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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.

Read the original blog entry...

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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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."
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@CloudExpo Blogs
Big data is old news. Today, the key to leveraging data effectively is to do fast data. In a similar fashion, traditional incident management—which entails collecting and analyzing large volumes of monitoring information—is no longer enough. Organizations must also now do “fast monitoring,” which means not only collecting monitoring data; but making it actionable in real-time. This post examines what fast monitoring means, and explains how incident management teams can implement this approach to realize great benefits.
Digital transformation requires rapid network changes. Navigating these changes well is difficult but critical, as the function of business applications are at stake. Customers aren’t interested in what’s going on behind the scenes; they simply want the platform to work. Hosting Facts researchers found that a single-second delay in website loading time can result in a seven percent loss in conversion, and 40 percent of web users will abandon a website if it takes longer than three seconds to load.
The mad dash to connect virtually every noun to the internet or The Internet of Things, is creating a massive M2M network for all the devices, systems, sensors and actuators to connect & communicate on the Internet. With that, they need a communications protocol to understand each other. One of those is Message Queue Telemetry Transport (MQTT). MQTT is a “subscribe and publish” messaging protocol designed for lightweight machine-to-machine (or IoT) communications. In this episode of Lightboard Lessons, I light up how MQTT works.
In 2016, blockchain technology came close to hitting its peak on Gartner’s annual Hype Cycle, signaling an imminent shift from an emerging, theoretical technology to widespread adoption. Like cloud, big data, and the Internet of Things (IoT) before it, blockchain is the tech industry’s latest Next Big Thing. Analysts and industry experts say it holds immense potential for organizations, but many business leaders don’t yet see a practical application for their operations. While a lot of people know blockchain is the technology behind Bitcoin, Ethereum, and other cryptocurrencies, what about ent...
These days attacks are becoming more sophisticated and more common. Mobile devices, cloud computing and the Internet of Things have increased the number of access points that must be secured. To complicate matters, CISOs are been directed to secure system without compromising the seamless experience that customers expect across channels, and if the organization is in a regulated industry, compliance issues likely increase the team’s workload. To best detect threats and respond to incidents quickly, many organizations decide they need a security operations center to provide proper protection 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...
Information Technology has advanced in different areas at different speeds. This has always been true, as people found workable solutions to the problems that faced them today, most often before there was a market for those solutions. While things like virtualization and eventually cloud computing grew slowly, programming languages sat at the same point for around a decade before the current round of new languages and approaches—Python, Ruby, Node, Swift—came about. These things seem unrelated, but there is a union of the two sets of changes that we are about to see blossom.
Special thanks for the help on this blog to the coolest, most hip group of industry experts that I have ever met: the Pathfinders. The Pathfinders is an elite forces group of master system engineers inside of Dell EMC who tackle our customers’ most difficult and inspiring challenges. I am honored to be part of that club! Suppose an autonomous vehicle learns of a more efficient route and wants sell this knowledge to other autonomous cars for a fee (using blockchain to handle machine to machine transaction). Suppose the autonomous vehicle could start to monetize itself; to self-fund its own op...
It's 3 am in California, and you are still awake trying to sort through a release process that has been delayed by several hours. The deployment to the main application cluster took several extra hours due to an unanticipated problem with the servers, and now everyone is waiting on the lead database administrator to call into a conference bridge so you can all move on to Step 53.5b of the deployment. Your production deployments always seem to be problematic and this one might be the worst you've experienced yet. Everyone's Tired: Bad Decisions AboundBy "everyone," I mean the 30 QA testers and...
It's been way too long, but the next in the series of IoT "Build-along-at-home" videos is done. In this edition I will show you the "Hello World" project for interacting with the IO ports on your Raspberry Pi. Please click here to watch the video and as always, comments and questions are appreciated and welcomed! If you are the type (like me) who just likes to figure stuff out on their own, I have heavily commented the code for you so you can get started quickly without the video, but it will help you with the hardware assemblies! Also I am already working on the next projects, using the moist...
