STOP moving data aroundEdge Analytics made easy

Analytics built for IoT

Imagine a world…

Where IoT analytics delivers 15% more output

from renewable energy sources

30TBAnalyze Data

in Real-time

15%Increase

Efficiency

$18K/hr; $158M/yrGenerate Operational/

Economic Benefits

(20,000 Wind Turbines; 10 GW Capacity; .3 Capacity Factor; $40/MW-hour)

2

IoT analytics has a set of distinct requirements.

3

Big DataData is growing faster and bigger

because of number of sensors

10B+ rows

5TB+

Fast DataData streamed from sensors

requires fast ingestion

1M+ rows

per sec

Edge AnalyticsIoT data is mostly generated

at the ‘Edges’ of the network

100+

Locations

Real-Time InsightsUse cases require near

Real Time Analytics

<1 sec query

response

time

Problem

Horizontal Integration, Vertical ApplicationsDelivering actionable insights requires use-case specific

applications that are adapted to changing needs rappidly

4

Network

Analytics

Query

4 billion

records

4 billion

records

Today - Purpose built Solutions stacks

Mobile

Analytics

Query

Network

Analytics

Query

Future – Flexible Application Architecture

Mobile

Analytics

User-Experience

Management?

Data Collection

Storage &

Analytics

Application

ParStream is integrated with leading IoT solutions.

5

Custom Apps DATAWATCH

An

aly

tics

Vis

ua

liza

tion

Data

Co

llec

tion

Solution

Standard SQL

Streaming import (MQTT, flume Avro...)

UDX for

analytical

libraries

Geo-Distributed setup substantially reduces network traffic enabling continuous monitoring (sampling proofed insufficient)

DecentralizationOvercoming bandwidth limitations and keeping data transfer

costs low required analytics to be pushed closer to the data

source (EDGE, FOG..)

6

Application

Database

More than

20 billion records

returned

Query Search Results

40 records found

4 billion

records

4 billion

records4 billion

records4 billion

records

4 billion

records

Today

Application

Query Search Results

40 records found

ParStream

ParStream Geo-Distributed Server

7

records

18

records

5

records12

records

8

records

4 billion

recordsParStream ParStream ParStream ParStream

Tomorrow with ParStream Geo-Distributed Analytics

GDA architecture vs MPP vs Federated servers

7

Analytical

Cluster (MPP)

Geo Distributed

Deployment

Federation

Servers

Architecture

High

Availability

Data

Model

Query

Execution

Problem

Tightly

coupled

nodes

Losely

coupled

nodes

Independent

sub-systems

‚System‘ redundancy

Full or no results

‚Node‘ redundancy

Full or partial results

‚Sub-system‘

redundancy

Full or no results

One modelOne model with

local extensions

Joint view on

local models

Data-structure-aware

system optimization

Data-structure-aware

execution plan with

data-aware local optimization

Data-structure-aware

execution-plan-translation

with independent execution

ParStream introduces EdgeAnalyticsBoxThe industry’s first appliance built for edge analytics/GDA

• Specifically designed to enable edge analytics (Geo-Distributed Analytics).

• Ruggedized for use in real-world edge analytics applications such as oil/drilling

sites, cell phone towers, wind farms, etc.

• Pre-loaded and tested with ParStream software.

• Technical Specs: Intel Core i5/i7 processor, 8-16 GB RAM and 64-128GB SSD

• EdgeAnalyticsBox provides customers with the convenience of a one-stop shop

for the their edge analytics needs, however, customers can run GDA on any

standard hardware with certain processing and storage requirements.

8

Solution

New Product of the Week

Geo-Distributed setup substantially reduces network traffic enabling continuous monitoring (sampling proofed insufficient)

Integration of analytics into OT processesDemystification and integration of advanced analytics into

regular business processes connecting operations and analytics

9

Traditional OT setup Future OT + Data Scientist setup

Monitoring

&

Control

Compliance

&

Archiving

HistorianCEP

Data Stream

Compliance

&

Archiving

HistorianCEP

Data Stream

Analytics

&

Optimization

ParStream

Monitoring

&

Control

ParStream is the only solution for all IoT analytics

requirements.

