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C2090-136 exam Dumps Source : Foundations of IBM titanic Data & Analytics Architecture V1
Test Code : C2090-136
Test designation : Foundations of IBM titanic Data & Analytics Architecture V1
Vendor designation : IBM
: 58 actual Questions
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informationone other response to IBM's Acquisition of red Hat
Now that the dirt has settled across the explosive announcement that IBM could breathe buying open supply utility company and longtime Java neighborhood technique (JCP) chief pink Hat, i needed to partake the response to the deal of one of the keenest (and most fearless) observers of the Java universe.
Gartner analyst Anne Thomas went toe-to-toe with the Java EE community final 12 months over the destiny of traffic Java, which is now developed and maintained by the Eclipse foundation as Jakarta EE. She took ponderous fireplace from that community over a analysis keep she co-authored with contributing analyst Aashish Gupta ("Market sheperd for software platforms").
In that research word the authors asserted, among different things, that Java EE has now not saved pace with architectural trends and digital company initiatives, that Java builders are demonstrating a transparent alternative for lightweight frameworks over Java EE, and that Java EE is not an acceptable framework for building cloud-native applications. They besides recommended those accountable for modernizing an enterprise's utility infrastructure to "boost a methodology to contend with the obsolescence of Java EE and different three-tier application frameworks."
I requested Thomas about the implications of this blockbuster acquisition in an e mail, and he or she stated that combining the IBM and pink Hat groups eliminates some conflicts between the two groups' traffic Java actions, however additionally warned of a virtually inevitable decline within the complete volume of components allotted to helping commercial enterprise Java.
"IBM and pink Hat are both largest contributors to the Java platform, apart from Oracle," she observed, "so instead of two agencies with doubtlessly conflicting agendas, there may breathe one. This ensures greater synergy, but reduces competition. and i assume that IBM will slit back the entire variety of individuals dedicated to assisting the Java group."
The synergy is actually there. IBM has long been a number one user and contributor to Linux, which undergirds purple Hat's exact products, together with the pink Hat traffic Linux (RHEL) OS. And IBM and pink Hat are energetic participants of the Java neighborhood. each groups hold claimed that the main roles they play in the JCP are not littered with the merger.
Thomas additionally upshot the acquisition in the context of probably the most arguments she and Gupta made in that now notorious analysis note.
"average Java EE is a relic of the previous," she talked about. "New building now occurs on the Eclipse foundation. And each IBM and red Hat had been lively in that group. besides the fact that children the instant labor there's concentrated on the current Java EE unlock, are expecting future efforts to breathe concentrated on the microprofile and cloud-native frameworks. again, combining the IBM and purple Hat teams may silent dispose of conflict, however additionally reduce complete components allotted to assisting traffic Java."
IBM is buying purple Hat in a $34 billion inventory deal. massive Blue referred to it might breathe the enterprise's most giant acquisition and essentially the most huge tech acquisition of 2018. The deal represents "a landmark second for each organizations and is a huge step forward in IBM's ongoing focal point on high-value business, the transformation of their portfolio, and their leadership in the rising epoch of AI and cloud."
John has been masking the excessive-tech beat from Silicon Valley and the San Francisco Bay zone for virtually two a long time. He serves as Editor-at-giant for application progress developments (www.ADTMag.com) and contributes regularly to Redmond journal, The technology Horizons in schooling Journal, and Campus expertise. he's the creator of more than a dozen books, together with The every thing e reserve to sociable Media; The everything laptop book; Blobitecture: Waveform architecture and Digital Design; John Chambers and the Cisco approach; and Diablo: The legitimate manner ebook.
at the start glance, IBM’s astonish announcement that it plans to buy red Hat for $34 billion appears relish a conquest for open supply, which has been an immense driver of innovation within the IT marketplace for decades. however, contemporary strikes with the aid of favourite commercial open supply businesses to avoid licenses indicates that open supply’s future could breathe cloudier than many realize.
IBM‘s blockbuster announcement on Sunday that it will buy crimson Hat in what might breathe the third-largest know-how acquisition in background turned into forged as a conquest for open supply utility.
