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Difference Between #BigData and Internet of Things | @ThingsExpo #IoT #M2M
What does it mean, as a vendor, to say that you support the Internet of Things (IoT) from an analytics perspective?

A recent argument with folks whose intelligence I hold in high regard (like Tom, Brandon, Wei, Anil, etc.) got me thinking about the following question:

What does it mean, as a vendor, to say that you support the Internet of Things (IoT) from an analytics perspective?

I think the heart of that question really boils down to this:

What are the differences between big data (which is analyzing large amounts of mostly human-generated data to support longer-duration use cases such as predictive maintenance, capacity planning, customer 360 and revenue protection) and IoT (which is aggregating and compressing massive amounts of low latency / low duration / high volume machine-generated data coming from a wide variety of sensors to support real-time use cases such as operational optimization, real-time ad bidding, fraud detection, and security breach detection)?

I don’t believe that loading sensor data into a data lake and performing data science to create predictive analytic models qualifies as doing IoT analytics.  To me, that’s just big data (and potentially REALLY BIG DATA with all that sensor data).  In order for one to claim that they can deliver IoT analytic solutions requires big data (with data science and a data lake), but IoT analytics must also include:

  1. Streaming data management with the ability to ingest, aggregate (e.g., mean, median, mode) and compress real-time data coming off a wide variety of sensor devices “at the edge” of the network, and
  2. Edge analytics that automatically analyzes real-time sensor data and renders real-time decisions (actions) at the edge of the network that optimizes operational performance (blade angle or yaw) or flags unusual performance or behaviors for immediate investigation (security breaches, fraud detection).

If you cannot manage real-time streaming data and make real-time analytics and real-time decisions at the edge, then you are not doing IOT or IOT analytics, in my humble opinion.  So what is required to support these IoT data management and analytic requirements?

The IoT “Analytics” Challenge
The Internet of Things (or Industrial Internet) operates at machine-scale, by dealing with machine-to-machine generated data.  This machine-generated data creates discrete observations (e.g., temperature, vibration, pressure, humidity) at very high signal rates (1,000s of messages/sec).  Add to this the complexity that the sensor data values rarely change (e.g., temperature operates within an acceptably small range).  However, when the values do change the ramifications, the changes will likely be important.

Consequently to support real-time edge analytics, we need to provide detailed data that can flag observations of concern, but then doesn’t overwhelm the ability to get meaningful data back to the core (data lake) for more broad-based, strategic analysis.

One way that we see organizations addressing the IoT analytics needs is via a 3-tier Analytics Architecture (see Figure 1).

Figure 1: IoT Analytics 3-Tier Architecture

We will use a wind turbine farm to help illustrate the 3-tier analytics architecture capabilities.

Tier 1 performs individual wind turbine real-time performance analysis and optimization.  Tier 1 must manage (ingest and compress) real-time data streams coming off of multiple, heterogeneous sensors. Tier 1 analyzes the data, and processes the incoming data against static or dynamically updated analytic models (e.g., rules-based, decision trees) for immediate or near-immediate actions.

Purpose-built T1 edge gateways leverage real-time data compression techniques (e.g., see the article “timeseries storage and data compression” for more information on timeseries databases) to only send a subset of the critical data (e.g., data that has changed) back to T2 and T3 (core).

Let’s say that you are monitoring the temperatures of a compressor inside of a large industrial engine.  Let’s say the average temperature of that compressor is 99 degrees, and only varies between 98 to 100 degrees within a 99% confidence level.  Let’s also say the compressor is emitting the following temperature readings 10 times a second:

99, 99, 99, 98, 98, 99, 99, 98, 99, 99, 100, 99, 99, 99, 100, 99, 98, 99, 99…

You have 10,000 of readings that don’t vary from that range.  So why send all of the readings (which from a transmission bandwidth perspective could be significant)?  Instead, use a timeseries database to only send mean, medium, mode, variances, standard deviation and other statistical variables of the 10,000 readings instead of the individual 10,000 readings.

