implementaBon,
learning,
training,
resources?
 5.
Opera6onal
BI
or
real‐6me
BI
requires
support
for
high
performance
updates
and
concurrent
 



queries
against
newly
added
data 
‐
How
does
Sybase
IQ
address
data
currency? 
‐

TPC‐H
full
disclosure
report
shows
slow
real
Bme
loads
in
columnar
DBs,
how
does
IQ
address
this? 

‐
Is
there
a
cost
penalty? Copyright © 2003-2010, BI Results, LLC. All Rights Reserved
  • Sybase
Technical
Briefing Best
Fit ‐

Workload ‐

Query
Mix ‐

Optimizing
Tools ‐

Concurrent
Users ‐

Applications/Markets ‐

Growth Sybase
IQ Copyright © 2003-2010, BI Results, LLC. All Rights Reserved
  • Twitter Tag: #brief
  • Questions From The Audience! Twitter Tag: #brief
  • Upcoming Topics October: Data Integration November: Master Data Management and Data Governance December: Data Visualization Twitter Tag: #brief
  • Thank You For Your Time