GPU DATA WAREHOUSE

FOR MASSIVEDATA ANALYTICS
Rafi Glickman,APAC Sales Director
1

CORPORATE PROFILEFOUNDED IN 201075+

Employees

10

Patents

Strategic Partnershipwith Alibaba Cloud
HQ in 7 WTC New York | R&D in Tel Aviv
2

Agenda Data is growing exponentially, but your database can’t keep up
 Why Big Data Analytics?
 The Challenge-Extracting Value out of Big Data
 Why existing solutions are not good enough?
 SQream Value Proposition
 Case Studies

3

YOUR DATA STORES ARE

GROWING EXPONENTIALLYGPU
Technology
CPU
Technology

2008<1-4TB

2010

<10TB

2016

TB-PB
4

Customers are looking for more and NEW Insights

Data Size

Data

Insights Quantity& Quality

Analytics

Insight• Data Size = Longer Periods• # of sources = More dimensions

COMPLEXITY# of Sources (Tables)55

Why Big Data Analytics?
 Data is the new oil

 Not a nice to have – a MUST have

 More data , more dimensions, time period moreinsights, more value!

6

What customers are telling us
 We want to become a data driven organization

 We have a lot of data, but analyze only a fraction!

 Our systems were not designed to handle the amount of data Time of data preparation and modeling is growing constantly
 Maintaining and upgrading our existing data warehouse isincreasingly expensive

7

WHY is it so challenging?
Data Sources

Pipelines

Customers
Transactio
nsPositions
Activity???

ETL

PAIN examples

MARKETING
Customer 360⁰ and multichannel experience

PERFORMANCE: Slow / Stuck processes ->Long time to Insight -> Loosing opportunities

FRAUD
Enhancing the fraud
detection algorithms

SIZE: Analyzing only part of the data ->less accurate insights -> Loosing money

COMPLIANCE
Producing capital
adequacy and Risk
reports

COST: Complicated and costly systems ->No scalability / Missing Insights -> Penalties

88Extracting Value out of Big Data
is not that simple !







Most databases can’t keep up with the current amounts of data
Cost
Time to Value
Knowledge
Technical Resources
Technical barriers
9BUT YOUR DATABASE WASN’T
BUILT TO HANDLE THIS LEVEL OF DATA
1970s-1990s

1990-2010

MPP zoneClassic Relational zone

Mongo DB

OracleTeradata
EDW
Vertica

DB2

SQL
ServerNoSQL & Hadoop

2005-2010

In-Memory zone
Aerospike

Kinetica

IBM
IBM
Netezza DB2 BLU VoltDB MapD

Oracle Redshift
Exadata
GPU Database

2010…

Massive Data zone
Hive

SQREAM DB

MemSQL
Relational DB

10

VALUABLE INSIGHTSGO UNDISCOVERED<10%

Data Analyzed

90%
BI Lost11

Valuable Insights go undiscovered
Data Analyzed~10%~90% BIis Lost12

The more data you have , the less % can be analyzed



Data Lake
Size

Data Lake

Analytic Data % of Data

100 TB

10 TB

10 %

100 TB

500 TB20 TB

4%500 TB

1 PB

30 TB

3%

1 PB

10 PB

50 TB

0.5 %

10 PB

Do you believe history has value ?

% of Data
analyzedA full cycle can take ~ 7 years – so to
get the real analytics you need toanalyze 14 years

Any hiccup may take you to the wrong
direction

10%

4%

3%

.5%

13

The value in your Big Data
Potential economic impact of $110 billion in retailbanking industry
 Enable better cross-selling Personalized products
 Dynamic pricing

 Better risk assessment

 More effective marketing14

Why existing solutions are not goodenoughHadoop environment

Appliances environment

15

Challenges within Hadoop
environment A great file system (and not a Data Base), but not an analytic
platform!
 Not designed to solve the Big Data analytics
 Complex environment

Variety of technologies

Army of programmers with larger range of skills
Maintenance of the large amount of code

Many nodes are hard to maintain and manage As a result it does not meet the Big Data analytics challenge !
16Challenges with common appliances(Exadata,Teradata , etc.),Expensive to start with
Very costly to scaleCompute and storage are linked
Inflexible

17

Help your customer s achieve more, better,
faster and previously unobtainable
BUSINESS and OPERATIONAL INSIGHTS
Eliminating Big Data Processing & Analytics
SIZE, SPEED & COST limits

18

SQream Value Proposition






Get more business insights by analyzing more data!
Focus on business insights and not infrastructure
Cost effective
Much faster!
With minimal footprint and administration

19

INTRODUCING SQREAM DB
GPU-ACCELERATED DATA WAREHOUSE

20x
Analyzemore data
With more dimensions

100x
Queriesfaster10%
Costof resources

20

SQream complements MPP systems


Bridge the gap by bringing massive data analysis capabilities to traditional MPP
ecosystems.