However, let’s say that all of a sudden we start getting readings outside the normal 99% confidence level:

99, 99, 99, 100, 100, 101, 101, 102, 102, 103, 104, 104, 105, …

Then we’d apply basic Change Data Capture (CDC) techniques to capture and transmit the subset of critical data to T2 and T3 (core).

Consequently, edge gateways leverage timeseries compression techniques to drive faster automated decisions while only sending a subset of critical data to the core for further analysis and action.

The Tier 1 analytics are likely being done via an on-premise analytics server or gateway (see Figure 2).

Figure 2:  IoT Tier 1 Analytics

Tier 2 optimizes performance and predicts maintenance needs across the wind turbines in the same wind farm.  Tier 2 requires a distributed dynamic content processing rule generation and execution analytics engine that integrates and analyzes data aggregated across the potentially heterogeneous wind turbines. Cohort analysis is typical in order to identify, validate and codify performance problems and opportunities across the cohort wind turbines.  For example, in the wind farm, the Tier 2 analytics are responsible for real-time learning that can generate the optimal torque and position controls for the individual wind turbines. Tier 2 identifies and shares best practices across the wind turbines in the wind farm without having to be dependent upon the Tier 3 core analytics platform (see Figure 3).

Figure 3: Tier 2 Analytics: Optimizing Cohort Performance

Tier 3 is the data lake enabled core analytics platform. The tier 3 core analytics platform includes analytics engines, data sets and data management services (e.g., governance, metadata management, security, authentication) that enable access to the data (sensor data plus other internal and external data sources) and existing analytic models that supports data science analytic/predictive model development and refinement.  Tier 3 aggregates the critical data across all wind farms and individual turbines, and combines the sensor data with external data sources which could include weather (humidity, temperatures, precipitation, air particles, etc.), electricity prices, wind turbine maintenance history, quality scores for the wind turbine manufacturers, and performance profiles of the wind turbine mechanics and technicians (see Figure 4).

Figure 4:  Core Analytics for Analytic Model Development and Refinement

With the rapid increase in storage and processing power at the edges of the Internet of Things (for example, the Dell Edge Gateway 3000 Series), we will see more and more analytic capabilities being pushed to the edge.

How Do You Start Your IoT Journey
While the rapidly evolving expertise on the IoT edge technologies can be very exciting (graphical processing units in gateway servers with embedded machine learning capabilities with 100’s of gigabytes of storage), the starting point for the IoT journey must first address this basic question:

How effective is your organization at leveraging data and analytics to power your business (or operational) models?

We have tweaked the Big Data Business Model Maturity Index to help organizations not only understand where they sit on the maturity index with respect to the above question, but also to provide a roadmap for how organizations can advance up the maturity index to become more effective at leveraging the wealth of IOT data with advanced analytics to power their business and operational models (see Figure 5).

Figure 5:  Big Data / IoT Business Model Maturity IndexMaturity Index

To drive meaningful business impact, you will need to begin with the business and not the technology:

  • Engage the business stakeholders on day one,
  • Align the business and IT teams
  • Understand the organization’s key business and operational initiatives, and
  • Identify and prioritize the use cases (decisions/goals) that support those business initiatives.

If you want to monetize your IOT initiatives, follow those simple guidelines and you will dramatically increase the probability of your business and monetization success.

For more details on the Internet of Things revolution, check out these blogs:

The post Difference between Big Data and Internet of Things appeared first on InFocus Blog | Dell EMC Services.

About William Schmarzo
Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business”, is responsible for setting the strategy and defining the Big Data service line offerings and capabilities for the EMC Global Services organization. As part of Bill’s CTO charter, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, avid blogger and is a frequent speaker on the use of Big Data and advanced analytics to power organization’s key business initiatives. He also teaches the “Big Data MBA” at the University of San Francisco School of Management.

