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Using Big Data to Cure Illness and Save Lives By @Dana_Gardner | @BigDataExpo #BigData
How New York Genome Center manages the massive data generated from DNA sequencing

The next BriefingsDirect big-data use case discussion examines how the non-profit New York Genome Center manages and analyzes up to 12 terabytes of data generated each day from its genome sequence appliances. We’ll learn how a swift, cost efficient, and accessible big-data analytics platform supports the drive to better diagnose disease and develop more effective medical treatments.

To hear how genome analysis pioneers exploit vast data outputs to speedily correlate for time-sensitive research, please join me in welcoming our guest, Toby Bloom, Deputy Scientific Director for Informatics at the New York Genome Center in New York City. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: First, tell us a little bit about your organization. It seems like it’s a unique institute, with a large variety of backers, consortium members. Tell us about it.

Bloom

Bloom: New York Genome Center is about two-and-a-half years old. It was formed initially as a collaboration among 12 of the large medical institutions in New York: Cornell, Columbia, NewYork-Presbyterian Hospital, Mount Sinai, NYU, Einstein Montefiore, and Stony Brook University. All of the big hospitals in New York decided that it would be better to have one genome center than have to build 12 of them. So we were formed initially to be the center of genomics in New York.

Gardner: And what does one do at a center of genomics?

Bloom: We're a biomedical research facility that has a large capacity to sequence genomes and use the resulting data output to analyze the genomes, find the causes of disease, and hopefully treatments of disease, and have a big impact on healthcare and on how medicine works now.

Gardner: When it comes to doing this well, it sounds like you are generating an awesome amount of data. What sort of data is that and where does it come from?

Bloom: Right now, we have a number of genome sequencing instruments that produce about 12 terabytes of raw data per day. That raw data is basically lots of strings of As, Cs, Ts and Gs -- the DNA data from genomes from patients who we're sequencing. Those can be patients who are sick and we are looking for specific treatment. They can be patients in large research studies, where we're trying to use and correlate a large number of genomes to find the similarities that show us the cause of the disease.

Gardner: When we look at a typical big data environment such as in a corporation, it’s often transactional information. It might also be outputs from sensors or machines. How is this a different data problem when you are dealing with DNA sequences?

Lots of data

Bloom: Some of it’s the same problem, and some of it’s different. We're bringing in lots of data. The raw data, I said, is probably about 12 terabytes a day right now. That could easily double in the next year. But than we analyze the data, and I probably store three to four times that much data in a day.

In a lot of environments, you start with the raw data, you analyze it, and you cook it down to your answers. In our environment, it just gets bigger and bigger for a long time, before we get the answers and can make it smaller. So we're dealing with very large amounts of data.

We do have one research project now that is taking in streaming data from devices, and we think over time we'll likely be taking in data from things like cardiac monitors, glucose monitors, and other kinds of wearable medical devices. Right now, we are taking in data off apps on smartphones that are tracking movement for some patients in a rheumatoid arthritis study we're doing.

In our environment, it just gets bigger and bigger for a long time, before we get the answers and can make it smaller. So we're dealing with very large amounts of data.

We have to analyze a bunch of different kinds of data together. We’d like to bring in full medical records for those patients and integrate it with the genomic data. So we do have a wide variety of data that we have to integrate, and a lot of it is quite large.

Gardner: When you were looking for the technological platforms and solutions to accommodate your specific needs, how did that pan out? What works? What doesn’t work? And where are you in terms of putting in place the needed infrastructure?

Bloom: The data that comes off the machines is in large files, and a lot of the complex analysis we do, we do initially on those large files. I am talking about files that are from 150 to 500 gigabytes or maybe a terabyte each, and we do a lot of machine-learning analysis on those. We do a bunch of Bayesian statistical analyses. There are a large number of methods we use to try to extract the information from that raw data.

When we've figured out the variance and mutations in the DNA that we think are correlated with the disease and that we were interested in looking at, we then want to load all of that into a database with all of the other data we have to make it easy for researchers to use in a number of different ways. We want to let them find more data like the data they have, so that they can get statistical validation of their hypotheses.