Designed for large-scale analytics



Based on GPU technology when scaling with CPUs is not cost-effectiveProvides a fast, cost-effective and energy efficient environmentBoosts analytics performance through MPP (massively parallel computing).

21

SQream main advantages

3

22Fast to get data in - Fast to get data out Use GPU for loading Over 5,000 GPU Compute core in every GPU
 900 GB/s Memory Bandwidth Compress all the data by 1:5
 Collect metadata

23

Real Data Ingestion Examples

24

TPCH Ingestion Examples

Table Name

Row CountRaw sizePART
SUPPLIERPARTSUPPCUSTOMER
NATIONREGIONLINEITEMORDERS
Total

2,000,000,000
100,000,000
8,000,000,000
1,500,000,000
25
559,999,994,267
15,000,000,000
86.6 B

230 GB
14 GB
1.2 TB
230 GB
2.2 KB
384 B
7.2 TB
1.7 TB
10.5 TB

Time
Elapsed(seconds)

324.0023.97
1599.92
360.009922.60
2343.754h3m

Effective Ingest Rate(GB/h,average)
2,456
2,103
2,700
2,300
2,612
2,611
2.6 TB/h25

Typical DBMS Loading

Data
Data
Data
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Data 
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Data 
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Data
Data
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Data
Data
Data
  
Data
Data
Data

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Data
Data
Data
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          Source Data
100TB raw

Indexes




Write to disk
~80TB

Indexing~105 TB• Data not ready for querying. Still need to create projections, materialized views, indexes,etc.
26

SQream DB Loading
SQream DB reads less data from disk, because of 1:5 compression
プライバシー保護のため、この画像の自動ダウンロードをブロックしました。

プライバシー保護のため、この画像の自動ダウンロードをブロックしました。

Data

Data
Data
Data
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Data
Data
Data
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Data
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Data
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100TB raw


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Data
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Data


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Data
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Metadata

Write to disk
~20TB

Data ready~20.1 TB with metadata• SQream DB’s GPU compression is automatic• Less data read = less I/O = faster query• Access raw data directly, without cubes, indexes
27Data Data Data

Data Data Data
Data Data Data

Raw data

Data Data Data

Data Data Data

Data Data Data

Columnar process+ Metadata tagging

Chunking

Automatic adaptive
compression

GPU

Data Data Data

Parallel chunkprocessing

Data Skipping

28

SQream minimizes time to analysis, eliminates complex data
pipelines, and addresses the poor scaling of traditional solutions.

29

Saving hours on Queries and reports

Replacing legacy MPP with a faster, easier to use SQream GPU-powered analytics database
1 HourData Sources

CDR 4G

40 nodesMPP

CDR 3G

ETL
Process

Aggregations

Direct Loading, 2.5TB/h ingest rate
Dozens of Reports

Non CDR

46 seconds with SQream DB
30

Cost Effectiveness We’ve decoupled storage and compute, with GPUs
 Just add more compute power (GPU) or storage
 1U Server can have 20,000 cores!
GPU

GPU

GPU

GPU

GPU

GPU

GPU

GPU

Storage with Shared Disks
31

COMPACT SIZE - HUGE POWER !
 Access with easy-to-use SQL SQream DB is MPP, but in a small package Decoupled compute and storage Small footprint!
 1U server with
GPUs can replace ~100TB of 42U
legacy data warehouse racks

32

Minimal Scaling Impact!
 Cost On performance Hardware cost
 Complexity

Query Performance
Seconds
12
11.5
11
10.5
10

Billion Rows

50

100

150

200

250

300
33

Accelerating

Hadoop34

Hadoop - A GREAT IDEA!
 Designed to store all ‘big data’ on cheap, commodity hardware Designed to solve ‘Google-scale’ issues for search engines
 Deploy code and jobs, rather than moving data. Run code
directly on data