Bill has nearly three decades of experience in data warehousing, BI and analytics. Bill authored EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the Vice President of Advertiser Analytics at Yahoo and the Vice President of Analytic Applications at Business Objects.

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WITECK
Citrix

LEUFFEN
Wrecker

ROGERS
Anexia

NEW
HOUSE

Agilitiv

DE MENO
Commvault

VAN TUIN
Red Hat

LIANG
Rancher Labs

BUTLER
Tego

MURTHY
Cloud Raxak

REMIL-
LARD

Microsoft

MANN
Splunk

ROWE
IBM Cloud

SKILLERN
Intel

ALLEN
Progress

BENEDICT
Cognizant

SMITH
Numerex
Cloud Expo New York All-Star Speakers Include

DE MENO
Commvault

ERWAY
Appneta

KEYS
ConsenSys

HINCH-
CLIFFE

7Summits

DOYLE
eCube

CHAVES
Pythian

BLOOMBERG
Intellyx

BUGAYENKO
Teamed.io

LAWSON
NewSci

HAFF
RedHat

HOLT
IBM

Jewell
Codenvy

MORGENTHAL
CSC

ARMSTRONG
AppNeta

DWYER
Iron.io

MATSUMURA
Gradle

WARFIELD
Coho

CLELAND
HGST

VASILIOU
Catchpoint

WALLGREN
Electric Cloud

DE SOUZA
Cisco

RANDALL
Gartner

ARMSTRONG
AppNeta

SMALLTREE
Cazena

MCCARTHY
Bsquare

DELOACH
Infobright

QUINT
Ontegrity

MCLAUCHLAN
Buddy Platform

MCCALLUM
Falconstor

GRECO
Kaazing

PRESLEY
Pythian

LOVELL-TROY
Pythian

PALIOTTA
Vector

ATCHISON
New Relic

BHUSVANE-
SHWARI

Microsoft

MITRA
Cognizant

KOCHER
Grey Heron

GUCCIONE
Keeper

PAPDO
POULOS

Cloud9

FLOREA
Tintri

HARLAN
Two Bulls

GOLO
SHUBIN

Bit6

KERBY
BMC

PROIETTI
Location
Smart

MARTIN
nfrastructure

RAO
Asurion

BRENTON
CollabNet

MOULINE
Everbridge

MARSH
Blue Pillar

SRINIVASAN
Symantec

GALBRAITH
HPE

NEWTON
Alfresco

PARKS
SecureRF

PEROTTI
Plantronics

ROGERS
Anexia

HOFFMAN
EastBanc

MORRISH
Interoute

TIFFANY
SoftLayer

OSTROVER-
KHYI

Mobidev

LEWIS
Formation
Data

KENDRICK
Isomorphic

WATSON
Trendalyze

BENSONOFF
Unigma

REEVES
Datical

WALLER-
STORFER

Dynatrace

CIOT
Progress

SHAN
CTS

MATTELA
Redpine

MOR
Cloudyn

GILLEN
Spark
Coginition

SOLT
Netvibes

BERNARDO
GE Digital

ROMANSKY
TrustPoint

LEFORT
BMC

ANDERSON
BMC

BRODY
Webair

NIELSEN
Redis Labs

BEAMER
GoTransverse

LESTER
LogMeIn

PONO
-MAREVA

Google

SINGH
Sencha

CALKINS
Amadeus

SEHGAL
Tintri

KLEIN
Rachio

MANN
Splunk

HOASIN
Aeris

SARKARIA
PHEMI

SPROULE
Metavine

YOO
Fuze

SNELL
Intel

COTY
Alert Logic

LANDA
Kintone

CHOQUETTE
RackN

MARTIN
Security

LEVINE
CytexOne

ALLEN
Freewave

HURWITZ
Hurwitz & Assc.