We want them to be able to find more patients for cohorts, so they can sequence more and get enough data. We need to be able to ask questions about how likely it is, if you have a given genomic variant, you get a given disease. Or, if you have the disease, how likely it is that you have this variant. You can only do that if it’s easy to find all of that data together in one place in an organized way.

So we really need to load that data into a database and connect it to the medical records or the symptoms and disease information we have about the patients and connect DNA data with RNA data with epigenetic data with microbiome data. We needed a database to do that.

We looked at a number of different databases, but we had some very hard requirements to solve. We were looking for one that could handle trillions of rows in a table without failing over, tens of trillions of rows without falling over, and to be able to answer queries fast across multiple tables with tens of trillions of rows. We need to be able to easily change and add new kinds of data to it, because we're always finding new kinds of data we want to correlate. So there are things like that.

Simple answer

We need to be able to load terabytes of data a day. But more than anything, I had a lot of conversations with statisticians about why they don’t like databases, about why they keep asking me for all of the data in comma-delimited files instead of databases. And the answer, when you boiled it down, was pretty simple.

When you have statisticians who are looking at data with huge numbers of attributes and huge numbers of patients, the kinds of statistical analysis they're doing means they want to look at some much smaller combinations of the attributes for all of the patients and see if they can find correlations, and then change that and look at different subsets. That absolutely requires a column-oriented database. A row-oriented relational database will bring in the whole database to get you that data. It takes forever, and it’s too slow for them.

So, we started from that. We must have looked at four or five different databases. Hewlett Packard Enterprise (HPE) Vertica was the one that could handle the scale and the speed and was robust and reliable enough, and is our platform now. We're still loading in the first round of our data. We're still in the tens of billions of rows, as opposed to trillions of rows, but we'll get there.

We must have looked at four or five different databases. Vertica was the one that could handle the scale and the speed and was robust and reliable enough and is our platform now.

Gardner: You’re also in the healthcare field. So there are considerations around privacy, governance, auditing, and, of course, price sensitivity, because you're a non-profit. How did that factor into your decision? Is the use of off-the-shelf hardware a consideration, or off-the-shelf storage? Are you looking at conversion infrastructure? How did you manage some of those cost and regulatory issues?

Bloom: Regulatory issues are enormous. There are regulations on clinical data that we have to deal with. There are regulations on research data that overlap and are not fully consistent with the regulations on clinical data. We do have to be very careful about who has access to which sets of data, and we have all of this data in one database, but that doesn’t mean any one person can actually have access to all of that data.

We want it in one place, because over time, scientists integrate more and more data and get permission to integrate larger and larger datasets, and we need that. There are studies we're doing that are going to need over 100,000 patients in them to get statistical validity on the hypotheses. So we want it all in one place.

What we're doing right now is keeping all of the access-control information about who can access which datasets as data in the database, and we basically append clauses to every query to filter down the data to the data that any particular user can use. Then we'll tell them the answers for the datasets they have and how much data that’s there that they couldn’t look at, and if they needed the information, how to go try to get access to that.

Gardner: So you're able to manage some of those very stringent requirements around access control. How about that infrastructure cost equation?

Bloom: Infrastructure cost is a real issue, but essentially, what we're dealing with is, if we're going to do the work we need to do and deal with the data we have to deal with, there are two options. We spend it on capital equipment or we spend it on operating costs to build it ourselves.

In this case, not all cases, it seemed to make much more sense to take advantage of the equipment and software, rather than trying to reproduce it and use our time and our personnel's time on other things that we couldn’t as easily get.

A lot of work went into HPE Vertica. We're not going to reproduce it very easily. The open-source tools that are out there don’t match it yet. They may eventually, but they don’t now.

Getting it right

Gardner: When we think about the paybacks or determining return on investment (ROI) in a business setting, there’s a fairly simple straightforward formula. For you, how do you know you’ve got this right? What is it when you see certain, what we might refer to in the business world as service-level agreements (SLAs) or key performance indicators (KPIs)? What are you looking for when you know that you’ve got it right and when you’re getting the job done, based all of its requirements and from all of these different constituencies?

