This document summarizes a presentation about the Splice Machine database product. Splice Machine is described as a SQL-on-Hadoop database that is ACID-compliant and can handle both OLTP and OLAP workloads. It provides typical relational database functionality like transactions and SQL on top of Apache Hadoop. Customers reportedly see a 10x improvement in price/performance compared to traditional databases. The presentation provides details on Splice Machine's architecture, performance benchmarks, customer use cases, and support for analytics and business intelligence tools.

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Executive Summary
Scale out is the new Agile
Business needs constant flexibility
No time for down time
Grow as quickly as you can sell

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Analyst: Robin Bloor
Robin Bloor is
Chief Analyst at
The Bloor Group
robin.bloor@bloorgroup.com
@robinbloor

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Splice Machine
! Splice Machine is a SQL-on-Hadoop database
! The product is ACID-compliant and can power both
OLAP and OLTP workloads
! Splice Machine is built on Java-based Apache Derby
and Hbase/Hadoop

9. Guests: John Leach & Rich Reimer
John Leach, Co-Founder and Chief Technology Officer
With over 15 years of software experience under his belt, John’s expertise in
analytics and BI drives his role as Chief Technology Officer. Prior to Splice
Machine, John founded Incite Retail in June 2008 and led the company’s strategy
and development efforts. Prior to Incite Retail, he ran the business intelligence
practice at Blue Martini Software and built strategic partnerships with
integration partners. His focus at Blue Martini was helping clients incorporate
decision support knowledge into their current business processes utilizing
advanced algorithms and machine learning.
Rich Reimer, VP of Marketing and Product Management
Rich has over 15 years of sales, marketing and management experience in high-tech
Treasure Isle studio head, where he used petabytes of data from millions of daily
users to optimize the business in real-time. Prior to Zynga, he was the COO and
co-founder of a social media platform named Grouply. Before founding Grouply,
Rich held executive positions at Siebel Systems, Blue Martini Software and Oracle
Corporation as well as sales and marketing positions at General Electric and Bell
Atlantic.
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companies. Before joining Splice Machine, Rich worked at Zynga as the
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10. Affordable
Scale-­‐Out
August
5,
2014

11. 11
Data
Doubling
Every
2
Years…
Driven
by
web,
social,
mobile,
and
Internet
of
Things
Source: 2013 IBM Briefing Book

12. 12
TradiBonal
RDBMSs
Overwhelmed…
Scale-­‐up
becoming
cost-­‐prohibi=ve
Oracle
is
too
darn
expensive!
My
DB
is
hiLng
the
wall
Users
keep
geLng
those
spinning
beach
balls
We
have
to
throw
data
away
Our
reports
take
forever

13. 13
Case
Study:
Harte-­‐Hanks
Overview
! Digital
markeBng
services
provider
! Real-­‐Bme
campaign
management
! Complex
OLTP
and
OLAP
environment
Challenges
! Oracle
RAC
too
expensive
to
scale
! Queries
too
slow
–
even
up
to
½
hour
! GeLng
worse
–
expect
30-­‐50%
data
growth
! Looked
for
9
months
for
a
cost-­‐effecBve
soluBon
SoluBon
Diagram
IniBal
Results
10-­‐20x
price/perf
with
no
applicaBon,
BI
or
ETL
rewrites
¼
cost
with
commodity
scale
out
3-­‐7x
faster
through
parallelized
queries
Cross-Channel
Campaigns
Real-Time
Personalization
Real-Time Actions

14. 14
Scale-­‐Out:
The
Future
of
Databases
Drama=c
improvement
in
price/performance
Scale
Up
(Increase
server
size)
Scale
Out
(More
small
servers)
$ vs.
$
$
$
$
$

15. 15
Who
are
We?
THE
ONLY
HADOOP
RDBMS
Replace
your
old
RDBMS
with
a
scale-­‐out
SQL
database
! Affordable,
Scale-­‐Out
! ACID
TransacBons
! No
ApplicaBon
Rewrites
10x
Beier
Price/Perf

16. 16
Customer
Performance
Benchmarks
Typically
10x
price/performance
improvement
30x
3-­‐7x
10-­‐20x
10x
20x
10-­‐15x
7x
5x
SPEED
VS.
PRICE/PERFORMANCE

17. Use
Cases
§ Digital
MarkeBng
§ Campaign
management
§ Unified
Customer
Profile
§ Real-­‐Bme
personalizaBon
§ Data
Lake
§ OperaBonal
reporBng
and
analyBcs
§ OperaBonal
Data
Stores
§ Fraud
DetecBon
§ Personalized
Medicine
§ Internet
of
Things
§ Network
monitoring
§ Cyber-­‐threat
security
§ Wearables
and
sensors
17

18. Seasoned
Team
18
Successful
Serial
Entrepreneurs
Enterprise
So?ware
Experience
Database
&
Big
Data
Experience
Big
Data
Research
&
Community
Leadership
Hadoop
User Group

19. What
People
are
Saying…
19
Recognized
as
a
key
innovator
in
databases
Scaling
out
on
Splice
Machine
presented
some
major
benefits
over
Oracle
...automaBc
balancing
between
clusters...avoiding
the
costly
licensing
issues.
Quotes
Awards
An
alternaKve
to
today’s
RDBMSes,
Splice
Machine
effecBvely
combines
tradiBonal
relaBonal
database
technology
with
the
scale-­‐out
capabiliBes
of
Hadoop.
The
unique
claim
of
…
Splice
Machine
is
that
it
can
run
transacKonal
applicaKons
as
well
as
support
analyBcs
on
top
of
Hadoop.