HYEDT
Seamless

SWANN
Adobe

NGUYEN
Adobe

OXENHORN
FalconStor

RAJAGO-
PALAN

ACCELERITE

DESH-
PANDE

ACCELERITE

MURTHY
Cloud Raxak

HARRIS
Cloud Raxak

COLE
Peak 10

HUYNH
VAN

Glue Networks

SOBHANI
Constellation

BERMING
HAM

SIOS

STEELE
Hanu

AKELA
Appdynamics

DOWNEY
BMC

ERRAM
OUSPE

Spanning

SAADI
Govplace

JACOBS
Singlehop

KEE
GlobalSign

Cloud Expo Silicon Valley All-Star Speakers Include

KOWALL
AppDynamics

VAN TUIN
Red Hat

DEMMER
Jut

COHEN
Netflix

MUCHANDI
Red Hat Inc

BORELLO
Sysdig

GILPIN
Conjur

KANADE
Harbinger
Systems

GORBACHEV
Systems
Services Inc.

SUSSMAN
Coalfire

KHAN
Solgeniakhela

CHOKSI
Harbinger
Systems

DE MENO
CommVault

BLOOMBERG
Intellyx

BUGWADIA
Nirmata

COTY
Alert Logic

FLETCHER
Alert Logic

CAUTHRON
NIMBOXX

LYNN
AgilData

WAGNER
Cloudyn

ANAND
Appocito

WEISS
Pythian

BRODY
Webair

JACKSON
Softlayer

DAVIDSON
Juniper

HOFFMAN
Pivotal

VERVAET
HGST

Murthy
CloudRaxak

FRANCISCO
Evolute

LETCHIN
Nexenta

SIMON
JFrog

BONIFAZI
Solgenia

WEISS
ProfitBricks

HOLT
IBM

HANNON
SoftLayer

GALLO
SoftLayer

SAYEGH
Codero

BIMMU
Clouber

SRINIVAS
IBM

CHAVAN
IBM

HEDGES
Clouddata

AHUJA
Cloud

MEINER
Oracle

SWARTZ
Ericsson
Cloud Expo New York All-Star Speakers Included

DE SOUZA
Cisco

POTTER
SafeLogic

ROBINSON
CompTIA

WARUSA
-WITHANA

WSO2 Inc

MEINER
Oracle

CHOU
Microsoft

HARRISON
Tufin

BRUNOZZI
VMware

KIM
MapR

KANE
Dyn

SICULAR
Basho

TURNER
Cloudian

KUMAR
Liaison

ADAMIAK
Liaison

KHAN
Solgenia

BONIFAZI
Solgenia

SUSSMAN
Coalfire

ISAACSON
RMS

LYNN
CodeFutures

HEABERLIN
Windstream

RAMA
MURTHY

Virtusa

BOSTOCK
IndependenceIT

DE MENO
CommVault

GRILLI
Adobe

WILLIAMS
Rancher Labs

CRISWELL
Alert Logic

COTY
Alert Logic

JACOBS
SingleHop

MARAVEI
Cisco

JACKSON
Softlayer

SINGH
IBM

HAZARD
Softlayer

GALLO
Softlayer

TAMASKAR
GENBAND

SUBRA
-MANIAN

Emcien

LEVESQUE
Windstream

IVANOV
StorPool

BLOOMBERG
EnterpriseWeb

BUDHANI
Soha

HATHAWAY
IBM Watson

TOLL
ProfitBricks

LANDRY
Microsoft

BEARFIELD
Blue Box

HERITAGE
Akana

PILUSO
SIASMSP

HOLT
IBM Cloudant

SHAN
CTS

PICCININNI
EMC

BRON-
GERSMA

Modulus

PAIGE
CenturyLink

SABHIKHI
Cognitive Scale

MILLS
Green House Data

KATZEN
CenturyLink

SLOPER
CenturyLink

SRINIVAS
EMC

TALREJA
Cisco

GORBACHEV
Systems Services Inc.