35

… WITH SOME PAIN FOR ANALYTICS
YARN

HCatalog

Not efficient forstructured data

Difficult to use

•

•

•

Struggles withstructured data
Joins very difficult•

Relies on uncommon
skills – Scala,
MapReduce
Finnicky SQL interfaces

Not designed foranalytics••

Serial workloads
Requires carefulmanagement of data

Patchwork of apps••

IT deployment is hard
Difficult to secure
36

BARRIERS FOR ANALYTICS
Built for scaling, not for speed

New queries can take days to prepareDays of work

Data sources

Hadoop storage andprocessing

BI customers

38

COMBINING THE BEST OF BOTH WORLDS Distributed, cost-effective storage
 Well established as a data lake
 Already deployed successfully Well-integrated

 Scalable in both compute & storage High-performance Ad-hoc queries
 Leverages existing SQL skills
 Easy to use

39

COMBINING THE BEST OF BOTH WORLDS40

HIGH PERFORMANCE WITH EXCELLENT SCALABILITY
Delivering on the Hadoop promise of scaling, without the complexity

Data engineers
GPU

GPU

BI and marketing
GPU

GPU

ReportsGPU

GPU

Data scientists
GPU

GPU

Independent compute clusters for service to all lines of businessCentralized scale-out storage
41

The value of sqream db
Ad-Hoc analytics at scale, enabling better business decisions,with fully-featured SQL

More and new revenue streams from the increased insights

Empowering data consumers, simplifying procedures,
streamlining operations and increasing employee productivity

42make the most of YOUR HADOOP INVESTMENT

SQL

Maximize Hadoop investment
Combining Hadoop with SQream
DB maximizes economy ofHadoop with performance of
SQream DB

Full SQL support
Fully-featured SQL support toempower data owners andbusiness users

Fast, performant, scalable
Analyze more data for criticalinsights faster - with SQream
DB
43

Why SQream?
With the power of the GPU, SQream can do in a single node
what others will need a cluster for：• Massive power in a single server ( no need in data distribution)• Very fast data ingestion rates with high ratio of AdaptiveCompression while gathering metadata statistics ( small footprint,
no need in indexes/prebuild aggregation etc.)• Very fast joins ( full normalization, no needin pre modeling based on the final queries)

SQream can take workloads from Hadoop on massive scalewith ease and with minimum footprint

44

Customer Examples• PubMatic：Offloading from Hadoop (via Spark) into CSV/Parquet and load into SQream.

45

- Increased Revenues by Improving Bids
PubMatic deployed SQream DB to unlock more insights from theirHadoop cluster.
Hundreds of Hadoop Nodes!
Extract

Queries take5 hours

Extract

Load

GPU

Data Sources

Semi-structured
 structured

Queries take5 minutes

2x NVIDIA Tesla GPUs

• Increased bid accuracy results in increased earnings and yield• 5 hours down to 5 minutes reduces time waiting for queries• Leveraging existing SQL knowledge, no Scala or MapReduce needed46

- Increased Appetite for Data

PubMatic expanded SQream DB for longer timeframes.
Hundreds of Hadoop Nodes can be Decomissioned!
Extract

Queries
can’t run

Extract

Load

GPU

Data Sources

Semi-structured
 structured

Queries take5 minutes

8x NVIDIA Tesla GPUs

• The original system handled 85TB. The enhanced system handles 4.3x more data - 360TB• 4x Dell PowerEdge servers and Qumulo filesystem-based deployment
47

Customer example 2• Fraud detection use case: Data enrichments and new statistics to discover new possible
deviations in purchases and possible frauds.
• Hadoop as the file system and Spark as the data processing unit.
• Basic everyday work is to add more statistics based on the existing data, in different
distribution/point of view：Statistics on all purchases based on timeStatistics on all purchases based productStatistics on all purchases based on location
Statistics on all purchases based on payment details

For complex statistics, each will requiredifferent data modeling and very
complex Spark operation.
48

Enhance Fraud Detection and Save Money
12TB Raw Data
Queries in SparkQueries in SQream

Cluster size

20 nodes Hadoop

1 node, V100 GPU

Generating statistics basedon changed windowfunctions
Data period: small

34 minutes

31 minutes

Generating statistics basedon changed windowfunctions
Data period: large

65 minutes

Never finished• Better utilization of time and resources.
• Run the queries that are impossible on Spark in order to achieve
unobtainable insights.