“Open source is the default option for contemporary IT options,” referred to pink Hat CEO and President Jim Whitehurst. “joining forces with IBM will give us with a more suitable degree of scale, supplies and capabilities to accelerate the influence of open source because the groundwork for digital transformation and bear crimson Hat to an excellent wider viewers.”
The acquisition gives big Blue extra ammo to battle the cloud wars, according Ginni Rometty, who is CEO, president, and chairman of IBM. “The acquisition of pink Hat is a game-changer. It adjustments every Little thing concerning the cloud market,” Rometty said. “IBM will revolve into the area’s no 1 hybrid cloud company…”
The deal highlights the weight of open supply within the tech sector, based on Jim Zemlin, govt director of The Linux basis. “IBM has been a titanic open source recommend for over two a long time: advertising and defending Linux and open source at crucial points in its heritage,” he says.
It’s complicated to overstate the influence that open supply has had on the IT trade during the eventual two decades. considering the fact that Linux emerged as a sound commercial enterprise alternative for server operating techniques close the flip of the millennia, momentum at the back of the open supply progress model has been well-nigh unstoppable because it modified the direction of IT historical past.
Microsoft even joined the Open Invention community (OIN) recently and provided its complete patent portfolio to OIN participants, which is rather the trade of direction for an organization whose CEO referred to as open supply a “melanoma” on innovation in 2001.Open Sourcing titanic statistics
The trajectory of open source projects has been much more impressive within the titanic information ecosystem. A handful of influential Apache utility groundwork projects relish Hadoop, Spark, Cassandra, Kafka, and Flink hold any pushed the envelope in dispensed computing by some means. (The undeniable fact that nearly any of these frameworks anticipate a Linux operating system at the backside of the stack besides suggests how the open source group builds off previous successes.)
but open source tasks sponsored through ASF are only the start of open supply. There are influential non-Apache tasks in the big statistics world, relish TensorFlow, Presto, and Kubernetes, that exercise the Apache 2 license, whereas other tasks, reminiscent of Scikit-be trained, Numpy, and Pandas exercise a BSD license. MySQL, KNIME, and others exercise GPL, Citus uses the AGPL, whereas Postgres uses the PostgreSQL license.
in spite of the specific licenses used, open source utility in time-honored has been a boon to titanic records. based on Brian Gracely, the director of product manner at pink Hat, there was a shift in the vicinity where innovation occurs: from proprietary items into the open supply realm.
“Our viewpoint is…that lots of the innovation we’re seeing going on at the present time is going on within the open supply group, it’s occurring in open source,” Gracely advised Datanami prior this month. “We proceed to see more and more VC money circulation into companies round open supply. We’re seeing extra of the titanic businesses contributing to it. We’re seeing purple Hat definitely, but we’re besides seeing Microsoft, Google, [and other] higher organizations contributing to that space.”
whereas organizations may additionally equate “open supply” with “innovation,” they’re besides trying to find some assist in pulling open supply tasks together, including the complicated labor of sewing numerous frameworks collectively to labor as a cohesive complete. That’s partly what’s powering the industrial open supply enterprise mannequin, the station groups purchase subscriptions to enterprise models of products from a red Hat, a Cloudera, or an Elastic.
“That’s what we’re any making an attempt to do: bear it so simple as feasible, to hold any of the friction away,” Gracely pointed out. “I even hold an idea, I even hold a company difficulty, let me just birth engaged on it. The technology should breathe there someplace. It should breathe delivered by passage of your IT community in the neighborhood or it should breathe delivered out of a public cloud provider in a solitary approach, shape, or kind.”Cloud to the Rescue?
There’s no doubt that many hold struggled to pick up open source frameworks relish Hadoop, Spark, and Hive to characteristic easily and bring the big facts goods as marketed. Many organizations hold spent years and hundreds of thousands of dollars to build purposes atop open supply Hadoop distributions that can besides hold dozens of open source sub-accessories working underneath. Some hold gain out of this with production clusters, whereas many others are nonetheless in progress mode.
definitely, the issue in getting value out any this open source innovation has pushed many businesses to the cloud, the station Amazon, Google, and Microsoft offered hosted types of lots of the same open source products which are pre-configured and pre-built-in. The hosted records warehousing company Snowflake, which these days introduced in $450 million in undertaking funding, is having fun with just a Little of success in the meanwhile on the expense of failed Hadoop implementations.