10

Customer Applications and Visualization Tools

IoT Data Collection Platforms Enterprise Data Sources

ParStream DB

Geo-

Distributed

Analytics

Alarm +

Action

Time

Series

Advanced

Analytics

Solution

Demo: Sensor Analytics for Real-time Environmental

Compliance

11

Solution

Backup

Global IoT Survey

- Global, cross industry survey

- 50/50 mix of business and

technology leaders

- Over 200 participants

- Focus on the use and value of

data in IoT initiatives

Better IoT data collection and analysis delivers more value

• 70% say they make better, more meaningful decisions with improved data

• 86% would increase the ROI of their IoT investment

IoT not delivering full potential because of data challenges

• 86% of business stakeholders say data is important to their IoT project

• Only 8% are fully capturing and analyzing IoT data in a timely fashion

• 94% face challenges collecting and analyzing IoT data

IoT projects vary widely – but all have challenges

• 53% are using IoT projects to optimize existing businesses

47% as a strategic business investment

• 96% have faced challenges with their IoT projects

Global IoT Survey: Key Findings

Analytical Frameworks

15

Query/Scripting

Language SQL Hive PigMongo

MR

Data Store

Execution

Engine

Programming

Interface/Model MapReduce

Hadoop

MapReduce

MongoDB

MapReduce

MongoDB

Column

Store

HBASE

Row / Document

Store

Giraph

......

......

......

......

MPP Exec.

Engine

Declarative

API

Mongo

CRUD

Storage

Access

Key-ValueFile

HDFS

Spark

Spark

SQLScala

FocusReal-Time

Analytics

Peta-Scale

Batch Proc.High-Throughput

Key Value Doc. Store

IoT analytics has a set of distinct requirements.

16

Big DataData is growing faster and bigger

because of number of sensors

10B+ rows

5TB+

Wind turbine: 100 turbines x 100M rows per year

Race car: 400M records / day x 365 days test drive

Telco: 1.000 cells x 1.000 rows / sec x 1 days - wow

Traffic analysis: 60M cars x 1 read / min x 365 days

Oil rig: 1 rig = 8 billion records / day (not verified)

Fast DataData streamed from sensors

requires fast ingestion

1M+ rows

per sec

Network monitoring: 1M rows per sec per cell

Asset monitoring: 60M cars x 1 reading per minute

Airplane monitoring: 4 turbines x 3k sensors x 100Hz

Oil exploration: 10.000 wells x 100 sensors x 1Hz

Oil rig: 1 drilling rig x 10.000 sensors x avg 100Hz

Edge AnalyticsIoT data is mostly generated

at the ‘Edges’ of the network

100+

Locations

Manufacturing: 300.000 plants in US (2012)

Cars / ships / airplanes: >1 billion world wide

Telco: 190.000 cell towers in US (2013)

Oil: 950.000 wells worldwide; 500.000 in US

Mobile advertising: de-central adserving / monitoring

Real-Time InsightsUse cases require near

Real Time Analytics

<1 sec query

response

time

Dashboarding: real-time visualization, many queries

Network monitoring: root cause analysis, optimization

Asset monitoring: conditional monitoring, safety

Security: anomalie detection, building safety

Traffic: location aware recommendations

Problem

Customer Proof Point for IoT in Renewable Energy:

Real-time Analytics for Wind Turbines

17

Business Challenge

Optimize wind turbine performance by quickly adjusting to changing

environmental factors (e.g., wind direction, temperature, etc.)

Minimize turbine downtime thru predictive maintenance.

Use Case

Real-time and continuous monitoring of data from 20,000 wind turbines,

including analysis of over 20TB of historical data

ParStream‘s Technology Value Proposition

Real-time monitoring of continuous data-flow for immediate

insights/actions

Historical analysis thru enabling storage and analytics in an integrated

platform by immediately importing and storing readings from turbines.

Benefits/Results (estimated)

15% improvement in productivity

Decreased downtime

$158M of annual economic benefits

Traction

Customer Proof Point for IoT in Manufacturing:

Real-time Analytics for Gas Turbine Monitoring

18

Business Challenge

Optimization of complex systems for efficiency and operational

(automated) decisions in real-time

Enabling new service-driven business models

Use Case

5,000 data points per sec/turbine for real-time analytics and

historical storage for model-based learning/root cause analysis.

ParStream‘s Technology Value Proposition

Real-time monitoring of continuous data-flow for immediate

insights/actions

“Analytical Historian” enabling storage and analytics in an

integrated platform by immediately importing and storing all

sensor readings

Benefits/Results

Improved startup with less vibration equals less deterioration

Less NOx emission

Improved overall efficiency

Prediction of material fatigue

Traction

Customer Proof Point for IoT in Manufacturing:

Real-time Analytics for Semiconductor Testing

19

Business Challenge

Current MySQL environment requires pre-built aggregations. The ability to perform root cause analysis is limited.