COLLISON
Apcera

PRABHU
OpenCrowd

LYNN
CodeFutures

SWARTZ
Ericsson

MOSHENKO
CoreOS

BERMINGHAM
SIOS

WILLIS
Stateless Networks

MURPHY
Gridstore

KHABE
Vicom

NIKOLOV
GetClouder

DIETZE
Windstream

DALRYMPLE
EnterpriseDB

MAZZUCCO
TierPoint

RIVERA
WHOA.com

HERITAGE
Akana

SEYMOUR
6fusion

GIANNETTO
Author

CARTER
IBM

ROGERS
Virtustream
Cloud Expo Silicon Valley All-Star Speakers

TESAR
Microsoft

MICKOS
HP

BHARGAVA
Intel

RILEY
Riverbed

DEVINE
IBM

ISAACSON
CodeFutures

LYNN
HP

HINKLE
Citrix

KHAN
Solgenia

SINGH
Bigdata

BEACH
SendGrid

BOSTOCK
IndependenceIT

DE SOUZA
Cisco

PATTATHIL
Harbinger

O'BRIEN
Aria Systems

BONIFAZI
Solgenia

BIANCO
Solgenia

PROCTOR
NuoDB

DUGGAL
EnterpriseWeb

TEGETHOFF
Appcore

BRUNOZZI
VMware

HICKENS
Parasoft

KLEBANOV
Cisco

PETERS
Esri

GOLDBERG
Vormetric

CUMBER-
LAND

Dimension

ROSENDAHL
Quantum

LOOMIS
Cloudant

BRUNO
StackIQ

HANNON
SoftLayer

JACKSON
SoftLayer

HOCH
Virtustream

KAPADIA
Seagate

PAQUIN
OnLive

TSAI
Innodisk

BARRALL
Connected Data

SHIAH
AgilePoint

SEGIL
Verizon

PODURI
Citrix

COWIE
Dyn

RITTEN-
HOUSE

Cisco

FALLOWS
Kaazing

THYKATTIL
TimeWarner

LEIDUCK
SAP

LYNN
HP

WAGSTAFF
BSQUARE

POLLACK
AOL

KAMARAJU
Vormetric

BARRY
Catbird

MENDEN-
HALL

SUPERNAP

SHAN
KEANE

PLESE
Verizon

BARNUM
Voxox

TURNER
Cloudian

CALDERON
Advanced Systems

AGARWAL
SOA Software

LEE
Quantum

OBEROI
Concurrent, Inc.

HATEM
Verizon

GALEY
Autodesk

CAUTHRON
NIMBOXX

BARSOUM
IBM

GORDON
1Plug

LEWIS
Verizon

YEO
OrionVM

NAKAGAWA
Transparent Cloud Computing

SHIBATA
Ubiquitous Corp

NATH
GE

GOKCEN
GE

STOICA
Databricks

TANKEL
Pivotal Software

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Testimonials
This week I had the pleasure of delivering the opening keynote at Cloud Expo New York. It was amazing to be back in the great city of New York with thousands of cloud enthusiasts eager to learn about the next step on their journey to embracing a cloud-first worldl."
@SteveMar_Msft
General Manager of Window Azure
 
How does Cloud Expo do it every year? Another INCREDIBLE show - our heads are spinning - so fun and informative."
@SOASoftwareInc
 
Thank you @ThingsExpo for such a great event. All of the people we met over the past three days makes us confident IoT has a bright future."
Yasser Khan
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One of the best conferences we have attended in a while. Great job, Cloud Expo team! Keep it going."