20. 20
Proven
Building
Blocks:
Hadoop
and
Derby
APACHE
DERBY
§
ANSI
SQL-­‐99
RDBMS
§
Java-­‐based
§
ODBC/JDBC
Compliant
APACHE
HBASE/HDFS
§ Auto-­‐sharding
§ Real-­‐Bme
updates
§ Fault-­‐tolerance
§ Scalability
to
100s
of
PBs
§ Data
replicaBon

21. 21
HBase:
Proven
Scale-­‐Out
§ Auto-­‐sharding
§ Scales
with
commodity
hardware
§ Cost-­‐effecBve
from
GBs
to
PBs
§ High
availability
thru
failover
and
replicaBon
§ LSM-­‐trees

22. 22
Distributed,
Parallelized
Query
ExecuBon
! Parallelized
computaBon
across
cluster
! Moves
computaBon
to
the
data
! UBlizes
HBase
co-­‐processors
! No
MapReduce

23. ANSI
SQL-­‐99
Coverage
23
§ Data
types
–
e.g.,
INTEGER,
REAL,
CHARACTER,
DATE,
BOOLEAN,
BIGINT
§ DDL
–
e.g.,
CREATE
TABLE,
CREATE
SCHEMA,
ALTER
TABLE,
DELETE,
UPDATE
§ Predicates
–
e.g.,
IN,
BETWEEN,
LIKE,
EXISTS
§ DML
–
e.g.,
INSERT,
DELETE,
UPDATE,
SELECT
§ Query
specificaKon
–
e.g.,
SELECT
DISTINCT,
GROUP
BY,
HAVING
§ SET
funcKons
–
e.g.,
UNION,
ABS,
MOD,
ALL,
CHECK
§ AggregaKon
funcKons
–
e.g.,
AVG,
MAX,
COUNT
§ String
funcKons
–
e.g.,
SUBSTRING,
concatenaBon,
UPPER,
LOWER,
POSITION,
TRIM,
LENGTH
§ CondiKonal
funcKons
–
e.g.,
CASE,
searched
CASE
§ Privileges
–
e.g.,
privileges
for
SELECT,
DELETE,
INSERT,
EXECUTE
§ Cursors
–
e.g.,
updatable,
read-­‐only,
posiBoned
DELETE/UPDATE
§ Joins
–
e.g.,
INNER
JOIN,
LEFT
OUTER
JOIN
§ TransacKons
–
e.g.,
COMMIT,
ROLLBACK,
READ
COMMITTED,
REPEATABLE
READ,
READ
UNCOMMITTED,
Snapshot
IsolaBon
§ Sub-­‐queries
§ Triggers
§ User-­‐defined
funcKons
(UDFs)
§ Views
–
including
grouped
views

24. 24
Lockless,
ACID
transacBons
State-­‐of-­‐the-­‐Art
Snapshot
Isola=on
Transaction C
! Adds
mulB-­‐row,
mulB-­‐table
transacBons
to
HBase
with
rollback
! Fast,
lockless,
high
concurrency
! ZooKeeper
coordinaBon
! Extends
research
from
Google
Percolator,
Yahoo
Labs,
U
of
Waterloo
Transaction A
Transaction B
Ts Tc

25. 25
BI
and
SQL
tool
support
via
ODBC
No
applica=on
rewrites
needed

26. 26
Who
are
We?
THE
ONLY
HADOOP
RDBMS
Replace
your
old
RDBMS
with
a
scale-­‐out
SQL
database
! Affordable,
Scale-­‐Out
! ACID
TransacBons
! No
ApplicaBon
Rewrites
10x
Beier
Price/Perf

27. Thank
You!

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Perceptions & Questions
Analyst:
Robin Bloor

29. Hadoop as a
Data Refinery?
Robin Bloor, PhD

30. Data Flow – A Set of Principles
u The data layer is one logical collection of data,
both external and internal
u The data flows, from ingest through a refining
process to a point of application
u It is best if data doesn’t flow much
u “Vanilla Hadoop” is a viable catching & refining
vehicle
u Beyond that a database is required to manage
workloads

31. Big Data Architecture

32. Data Refining

33. The Data Engines
STREAMING DATA
OLTP
LARGE QUERY
LARGE ANALYTICAL QUERY
SQL, JSON, SPARQL QUERIES

34. u How does Splice Machine organize its data?
u Is this an OLTP database or a BI database? Or can
it be both at the same time?
u What do you see as the sweet spot for this
database:
• In respect of Big Data?
• In respect of business applications?

35. u Is Splice Machine also suited for analytical
applications?
u Do you also find yourselves competing with
NoSQL products?
u In respect of scale, what is your largest
implementation by data volume and by
transaction rate?

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37. This Month: BIG DATA ECOSYSTEM
September: INTEGRATION & DATA FLOW
October: ANALYTIC PLATFORMS
www.insideanalysis.com/webcasts/the-briefing-room
Twitter Tag: #briefr
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Upcoming Topics
2014 Editorial Calendar at
www.insideanalysis.com

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THANK YOU
for your
ATTENTION!
Opening slide image courtesy of Wikimedia Commons
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