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Table of contents

Gartner, Cisco, and Intel estimate there will be between 20 and no, they don't agree, surprise! But it's not just the quantity of devices. Consider how much data is coming off of each one. I have a temperature sensor in my garage. Even with a one-minute level of granularity one measurement a minute , that's still , data points in a year, and that's just one sensor. Let's say you have a factory with a thousand sensors, you're looking at half a billion data points, just for the temperature alone.

Or, consider our new world of connected apps. Everyone is carrying a smartphone. Let's look at a simple example, a to-do list app. More and more vendors are managing app data in the cloud, so users can access their to-do lists across devices. Since many apps use a freemium model , where a free version is used as a loss-leader for a premium version, SaaS-based app vendors tend to have a lot of data to store. Todoist , for example the to-do manager I use has roughly 10 million active installs, according to Android Play.

That's not counting all the installs on the Web and iOS.

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Each of those users has lists of items -- and all that data needs to be stored. Todoist is certainly not Facebook scale, but they still store vastly more data than almost any application did even a decade ago. Then, of course, there are all the internal enterprise collections of data, ranging from energy industry to healthcare to national security. All of these industries are generating and capturing vast amounts of data.

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Remember our Facebook example? But if you want your mind blown, consider this: Facebook users upload more than million photos a day. A day. So that billion number from last year will seem like a drop in the bucket in a few months. Also: Facebook explains Fabric Aggregator, its distributed network system. Velocity is the measure of how fast the data is coming in. Facebook has to handle a tsunami of photographs every day. It has to ingest it all, process it, file it, and somehow, later, be able to retrieve it.

Here's another example. Let's say you're running a marketing campaign and you want to know how the folks "out there" are feeling about your brand right now. How would you do it? One way would be to license some Twitter data from Gnip acquired by Twitter to grab a constant stream of tweets, and subject them to sentiment analysis. That feed of Twitter data is often called "the firehose" because so much data in the form of tweets is being produced, it feels like being at the business end of a firehose.


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Here's another velocity example: packet analysis for cybersecurity. The Internet sends a vast amount of information across the world every second. For an enterprise IT team, a portion of that flood has to travel through firewalls into a corporate network. Unfortunately, due to the rise in cyberattacks, cybercrime, and cyberespionage, sinister payloads can be hidden in that flow of data passing through the firewall.

To prevent compromise, that flow of data has to be investigated and analyzed for anomalies, patterns of behavior that are red flags. This is getting harder as more and more data is protected using encryption. At the very same time, bad guys are hiding their malware payloads inside encrypted packets.

Everything you need to know about the Internet of Things right now. Or take sensor data. The more the Internet of Things takes off, the more connected sensors will be out in the world, transmitting tiny bits of data at a near constant rate.

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As the number of units increase, so does the flow. You may have noticed that I've talked about photographs, sensor data, tweets, encrypted packets, and so on. Each of these are very different from each other. This data isn't the old rows and columns and database joins of our forefathers. It's very different from application to application, and much of it is unstructured. That means it doesn't easily fit into fields on a spreadsheet or a database application. Take, for example, email messages.

A legal discovery process might require sifting through thousands to millions of email messages in a collection. Not one of those messages is going to be exactly like another. Each one will consist of a sender's email address, a destination, plus a time stamp. Each message will have human-written text and possibly attachments. Photos and videos and audio recordings and email messages and documents and books and presentations and tweets and ECG strips are all data, but they're generally unstructured, and incredibly varied. It would take a library of books to describe all the various methods that big data practitioners use to process the three Vs.

For now, though, your big takeaway should be this: once you start talking about data in terms that go beyond basic buckets, once you start talking about epic quantities, insane flow, and wide assortment, you're talking about big data. One final thought: there are now ways to sift through all that insanity and glean insights that can be applied to solving problems, discerning patterns, and identifying opportunities.


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    • That process is called analytics, and it's why, when you hear big data discussed, you often hear the term analytics applied in the same sentence. The three Vs describe the data to be analyzed. Analytics is the process of deriving value from that data. Taken together, there is the potential for amazing insight or worrisome oversight. Like every other great power, big data comes with great promise and great responsibility. By the way, I'm doing more updates on Twitter and Facebook than ever before.

    Big Data Cloud storage becomes the de facto data lake.


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      • While AI, IoT, and GDPR grab the headlines, don't forget about the about the generational impact that cloud migration and streaming will have on big data implementations. Executive's guide to IoT and big data free ebook. The Internet of Things and big data are growing at an astronomical rate. This ebook explores the consequences and benefits of this expanding digital universe -- and what it could mean for your organization. A day in the data science life: Salesforce's Dr. Shrestha Basu Mallick. Here's a look at how a Salesforce data scientist approached a price optimization model based on what expert sellers were doing in the field.

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      The 10 cities with the highest salaries for data scientists [TechRepublic]. Here are the best places to find a high-paying job in the field. Linux gaming made easy: The fastest way to get up and running. In the database approach, ideally, each data item is stored in only one place in the database. In some cases, data redundancy still exists to improve system performance, but such redundancy is controlled by application programming and kept to minimum by introducing as little redudancy as possible when designing the database.

      The integration of all the data, for an organization, within a database system has many advantages. First, it allows for data sharing among employees and others who have access to the system. Second, it gives users the ability to generate more information from a given amount of data than would be possible without the integration.

      Volume, velocity, and variety: Understanding the three V's of big data

      Database management systems must provide the ability to define and enforce certain constraints to ensure that users enter valid informatio n and maintain data integrity. A database constraint is a restriction or rule that dictates what can be entered or edited in a table such as a postal code using a certain format or adding a valid city in the City field.

      There are many types of database constraints. Data type , for example, determines the sort of data permitted in a field, for example numbers only.