Bloom: There’s a set of different things. The thing I am looking for first is whether the scientists who we work with most closely, who will use this first, will be able to frame the questions they want to ask in terms of the interface and infrastructure we’ve provided.

I want to know that we can answer the scientific questions that people have with the data we have and that we’ve made it accessible in the right way. That we’ve integrated, connected and aggregated the data in the right ways, so they can find what they are looking for. There's no easy metric for that. There’s going to be a lot of beta testing.

The place where this database is going to be the most useful, not by any means the only way it will be used, is in our investigations of common and complex diseases, and how we find the causes of them and how we can get from causes to treatments.

The second thing is, are we are hitting the performance standards we want? How much data can I load how fast? How much data can I retrieve from a query? Those statisticians who don’t want to use relational databases, still want to pull out all those columns and they want to do their sophisticated analysis outside the database.

Eventually, I may convince them that they can leave the data in the database and run their R-scripts there, but right now they want to pull it out. I need to know that I can pull it out fast for them, and that they're not going to object that this is organized so they can get their data out.

Gardner: Let's step back to the big picture of what we can accomplish in a health-level payback. When you’ve got the data managed, when you’ve got the input and output at a speed that’s acceptable, when you’re able to manage all these different level studies, what sort of paybacks do we get in terms of people’s health? How do we know we are succeeding when it comes to disease, treatment, and understanding more about people and their health?

Bloom: The place where this database is going to be the most useful, not by any means the only way it will be used, is in our investigations of common and complex diseases, and how we find the causes of them and how we can get from causes to treatments.

I'm talking about looking at diseases like Alzheimer’s, asthma, diabetes, Parkinson’s, and ALS, which is not so common, but certainly falls in the complex disease category. These are diseases that are caused by some combinations of genomic variance, not by a single gene gone wrong. There are a lot of complex questions we need to ask in finding those. It takes a lot of patience and a lot of genomes, to answer those questions.

The payoff is that if we can use this data to collect enough information about enough diseases that we can ask the questions that say it looks like this genomic variant is correlated with this disease, how many people in your database have this variant and of those how many actually have the disease, and of the ones who have the disease, how many have this variant. I need to ask both those questions, because a lot of these variants confer risk, but they don’t absolutely give you the disease.

If I am going to find the answers, I need to be able to ask those questions and those are the things that are really hard to do with the raw data in files. If I can do just that, think about the impact on all of us? If we can find the molecular causes of Alzheimer’s that could lead to treatments or prevention and all of those other diseases as well.

You may also be interested in:

About Dana Gardner
At Interarbor Solutions, we create the analysis and in-depth podcasts on enterprise software and cloud trends that help fuel the social media revolution. As a veteran IT analyst, Dana Gardner moderates discussions and interviews get to the meat of the hottest technology topics. We define and forecast the business productivity effects of enterprise infrastructure, SOA and cloud advances. Our social media vehicles become conversational platforms, powerfully distributed via the BriefingsDirect Network of online media partners like ZDNet and IT-Director.com. As founder and principal analyst at Interarbor Solutions, Dana Gardner created BriefingsDirect to give online readers and listeners in-depth and direct access to the brightest thought leaders on IT. Our twice-monthly BriefingsDirect Analyst Insights Edition podcasts examine the latest IT news with a panel of analysts and guests. Our sponsored discussions provide a unique, deep-dive focus on specific industry problems and the latest solutions. This podcast equivalent of an analyst briefing session -- made available as a podcast/transcript/blog to any interested viewer and search engine seeker -- breaks the mold on closed knowledge. These informational podcasts jump-start conversational evangelism, drive traffic to lead generation campaigns, and produce strong SEO returns. Interarbor Solutions provides fresh and creative thinking on IT, SOA, cloud and social media strategies based on the power of thoughtful content, made freely and easily available to proactive seekers of insights and information. As a result, marketers and branding professionals can communicate inexpensively with self-qualifiying readers/listeners in discreet market segments. BriefingsDirect podcasts hosted by Dana Gardner: Full turnkey planning, moderatiing, producing, hosting, and distribution via blogs and IT media partners of essential IT knowledge and understanding.