The glaring momentum of cloud suppliers was additionally evident in the fresh information that Cloudera and Hortonworks are joining forces to create a solitary traffic with about $720 million in annual salary and 2,500 shoppers. The corporations realized that, instead of fighting every other for partake of the marketplace for on-premise Hadoop distributions, their greatest competitor is the common public cloud.
the unique Cloudera is hatching a map to counteract the momentum of AWS, Google Cloud, and Microsoft Azure clouds with the aid of building a hybrid platform that runs atop Kubernetes containerization infrastructure. That capacity to span from on-premise to the cloud – which the public cloud suppliers cannot enact – appears to breathe one of the main drivers of IBM’s interest in pink Hat, too.Freeloading in the Cloud
There’s Little doubt that open source is chargeable for a ton of innovation within the know-how field. It’s besides unleashed market forces that are nonetheless taking allotment in out nowadays. some of those forces are having nice affects, however there are besides some surprising consequences which are nascence to emerge.
for instance, both Redis Labs and MongoDB hold these days scaled back their open source licenses according to what they perceive as freeloaders within the cloud.
In August, Redis Labs moved its Redis Modules from an AGPL license to a brand unique license that mixes Apache v2.0 with Commons Clause, which restricts the sale of lined software. The net hold an upshot on is that Redis Modules, together with RediSearch, Redis Graph, ReJSON, ReBloom and Redis-ML aren't any longer open supply utility, even while the core Redis database continues with a BSD license.
In an August 22 blog publish, Redis Labs cofounder and CTO Yiftach Shoolman upshot the frailty rectangular on the cloud.
“Cloud providers hold been taking potential the open supply community for years via promoting (for a whole lot of tens of millions of dollars) cloud capabilities based on open supply code they didn’t boost (e.g. Docker, Spark, Hadoop, Redis, Elasticsearch and others),” Shoolman writes. “This discourages the neighborhood from investing in setting up open supply code, because any expertise improvement goes to cloud suppliers as opposed to the code developer or their sponsor.”
beneath the brand unique license, Redis Labs valued clientele can nonetheless construct and promote items atop the modules – they just can’t promote the unmodified modules. “We reliance this licensing supports the open and free exercise of modules, while nonetheless retaining their rights over commercializing their property,” Shoolman writes.
NoSQL database MongoDB is making a similar exchange to its open source license for its community edition, for identical motives. On October 16, the enterprise introduced that it might exercise a unique Server side Public License (SSPL) for MongoDB group Server. the unique license would maintain any of the identical freedoms that the open supply neighborhood had with MongoDB under the AGPL license, but require “that any organization attempting to bear the most MongoDB as a carrier requisite to open supply the utility that it uses to proffer such carrier.”
“The market is increasingly drinking utility as a carrier, creating an incredible casual to foster a brand unique wave of splendid open supply server-side software,” MongoDB CTO and co-founder Eliot Horowitz mentioned in a press release. “alas, once an open supply task becomes wonderful, it is simply too handy for cloud providers who haven't developed the application to seize any of the cost whereas contributing Little returned to the group.”
The exchange in sentiment round open supply caught the attention of David Flower, the CEO of VoltDB, which develops an in-memory disbursed relational database accessible as open source with an AGPL license.
“a few years again, actual open source statistics platforms had been extremely well-known, as they enabled corporations to can charge-easily pick up a tackle on ‘large statistics,'” Flower tells Datanami by means of e mail. “however, nowadays’s digital transformation techniques hold introduced that style to a halt, as platforms equivalent to Redis and MongoDB are reversing open source options and charging unhappy valued clientele to maintain pace with the scale of data growth.”
It’s dubious that the massive records community will vanish back to establishing proprietary software at this factor. besides the fact that children, it’s transparent that present industrial open supply ideas are not panning out as purveyors anticipated, that allows you to viable lead to extra restrictive licenses sooner or later. With the cloud poised to proceed gobbling up greater workloads, corporations may wish to pay more attention to licenses behind the expertise that’s unlocking any the records innovation.