@Peak_Ten


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@CloudExpo Blogs
When it comes to cloud computing, the ability to turn massive amounts of compute cores on and off on demand sounds attractive to IT staff, who need to manage peaks and valleys in user activity. With cloud bursting, the majority of the data can stay on premises while tapping into compute from public cloud providers, reducing risk and minimizing need to move large files. In his session at 18th Cloud Expo, Scott Jeschonek, Director of Product Management at Avere Systems, discussed the IT and business benefits that cloud bursting provides, including increased compute capacity, lower IT investment,...
Nordstrom is transforming the way that they do business and the cloud is the key to enabling speed and hyper personalized customer experiences. In his session at 21st Cloud Expo, Ken Schow, VP of Engineering at Nordstrom, will discuss some of the key learnings and common pitfalls of large enterprises moving to the cloud. This includes strategies around choosing a cloud provider(s), architecture, and lessons learned. In addition, he’ll go over some of the best practices for structured team migration and discuss ways to control cloud costs.
Companies are harnessing data in ways we once associated with science fiction. Analysts have access to a plethora of visualization and reporting tools, but considering the vast amount of data businesses collect and limitations of CPUs, end users are forced to design their structures and systems with limitations. Until now. As the cloud toolkit to analyze data has evolved, GPUs have stepped in to massively parallel SQL, visualization and machine learning.
Join IBM November 1 at 21st Cloud Expo at the Santa Clara Convention Center in Santa Clara, CA, and learn how IBM Watson can bring cognitive services and AI to intelligent, unmanned systems. Cognitive analysis impacts today’s systems with unparalleled ability that were previously available only to manned, back-end operations. Thanks to cloud processing, IBM Watson can bring cognitive services and AI to intelligent, unmanned systems. Imagine a robot vacuum that becomes your personal assistant that knows everything and can respond to your emotions and verbal commands!
Organizations do not need a Big Data strategy; they need a business strategy that incorporates Big Data. Most organizations lack a road map for using Big Data to optimize key business processes, deliver a differentiated customer experience, or uncover new business opportunities. They do not understand what’s possible with respect to integrating Big Data into the business model.
Coca-Cola’s Google powered digital signage system lays the groundwork for a more valuable connection between Coke and its customers. Digital signs pair software with high-resolution displays so that a message can be changed instantly based on what the operator wants to communicate or sell. In their Day 3 Keynote at 21st Cloud Expo, Greg Chambers, Global Group Director, Digital Innovation, Coca-Cola, and Vidya Nagarajan, a Senior Product Manager at Google, will discuss how from store operations and optimization to employee training and insights, all ultimately create the best customer experie...
We all know that end users experience the Internet primarily with mobile devices. From an app development perspective, we know that successfully responding to the needs of mobile customers depends on rapid DevOps – failing fast, in short, until the right solution evolves in your customers' relationship to your business. Whether you’re decomposing an SOA monolith, or developing a new application cloud natively, it’s not a question of using microservices – not doing so will be a path to eventual business failure.
The next XaaS is CICDaaS. Why? Because CICD saves developers a huge amount of time. CD is an especially great option for projects that require multiple and frequent contributions to be integrated. But… securing CICD best practices is an emerging, essential, yet little understood practice for DevOps teams and their Cloud Service Providers. The only way to get CICD to work in a highly secure environment takes collaboration, patience and persistence. Building CICD in the cloud requires rigorous architectural and coordination work to minimize the volatility of the cloud environment and leverage t...
With major technology companies and startups seriously embracing Cloud strategies, now is the perfect time to attend 21st Cloud Expo October 31 - November 2, 2017, at the Santa Clara Convention Center, CA, and June 12-14, 2018, at the Javits Center in New York City, NY, and learn what is going on, contribute to the discussions, and ensure that your enterprise is on the right path to Digital Transformation.
Infoblox delivers Actionable Network Intelligence to enterprise, government, and service provider customers around the world. They are the industry leader in DNS, DHCP, and IP address management, the category known as DDI. We empower thousands of organizations to control and secure their networks from the core-enabling them to increase efficiency and visibility, improve customer service, and meet compliance requirements.