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CLIFFE

7Summits

DE SOUZA
Cisco

RANDALL
Gartner

ARM-
STRONG

AppNeta

SMALL-
TREE

Cazena

MCCARTHY
Bsquare

DELOACH
Infobright

QUINT
Ontegrity

MALAU-
CHLAN

Buddy Platform

PALIOTTA
Vector

MITRA
Cognizant

KOCHER
Grey Heron

PAPDO
POULOS

Cloud9

HARLAN
Two Bulls

GOLO
SHUBIN

Bit6

PROIETTI
Location
Smart

MARTIN
nfrastructure

MOULINE
Everbridge

MARSH
Blue Pillar

PARKS
SecureRF

PEROTTI
Plantronics

HOFFMAN
EastBanc

WATSON
Trendalyze

BENSON-
OFF

Unigma

SHAN
CTS

MATTELA
Redpine

GILLEN
Spark
Coginition

SOLT
Netvibes

BERN-
ARDO

GE Digital

ROMAN-
SKY

TrustPoint

BEAMER
GoTransverse

LESTER
LogMeIn

PONO
-MAREVA

Google

SINGH
Sencha

CALKINS
Amadeus

KLEIN
Rachio

HOASIN
Aeris

SARKARIA
PHEMI

SPROULE
Metavine

SNELL
Intel

LEVINE
CytexOne

ALLEN
Freewave

MCCAL-
LUM

Falconstor

HYEDT
Seamless

WebRTC Summit Silicon Valley All-Star Speakers Include

SCHULZ
Luxoft

TAM-
BURINI

Autodesk

MCCARTHY
Bsquare

THURAI
SaneIoT

TURNER
Cloudian

ENDO
Intrepid

NAKAGAWA
Transparent

SHIBATA
Transparent

LEVANT-LEVI
testRTC

VARAN NATH
GE

COOPER
M2Mi

SENAY
Teletax

SKEEN
Vitria

KOCHER
Grey Heron

GREENE
PubNub

MAGUIRE
HP

MATTHIEU
Octoblu

STEINER-
JOVIC

AweSense

LYNN
AgilData

HEDGES
Cloudata

DUFOUR
Webroot

ROBERTS
Platform

JONES
Deep

PFEIFFER
NICTA

NIELSEN
Redis

PAOLAL-
ANTORIO

DataArchon

KAHN
Solgenia

LOPEZ
Kurento

KIM
MapR

BROMHEAD
Instaclustr

LEVINE
CytexOne

BONIFAZI
Solgenia

GORBA-
CHEV

Intelligent
Systems

THYKAT-
TIL

Navisite

TRELOAR
Bebaio

SIVARAMA-
KRISHNAN

Red Hat
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

BLOOM-
BERG

Intellyx

BUDHANI
Soha

HATHAWAY
IBM Watson

TOLL
ProfitBricks

LANDRY
Microsoft

BEARFIELD
Blue Box

HERITAGE
Akana

PILUSO
SIASMSP

HOLT
IBM Cloudant

SHAN
CTS

PICCIN-
INNI

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

BERMING-
HAM

SIOS

WILLIS
Stateless Networks

MURPHY
Gridstore

KHABE
Vicom

NIKOLOV
GetClouder

DIETZE
Windstream

DALRY-
MPLE

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
Transparent Cloud Computing

NATH
GE

GOKCEN
GE

STOICA
Databricks

TANKEL
Pivotal Software


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
CEO of @Cnnct2me
 
One of the best conferences we have attended in a while. Great job, Cloud Expo team! Keep it going."

@Peak_Ten


Who Should Attend?
Senior Technologists including CIOs, CTOs & Vps of Technology, Chief Systems Engineers, IT Directors and Managers, Network and Storage Managers, Enterprise Architects, Communications and Networking Specialists, Directors of Infrastructure.

Business Executives including CEOs, CMOs, & CIOs , Presidents & SVPs, Directors of Business Development , Directors of IT Operations, Product and Purchasing Managers, IT Managers.

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SYS-CON Media has a flourishing Media Partner program in which mutually beneficial promotion and benefits are arranged between our own leading Enterprise IT portals and events and those of our partners.

