a real-time version of the truth

Post on 11-Apr-2017

541 Views

Category:

Technology

0 Downloads

Preview:

Click to see full reader

TRANSCRIPT

Grab some coffee and enjoy the pre-show banter

before the top of the

hour! !

The Briefing Room

A Real-Time Version of the Truth

Welcome

Host: Eric Kavanagh

eric.kavanagh@bloorgroup.com @eric_kavanagh

u Reveal the essential characteristics of enterprise software, good and bad

u Provide a forum for detailed analysis of today’s innovative technologies

u Give vendors a chance to explain their product to savvy analysts

u Allow audience members to pose serious questions... and get answers!

Mission

Topics

September: DATA IN MOTION / STREAMING

October: DISCOVERY / VISUALIZATION

November: IoT

Go With the Flow

u Streaming Analytics Saves Time & Money

u A Real-Time Architecture Is Required

u Focus on Solving Old Riddles in New Ways

Analyst

Dez Blanchfield Data Scientist, The Bloor Group

Dez.Blanchfield@bloorgroup.com @dez_blanchfield

Striim

u Striim is a end-to-end streaming platform designed for data integration, operational intelligence and analytics

u The platform provides real-time correlation across multiple streams and enables data enrichment on streaming data

u Striim recently announced support for hybrid cloud environments and native Apache Kafka integration

Guest: Steve Wilkes

Steve Wilkes, Founder and Chief Technology Officer, Striim Steve Wilkes is a life-long technologist, architect, and hands-on development executive. Prior to founding Striim, Steve was the senior director of the Advanced Technology Group at GoldenGate Software. Here he focused on data integration, and continued this role following the acquisition by Oracle, where he also took the lead for Oracle’s cloud data integration strategy. His earlier career included Senior Enterprise Architect at The Middleware Company, principal technologist at AltoWeb and a number of product development and consulting roles including Cap Gemini’s Advanced Technology Group. Steve has handled every role in the software lifecycle and most roles in a technology company at some point during his career. He still codes in multiple languages, often at the same time.

AReal-TimeVersionoftheTruth

SteveWilkes–StriimFounder/CTO

The Data Landscape Is Changing

Human Machine Devices

On-Premise Cloud Hybrid

High Latency Low Latency

Reactive Proactive

What

Where

On-Disk In-Memory

When

How

Why 2000 2020

Analytics is Changing

database humans data

warehouse

logs machines

big data

Analytics is Changing

events devices

streaming

Analytics is Changing

database humans

events devices

logs machines

Analytics is Changing

streaming

Enterprise Infrastructure is Expanding •  Enterprise Home of Apps and Data for Years •  Cloud Promises Elastic Unlimited Scaling •  IoT Brings High Momentum Data

•  Intersections are Interesting

•  Enterprise <-> Cloud •  Intranet Of Things

–  Manufacturing –  Healthcare –  Retail

•  Consumer IoT •  IoT Processing Everywhere

Enterprise

IoT Cloud

Hybrid Cloud

Industrial IoT

Fog

IoT Cloud

And Requirements Are More Demanding

Integrate Correlate Analyze Predict Monitor

Enterprise

IoT Cloud

Hybrid Cloud

Industrial IoT

Fog

IoT Cloud

Streaming is the Foundation for All of This

Integrate Correlate Analyze Predict Monitor

In-Memory

Streaming Integration

& Analytics

Streaming In a Nutshell

streaming platforms provide low-latency in-memory integration, processing,

monitoring, and visualization of real-time data from all data sources

across enterprise, cloud, and IoT for proactive analytics

streaming platforms provide low-latency in-memory integration, processing,

monitoring, and visualization of real-time data from all data sources

across enterprise, cloud, and IoT for proactive analytics

streaming platforms provide low-latency in-memory integration, processing,

monitoring, and visualization of real-time data from all data sources

across enterprise, cloud, and IoT for proactive analytics

Multiple Common Use Cases

•  Within Enterprise From Logs and Databases •  Enterprise <-> Cloud including AWS, Azure, Google

Real-Time Data Movement

•  Security & Fraud Monitoring •  Replication Validation

Multi-Log / Multi-Source Correlation

•  Manufacturing Monitoring and Quality Control •  Location Services for Retail and Healthcare

IoT & Edge Processing

Hybrid

ReliablyProvideCurrent,AccurateandCompleteDecisionData

USECASEIREPLICATIONVALIDATION&

HEALTHMONITORING

Use Case I Themes

Integration Enterprise Change

Data Capture

Analytics

Visualization Correlation Monitoring

ButYouWanttoValidateItisWorkingCorrectlyandMonitorIt

SourceDB

TargetDB

You Use a Database Replication Solution

ReplicaConFlow

YouWantToSeeIfTransacConsMakeItFromSourcetoTarget

SourceDB

TargetDB

Missing Data Is A Liability

ReplicaConFlow

MissedTransacCons

OrAreSomehowMissedAndNeverMakeittotheTarget

YouAlsoWanttoMonitorandAlertontheReplicaConLag

SourceDB

TargetDB

Replication Lag is Also a Risk

ReplicaConFlow

!