Digital transformation is changing the face of business. The IDC predicts that enterprises will commit to a massive new scale of digital transformation, to stake out leadership positions in the "digital transformation economy." Accordingly, attendees at the upcoming Cloud Expo | @ThingsExpo at the Santa Clara Convention Center in Santa Clara, CA, Oct 31-Nov 2, will find fresh new content in a new track called Enterprise Cloud & Digital Transformation.
Enterprises are adopting Kubernetes to accelerate the development and the delivery of cloud-native applications. However, sharing a Kubernetes cluster between members of the same team can be challenging. And, sharing clusters across multiple teams is even harder. Kubernetes offers several constructs to help implement segmentation and isolation. However, these primitives can be complex to understand and apply. As a result, it’s becoming common for enterprises to end up with several clusters. This leads to a waste of cloud resources and increased operational overhead.
SYS-CON Events announced today that Avere Systems, a leading provider of enterprise storage for the hybrid cloud, will exhibit at SYS-CON's 21st International Cloud Expo®, which will take place on Oct 31 - Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA. Avere delivers a more modern architectural approach to storage that doesn't require the overprovisioning of storage capacity to achieve performance, overspending on expensive storage media for inactive data or the overbuilding of data centers to house increasing amounts of storage infrastructure.
Today most companies are adopting or evaluating container technology - Docker in particular - to speed up application deployment, drive down cost, ease management and make application delivery more flexible overall. As with most new architectures, this dream takes significant work to become a reality. Even when you do get your application componentized enough and packaged properly, there are still challenges for DevOps teams to making the shift to continuous delivery and achieving that reduction in cost and increase in speed. Sometimes in order to reduce complexity teams compromise features ...
SYS-CON Events announced today that IBM has been named “Diamond Sponsor” of SYS-CON's 21st Cloud Expo, which will take place on October 31 through November 2nd 2017 at the Santa Clara Convention Center in Santa Clara, California.
In this strange new world where more and more power is drawn from business technology, companies are effectively straddling two paths on the road to innovation and transformation into digital enterprises. The first path is the heritage trail – with “legacy” technology forming the background. Here, extant technologies are transformed by core IT teams to provide more API-driven approaches. Legacy systems can restrict companies that are transitioning into digital enterprises. To truly become a leader in the space, it’s necessary to accommodate, incorporate, and exploit the new breed of enterprise...
Digital transformation leaders have poured tons of money and effort into coding in recent years. And with good reason. To succeed at digital, you must be able to write great code. You also have to build a strong Agile culture so your coding efforts tightly align with market signals and business outcomes. But if your investments in testing haven’t kept pace with your investments in coding, you’ll lose. But if your investments in testing haven’t kept pace with your investments in coding, you’ll lose. Here’s why: Reason #1: Functional testing is one of your worst DevOps bottlenecks.
Most technology leaders, contemporary and from the hardware era, are reshaping their businesses to do software. They hope to capture value from emerging technologies such as IoT, SDN, and AI. Ultimately, irrespective of the vertical, it is about deriving value from independent software applications participating in an ecosystem as one comprehensive solution. In his session at @ThingsExpo, Kausik Sridhar, founder and CTO of Pulzze Systems, will discuss how given the magnitude of today's application ecosystem, tweaking existing software to stitch various components together leads to sub-optimal ...
In a recent survey, Sumo Logic surveyed 1,500 customers who employ cloud services such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). According to the survey, a quarter of the respondents have already deployed Docker containers and nearly as many (23 percent) are employing the AWS Lambda serverless computing framework. It’s clear: serverless is here to stay. The adoption does come with some needed changes, within both application development and operations. That means serverless is also changing the way we leverage public clouds. Truth-be-told, many enterpris...
In his Opening Keynote at 21st Cloud Expo, John Considine, General Manager of IBM Cloud Infrastructure, will lead you through the exciting evolution of the cloud. He'll look at this major disruption from the perspective of technology, business models, and what this means for enterprises of all sizes. John Considine is General Manager of Cloud Infrastructure Services at IBM. In that role he is responsible for leading IBM’s public cloud infrastructure including strategy, development, and offering management. To date, IBM has launched more than 50 cloud data centers that span the globe. He has be...