Next month will mark the 47th anniversary of Apollo 13. The film that recounts the story is a favorite of mine, probably because it so masterfully captures the incredible suspense, fear, and hope felt by people everywhere, that I personally recall very well. I also know how an organization’s digital transformation can generate similar reactions! Apollo 13 was likely a casualty of the space program’s incredibly aggressive schedule. NASA, with U.S. political and military leaders, was motivated by competition from the U.S.S.R. and fear of the potential consequences of not being first. Urgency oft...
BYOD. IoT. Cloud computing. DevOps. IT professionals today have more demands (and more acronyms) on their time than ever. Application development and delivery are changing rapidly and increasing in complexity, revealing the limitations of traditional DNS approaches in achieving modern network goals. The IT department has made use of open source platforms for decades to provide DNS and traffic management in their internal networks and for their public, internet-facing services. The majority of DNS servers, both on the internet and in enterprise intranets, are open source-based solutions such as...
Containerization is popularly viewed as the ‘virtualization of virtualization’ or ‘next generation virtualization.’ However, containers have existed long before virtualization or the advent of modern container technology like Docker and Linux Containers. Similar tech was built into mainframe systems that pervaded the IT landscape for the preceding decades. However, the implication, as the name suggests, is that modern software containerization will have the same seismic impact on the IT industry as shipping containers have had on maritime freight transport. Indeed, it is quite common now for ...
Lots of cloud technology predictions and analysis are still dealing with future spending and planning, but there are plenty of real-world cloud use cases and implementations happening now. One approach, taken by stalwart GE, is to use SaaS applications for non-differentiated uses. For them, that means moving functions like HR, finance, taxes and scheduling to SaaS, while spending their software development time and resources on the core apps that make GE better, such as inventory, planning and sales. Essentially, as for more and more large enterprises, GE is getting out of the infrastructure b...
I happened to be in India last November when prime minister Modi announced the demonetization program, where 86% of the currency in the form of two paper bills (Rs. 500 and 1000 denomination) were made defunct. People were given time to deposit their existing currencies in the bank. Those who had unusually high volume of such currencies were supposed to declare the legal source or face stiff penalties such as 60-75% tax. The goal was to catch the money hoarders and black marketers who avoid paying taxes on such undeclared money.
In the first article of this three-part series on hybrid cloud security, we discussed the Shared Responsibility Model, and examined how the most common attack strategies persist, are amplified, or are mitigated as assets move from data centers to the cloud. In part two, we talked about the unique security challenges that are introduced by public cloud environments. In this third and final installment, we’ll review why it’s essential to approach hybrid cloud security with different methods, tools and best practices than those used in the data center.
When discussing disruptive technologies, the topic of blockchain inevitably enters the conversation. Gartner recently listed blockchain as one of the ‘Key platform-enabling technologies to track.’ Approximately $1.4B has been invested in blockchain just this year, according to PwC executive Seamus Cushley. IBM announced this week a Blockchain-as-a-Service Enterprise offering at their Interconnect Conference. 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.
Choosing a programming framework for a small business can be overwhelming– there are so many. Here are a few of the best choices, to help you get started. Ask a room of ten developers which programming framework is the ‘best on the market,' and you're liable to receive ten different answers. Each developer will sing the praises of a different language, and each one will very probably feel that theirs is the only logical choice. The most confusing thing, though? Each and every one of those developers will be correct.
The IT industry is undergoing a significant evolution to keep up with cloud application demand. We see this happening as a mindset shift, from traditional IT teams to more well-rounded, cloud-focused job roles. The IT industry has become so cloud-minded that Gartner predicts that by 2020, this cloud shift will impact more than $1 trillion of global IT spending. This shift, however, has left some IT professionals feeling a little anxious about what lies ahead. The good news is that cloud computing is relatively new by IT's standards, which means it's constantly expanding to encompass new career...
As Enterprise business moves from Monoliths to Microservices, adoption and successful implementations of Microservices become more evident. The goal of Microservices is to improve software delivery speed and increase system safety as scale increases. Documenting hurdles and problems for the use of Microservices will help consultants, architects and specialists to avoid repeating the same mistakes and learn how and when to use (or not use) Microservices at the enterprise level. The circumstance when Microservices is an appropriate solution described in this article and based on the authors' wor...