If you would like to participate, please provide us with details of your website/s and event/s or your organization and please include basic audience demographics as well as relevant metrics such as ave. page views per month.

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@CloudExpo Blogs
In his keynote at 18th Cloud Expo, Andrew Keys, Co-Founder of ConsenSys Enterprise, provided an overview of the evolution of the Internet and the Database and the future of their combination – the Blockchain. Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settlement products to hedge funds and investment banks. After, he co-founded a revenue cycle management c...
Blockchain offers impeccable security with its cryptography-based decentralized system as well as the plethora of possible uses retailers could exploit in the near future. In a world of increasing cyberattacks, internet fraud and online hacking, blockchain comes as a breath of fresh air. With its encrypted data and decentralized network system, it's a thorn in every hacker's side. Generally being associated with the finance sector, blockchain is now taking retail by storm. It's on a course that will change the retail industry as we know it. But how exactly is it going to achieve such a feat?
Blockchain. A day doesn’t seem to go by without seeing articles and discussions about the technology. According to PwC executive Seamus Cushley, approximately $1.4B has been invested in blockchain just last year. In Gartner’s recent hype cycle for emerging technologies, blockchain is approaching the peak. It is considered by Gartner as one of the ‘Key platform-enabling technologies to track.’ While there is a lot of ‘hype vs reality’ discussions going on, there is no arguing that blockchain is being taken very seriously across industries and cannot be ignored.
Making informed network investment decisions about emerging technologies such as network function virtualization (NFV) and software-defined networking (SDN) can help evolve the network to keep pace with the innovations of the devices and people it’s connecting. As you work with business leaders to make decisions about upgrading your infrastructure with these networking developments, it’s important to understand the similarities, differences, and benefits of dual NFV and SDN implementation. With their ability to offer a new way to design, deploy, and manage the network and its services, NFV a...
ChatOps is an emerging topic that has led to the wide availability of integrations between group chat and various other tools/platforms. Currently, HipChat is an extremely powerful collaboration platform due to the various ChatOps integrations that are available. However, DevOps automation can involve orchestration and complex workflows. In his session at @DevOpsSummit at 20th Cloud Expo, Himanshu Chhetri, CTO at Addteq, will cover practical examples and use cases such as self-provisioning infrastructure/applications, self-remediation workflows, integrating monitoring and complimenting integra...
You know you need the cloud, but you’re hesitant to simply dump everything at Amazon since you know that not all workloads are suitable for cloud. You know that you want the kind of ease of use and scalability that you get with public cloud, but your applications are architected in a way that makes the public cloud a non-starter. You’re looking at private cloud solutions based on hyperconverged infrastructure, but you’re concerned with the limits inherent in those technologies.
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, discussed how from store operations and optimization to employee training and insights, all ultimately create the best customer experience b...
"IBM is really all in on blockchain. We take a look at sort of the history of blockchain ledger technologies. It started out with bitcoin, Ethereum, and IBM evaluated these particular blockchain technologies and found they were anonymous and permissionless and that many companies were looking for permissioned blockchain," stated René Bostic, Technical VP of the IBM Cloud Unit in North America, in this SYS-CON.tv interview at 21st Cloud Expo, held Oct 31 – Nov 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
A strange thing is happening along the way to the Internet of Things, namely far too many devices to work with and manage. It has become clear that we'll need much higher efficiency user experiences that can allow us to more easily and scalably work with the thousands of devices that will soon be in each of our lives. Enter the conversational interface revolution, combining bots we can literally talk with, gesture to, and even direct with our thoughts, with embedded artificial intelligence, which can process our conversational commands and orchestrate the outcomes we request across our persona...
Most of us understand that artificial intelligence (AI) offers opportunities for productivity improvements in the form of speed, automation, standardized actions and responses, plus the opportunity for continuous improvements via machine learning. These opportunities are enabled by data inputs that are analyzed and processed through AI algorithms that execute a desired decision and action. For all of the great capabilities and benefits that AI can provide, there is also a potential dark side. AI solutions can easily codify our prejudices, bias, gender stereotypes and promote injustices intenti...