30s

CDC CDC

YouUseChangeDataCapturetoReadAcCvityFromtheSourceandTarget

SourceDB

TargetDB

How It Works

ReplicaConFlow

Windowing�

Continuous Queries�

Correlation�

AndConCnuousQueriesOverWindowsToCorrelateTransacCons

CDC CDC

SourceDB

TargetDB

How It Works

ReplicaConFlow

Windowing�

Continuous Queries�

Correlation�

ByCorrelaCngTransacConsCommiMedonTheSourceandTheTarget

YouCalculatetheLagforMatchesAndAlertonMissingTransacCons

2s 5s 30s

CDC

CDC

YouCanMonitorAcCvityandGetAlertsForLagandMissedTransacCons

SourceDB

TargetDB

Monitoring and Alerts

ReplicaConFlow

Windowing�

Continuous Queries�

Correlation�

30s

Non-Intrusive Monitoring of Replication Health

Reduces Liability by Quickly Spotting Missed Transactions

Reduces Risk By Real-Time Monitoring of End-to-End Lag

Customer Benefits

USECASEIIUTILIZINGPARTNERSFOR

REAL-TIMELOCATIONSERVICES

Use Case II Themes

Integration Enterprise Cloud Analytics

IoT Visualization Correlation Monitoring

Real-Time Location For Multiple Industries

• Track WIP in real-time

• Track specialty tools

• Track key engineering support

Manufacturing

• Emergency room equipment (x-ray machines)

• Track patient location and wait time

• Track critical physician skill set

Healthcare

• Track store inventory

• Track employees • Reduce theft

Retail

• Track key parts • Access local parts

inventory • Access parts

maintenance history

• Track key equipment

Aviation

Health Care Specific Use Case

Goals

•  Monitor Patient Visits •  Prioritize Patients •  Monitor Wait Times by Priority •  Alert on Large Waits •  Take Immediate Action

•  End Result – Reduce Wait

The Setup •  Locators Are Place Around

The Waiting Room •  And Zones are Configured

for Different Areas •  When Patients Walk In •  They are Given a Tag to

Continuously Track Location •  And the Time Spent in

Each Zone is Recorded

+

+

2 Mins

How It Works

+

LocaConInformaConfromTags

Zones

AndGeometryofZones

Filtering�

Aggregation�

Transformation�

Windowing�

Continuous Queries�

AreCombinedUsingaSpaCalQuery

UDP

How It Works

+

PaMernMatchingisUsedtoSpotUsersBeingInAnyZoneforTooLong

Zones

Filtering�

Aggregation�

Transformation�

Windowing�

Continuous Queries�

WindowsandAggregateQueriesAreUsedToKeepTrackofWaitTime

How It Works

+

AndThisIsAllDisplayedonanInteracCveDashboard

Zones

Filtering�

Aggregation�

Transformation�

Windowing�

Continuous Queries�

The Whole Solution is a Fujitsu Partnership

Rapid Response & Process Flow Data & Decision & Action & Notice & Record

Location Badge & Tags

Real-Time Monitoring & Tracking

DataFromFujitsuDevicesAlertsTriggeringAutomated

FujitsuCloudServices

WithEverythingRunningOn

FujitsuM10Systems

Customer Benefits

+

Real-Time Location Monitoring

Alerts for Waiting too Long

Monitoring of Wait Times

Automated Workflows

WHATISSTRIIM?

Why Striim? We Handle the Hard Bits.

CONTINUOUS COLLECTION

SUPPORTING MULTIPLE SOURCES

ADDRESSING SCALE,

FAILURES

DISTRIBUTED GRID /

PERSISTENCE MANAGEMENT

SHARDING /SCALING

OVER LARGE STREAMING

DATA VOLUMES

DISTRIBUTED RESULTS CACHE

HIGH READ THROUGHPUT

INTEGRATION INTO MULTIPLE TARGETS AND

DATA FORMATS

REALTIME VISUALIZATION

REALTIME ALERTS

SIMPLE DECLARATIVE

INTERFACE TO DELIVER DATA

DRIVEN APPS � � � � �

Collect Process Deliver

App Developers focus on business logic

CONTINUOUS

� � � � �

Databases & Data Warehouses

Messaging

Big Data & NOSQL

Cloud

Files Databases

Log files

Sensors

Messaging

External Context�

Filtering� Enrichment�

Aggregation�

Transformation�

Windowing�

Continuous Queries�

STR

EAM

ING

INTE

GR

ATI

ON� Streaming

CDC

Parallel Log Collection

Edge Processing

Continuous Event

Collection

STR

EAM

ING

IN

TELL

IGEN

CE

Alerts

Results�Rea

l-tim

e

Das

hboa

rds�

Correlation �Detection� Matching�Triggers

Streaming Integration & Analytics

Design Flows Analyze Deploy

Visualize Monitor

One Consistent Easy-to-Use Distributed Platform

Want To Know More?

steve@striim.com

@BXCellent

www.striim.com

Perceptions

Analyst: Dez Blanchfield

LivingInTheStream

SoMuchChange-SoLi6leTime

Inlessthanalife,me,dataprocessinghasgonefromthepunchedcard,toreal-,meanaly,cs•  DecadestoYears•  YearstoMonths•  MonthstoDays•  DaystoHours•  HourstoSeconds•  NoweverythingisReal-;me

FirstPrincipals–ManagingData

EarlyBigDataArchitectures

TheCostOfDISKFellToNearZero

TheCostOfRAMFellToNearZero

CustomersAndDigitalDisrupIon

•  CelebrityCustomerExperience&withasideorderofSocial

•  TheFitBitgeneraIonofalways-on&alwaystracking

•  Real-Imeeverything-banking,bidding&recommendaIonengines

•  CyberSecurity

•  FraudDetecIon

•  EnItyExtracIon

•  GeospaIaldatabydefault

•  PermanentlyconnectedMobile

•  Thesheerscale&impactoftheIoT&M2M

CurrentBigDataArchitectures

FromBatchToReal-TimeStreams

MoreUsers,Devices&Data

TheIoTNeverStopsStreaming

Upcoming Topics

www.insideanalysis.com

September: DATA IN MOTION / STREAMING

October: DISCOVERY / VISUALIZATION

November: IoT

THANK YOU for your

ATTENTION!

Some images provided courtesy of Wikimedia Commons

top related