Computing aggregations takes too long reducing machine utilization and causing more scrap product.

Use Case

One Automated Testing Equipment handles 24 wafers per lot, 1 wafer generates 1 Billions test results. Data volume required pre-built aggregations which took too long to build

ParStream‘s Technology Value Proposition

Real-Time monitoring of continuous data-flow for immediate insight /action to reduce waste and increase outputs

Unlimited scalability allows Galaxy to market to bigger semiconductor testing and manufacturing companies

Benefits/Results

Improved Machine Utilization: Current batch style analysis of test data causes expensive test machines to be underutilized

Revenue Increase: Increased data volume opens new, more lucrative markets, ability to sell to larger customers

New Products: Drill down analysis to detail test results leads to new insights

Cost Savings: Ability to analyze detail level data expected to produce new insights in causes of test failures

Traction

Customer Proof Point for IoT in Automotive/Telematics:

Real-time Analytics for Sensor Data/Vehicle Monitoring

20

Business Challenge

Optimization of multiple systems for efficiency and operational

(automated) decisions on billions of records

Enabling new service-driven business models

Use Case

Real-time monitoring of continuous GPS data and events flows

ParStream‘s Technology Value Proposition

Over 260 million new records/month for real-time analytics

31 billion records of historical data

ParStream collects all data from different systems near-real-time

Benefits/Results

Reduced overall data manipulation time by over 90%

Reduced annual hardware by over 60%

Improved execution time and scheduling efforts

Improved analysis/prediction of driver profiles

Traction

ParStream is uniquely positioned for Real-time

Analytics in IoT

21

REAL-TIME

IMPORT

REAL-TIME

QUERYING

FLEXIBLE

ANALYTICS

Small Form Factor / Low TCO

Bil

lio

ns

of

Re

co

rds

Thousands of Columns

Solution

ParStream’s patented technology provides a

competitive advantage.

1

2

3 Lockless architectureEnables ultra-fast query and

data import performance

Massive parallel processingDelivers linear scalability and

high query throughput

4 Small footprint Enables analytics at the edge

with a low TCO

High Performance

Compressed IndexesProvide ultra-high query

performance

SQL API / JDBC /ODBC C++ UDx API

Real-Time Analytics Engine

In-Memory and

Disk Technology

Multi-Dimensional

Partitioning

Massively Parallel

Processing (MPP)

Shared Nothing

Architecture

3rd generation Columnar Storage

High Speed Parallel Loader with Low Latency

High

Performance

Compressed

Index (HPCI)

22

Solution

Industry-leading Product Recognition

23

ParStream is the most

reliable System in our

Data Center

CTO, etracker

ParStream was 40 times

faster than its nearest

competitor.

CTO, Cake Marketing

ParStream enabled us

to scale internationally -

TCO is much lower than

with Hadoop

VP Eng, Searchmetrics

#1

Big Data

Startup

Cisco Entrepreneurs in Residence

Traction

2014 IoT Excellence Award

GDA architecture vs MPP vs Federated servers

24

Analytical Cluster

(MPP)

Geo Distributed

Deployment

Federation

Servers

Hardware − Commodity or high-end DB

Server

− “low latency, high

bandwidth”

− Colocated Nodes

−

− GDA master is loosely coupled to slave

− systemNodes are individual ParStream

deployments

− Nodes can have very different sizing

− coupled to component systems

− Geologically spread

High

Availability

− Redundancy per structure

− Full/no results

− decoupled from the Component Systems

− Best of bread per backend system

− GDA master expects nodes to be not

available at query runtime

− Nodes with normal HA characteristics

− decoupled from the Component

Systems

− Best of breed per Backend

System

Data

Model

− Global Model − Master and Nodes share a common data-

model

− Independent local Schema extensions

− Distribution models supported very

similar to cluster (COLOCATION,

EVERYWHERE replicated dimension

tables)

− joint View on component data

model

Execution − MPP

− Data Structure aware

− Computation next to data

− Data-aware engine optimizations during

query executions

− Node-local, partition centric workload

− query results can be fully or partially

computed remotely

− Queries against the virtual data

model

− Translation for different

components

− No Coupling and Optimization as

in a native cluster possible

Problem