49

RETAILHEALTHCARE

Monitor Competitors
Customer Experience
Operational Decisions

Care Management
IOT Devices
Genomic Research

TELECOM

FINANCE

Customer 360
Competitive Analysis
Network Optimization

Fraud analysis
Risk consolidation
Customized services

50

WHAT OURCUSTOMERS SAY““

SQream and Orange demonstrate 100x cost
performance, removing limits of databases.”

SQream helps us keep pace with rapidly
increasing data for real customer benefits.”

Pascal Déchamboux | Director of Software

Suppachai Panichayunon, Head Solution Architect““

SQream is helping us to cut years of cancer
research on large genomic datasets.”

We saw a cost effective opportunity to obtain
analytic capabilities we couldn’t have before.“

Prof. Gideon Rechavi, Head of Cancer Research

RF Group Leader
51

The leading mobile operator in Thailand
40 million subscribers

Main Challenges• Couldn’t get a holistic view of network subscribers with ad-hoc queries• Dashboard took hours to show locations of key customers
Business Requirements• Enable to easily analyze and troubleshoot the network quality in multiple locations in just a few clicks.
• Ability to drill down for much deeper data analysis– Speed Test Data in different locations to manage the quality of the network.
– Decrease potential of competitor targeting

52

UNDERSTAND 40 MILLION CUSTOMERSTELECOM

360M

Ingest time

18M

120M

Reporting time

10M

$10,000,000

Ownership Cost

$200K

40 NODES
5 full racks
7600 CPU cores

HP DL380g9
with NVIDIA Tesla GPU
96 GB RAM + 6 TB storage
53

PROCESS TAKES A REALLY LONG TIME
30 minutes

3-5 hours

1-2 hours

ETL + Cubes +aggregation + index

Data sources

Data lake

Legacy MPP DB
1000 of CPUs

BI customers
54

Saving hours on Queries and reports

Replacing legacy MPP with a faster, easier to use SQream GPU-powered analytics database
1 HourData Sources

CDR 4G

40 nodesMPP

CDR 3G

ETL
Process

Aggregations

Direct Loading, 2.5TB/h ingest rate
Dozens of Reports

Non CDR

46 seconds with SQream DB
55

Architecture before Sqream DB
3G
4G
CDRs
OthersETL

1-2 hours

Green
plum

Billing
Profiles
…

Daily
aggr.

GP

Daily report

3 hours(max)

Pre-aggregations#1

Daily reports

Monthly reports(7 days)Monthl
y
#1

5hr

GP

•••

#2 #3 #4

Monthl
y
#2

3hr

•••

#31

Monthl
y
#N

0.5hr

56
56

Simplified with SQream DB
3G
4G
CDRs

Others#1

Daily reports

Monthly reports
1 day#2 #3 #4

Monthly#1Monthly
#2

10m

4m

•••

•••

#31

Monthly
#N

2m

57
57

Orange France Telecom

58
58

59
59

The cost of performance
Benchmark performed by the operator
Analyze subscriber network usage - 4.3 billion call records, 30 million subscribers

Teradata EDW

SQream DB

Full 42U rackwith 14 servers$

Single Dell R720
with NVIDIA Tesla card

875

Query time

302

14

Logical Servers

1

550

Total time

71

6,300,000

Cost of Ownership$

300,00060

INCREASE REVENUES
AD-TECH

85 TB/day in ad impressions for constructing bidding histograms
Data
Acquisition
Sources

Extract

Extract

Queries take5 hours

AVRO PARSER

Load

2x NVIDIA
Tesla GPUs

Queries take5 minutes
61

INCREASE REVENUES
AD-TECH360 TB/day ingested to enhance bid histogram accuracy
Data
Acquisition
Sources

X

Extract

Extract

Not feasible

AVRO PARSER

Load

8x NVIDIA
Tesla GPUs

Queries take5 minutes
62

Who is it for?
 BI Analysts


Need to analyze large amounts of data

Require fast response times to reports, dashboards

Easy connectivity with lots of different tools, including Python/Jupyter Data Scientists
Very straight-forward data exploration, with no need to pre-index

the raw data Any company
With lots of data and difficult scalability of current solutions

Need to build a high performance data pipeline without spending millions of $63

SQream Integration with End to End Pipelines• Connectivity – ODBC, JDBC and specific connectors e.g. Spark,
Tableau• Can also use “External table” to query data from source

64

FEEL FREE TO

CONTACT
Rafi Glickman,APAC Sales Director

rafig@sqream.com | www.sqream.com

ADDRESSWE ARE SOCIAL

Headquarters, 7 WTC
250 Greenwich Street
New York, New York65