BnkToTheFuture.com is the largest online investment platform for investing in FinTech, Bitcoin and Blockchain companies. We believe the future of finance looks very different from the past and we aim to invest and provide trading opportunities for qualifying investors that want to build a portfolio in the sector in compliance with international financial regulations.
The need for greater agility and scalability necessitated the digital transformation in the form of following equation: monolithic to microservices to serverless architecture (FaaS). To keep up with the cut-throat competition, the organisations need to update their technology stack to make software development their differentiating factor. Thus microservices architecture emerged as a potential method to provide development teams with greater flexibility and other advantages, such as the ability to deliver applications at warp speed using infrastructure as a service (IaaS) and platform as a ...
For DevOps teams, the concepts behind service-oriented architecture (SOA) are nothing new. A style of software design initially made popular in the 1990s, SOA was an alternative to a monolithic application; essentially a collection of coarse-grained components that communicated with each other. Communication would involve either simple data passing or two or more services coordinating some activity. SOA served as a valid approach to solving many architectural problems faced by businesses, as applications could be developed by more teams working in parallel and more productively by reusing orig...
Product connectivity goes hand and hand these days with increased use of personal data. New IoT devices are becoming more personalized than ever before. In his session at 22nd Cloud Expo | DXWorld Expo, Nicolas Fierro, CEO of MIMIR Blockchain Solutions, will discuss how in order to protect your data and privacy, IoT applications need to embrace Blockchain technology for a new level of product security never before seen - or needed.
It has never been a better time to be a developer! Thanks to cloud computing, deploying our applications is much easier than it used to be. How we deploy our apps continues to evolve thanks to cloud hosting, Platform-as-a-Service (PaaS), and now Function-as-a-Service. FaaS is the concept of serverless computing via serverless architectures. Software developers can leverage this to deploy an individual "function", action, or piece of business logic. They are expected to start within milliseconds and process individual requests and then the process ends.
Leading companies, from the Global Fortune 500 to the smallest companies, are adopting hybrid cloud as the path to business advantage. Hybrid cloud depends on cloud services and on-premises infrastructure working in unison. Successful implementations require new levels of data mobility, enabled by an automated and seamless flow across on-premises and cloud resources. In his general session at 21st Cloud Expo, Greg Tevis, an IBM Storage Software Technical Strategist and Customer Solution Architect, explored how storage and software-defined solutions from IBM have evolved for the road ahead. Lea...
Some journey to cloud on a mission, others, a deadline. Change management is useful when migrating to public, private or hybrid cloud environments in either case. For most, stakeholder engagement peaks during the planning and post migration phases of a project. Legacy engagements are fairly direct: projects follow a linear progression of activities (the “waterfall” approach) – change managers and application coders work from the same functional and technical requirements. Enablement and development mirror one another, progressing from proof-of-concept planning to final product delivery. Exce...
Gone are the days when application development was the daunting task of the highly skilled developers backed with strong IT skills, low code application development has democratized app development and empowered a new generation of citizen developers. There was a time when app development was in the domain of people with complex coding and technical skills. We called these people by various names like programmers, coders, techies, and they usually worked in a world oblivious of the everyday priorities of the business world. However, with the passage of time, this scenario is much more democr...
The use of containers by developers -- and now increasingly IT operators -- has grown from infatuation to deep and abiding love. But as with any long-term affair, the honeymoon soon leads to needing to live well together ... and maybe even getting some relationship help along the way. And so it goes with container orchestration and automation solutions, which are rapidly emerging as the means to maintain the bliss between rapid container adoption and broad container use among multiple cloud hosts. This BriefingsDirect cloud services maturity discussion focuses on new ways to gain container orc...
Blockchain is a shared, secure record of exchange that establishes trust, accountability and transparency across business networks. Supported by the Linux Foundation's open source, open-standards based Hyperledger Project, Blockchain has the potential to improve regulatory compliance, reduce cost as well as advance trade. Are you curious about how Blockchain is built for business? In her session at 21st Cloud Expo, René Bostic, Technical VP of the IBM Cloud Unit in North America, discussed the basics of Blockchain, previewed the Blockchain Reference Architecture, and introduced the mechanics o...