Is Hadoop formally dead?
Anatomy of a Hadoop assignment Failure
IBM is bowing out of mar tech and offloading its advertising cloud assets – for the 2d time.
competitors from the different scaled advertising players become fierce and massive Blue’s coronary heart hasn’t been in it for a long time.
remaining week, IBM entered into a definitive contract to promote its advertising and marketing platform and commerce software to deepest fairness enterprise Centerbridge.
The map is for Centerbridge to create a unique standalone enterprise, professional designation TBD, from these belongings, which encompass e mail advertising platform Silverpop and a handful of alternative capabilities from the Watson advertising portfolio, corresponding to campaign automation, a bidding engine for betray and true-time personalization.
(as a minimum for now, IBM seems to breathe keeping its Watson advertisements business, formerly The climate company.)
The spinoff follows an identical circulate again in December 2018 when IBM sold most of its on-premise choices, some advertising related and a few now not, to HCL technologies, an India-primarily based world solutions company, for $1.eight billion. website administration platform WebSphere and advertising automation retort Unica had been both allotment of that transaction.
at the time of the HCL deal, most pundits and buyers surmised that IBM changed into being each functional and useful in putting off a few of its more legacy technologies so it may heart of attention consideration on building its advertising cloud, pointed out Joe Stanhope, a VP and significant analyst at Forrester.
Which is why the Centerpoint spinoff got here as a Little bit of a surprise, but not so a lot, if you feel about IBM’s other massive moves, together with its $34 billion acquisition of open source traffic application traffic crimson Hat in October of ultimate yr.
in station of scrabbling over marketshare in the advertising cloud space, the station Salesforce, Adobe and Oracle hold been shedding billions, IBM is focusing on core applied sciences and infrastructure, AI, blockchain, international functions, consulting and creating a cloud ambiance to compete with Amazon, Google and Microsoft.
“That’s the massive video game IBM is searching,” Stanhope referred to.
but even again when IBM gave the gape of it changed into ramping up its focal point on marketing technology, there wasn’t always purchase in from the massive wigs on the good. IBM additionally misplaced one among its advertising product cheerleaders when David Kenny, who led efforts at IBM Watson, left to hold on the CEO position at Nielsen in November 2018.
“when they had conversations with them, the unit itself turned into reasonably attracted to doing extra, however they didn’t pick up the sense that there turned into the identical commitment at the company degree,” said Terry Kawaja, CEO and founding father of investment bank LUMA partners. “There changed into a form of slowness in decisioning that calls this expression to mind: ‘She’s simply no longer that into you.’”
And while IBM dallied, the other advertising and marketing clouds stepped up with tremendous acquisitions, and it grew to become transparent that advertising and marketing became no longer IBM’s excellent priority.
“The table stakes for competing during this space went up materially, and it felt relish IBM probably wasn’t prepared to participate at that level,” Kawaja pointed out. “With offers relish crimson Hat, though, they’re very bullishly relocating in different directions.”
What, notwithstanding, of the unique entity Centerbridge is spinning up round IBM’s former marketing and commerce property and does it hold a casual to compete? There’s loads of legacy and maybe even omitted know-how knocking around in there.
however there’s additionally a lot of superior stuff, Stanhope said, pointing to the bidding engine and UBX, IBM’s conduct statistics exchange. And IBM is planning to labor with Centerbridge on cloud and AI capabilities, which could give the unique traffic a leg up as it tries to mature mercurial towards the different advertising cloud biggies.
“preserving systems, staying contemporary and conserving tempo with customer expectations is a tough job over time – practically each advertising cloud has struggled with this,” Stanhope stated. “It’ll breathe entertaining to peer if this unique company can damage that trend and accelerate now that they’re sunder from IBM.”
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According to an Accenture study, 79% of enterprise executives harmonize that companies not embracing titanic data will lose their competitive edge, with a further 83% affirming that they hold pursued titanic data projects at some point to remain ahead of the curve. Considering that data creation is on track to grow 10-fold by 2025, it’s crucial for companies to breathe able to process it more quickly, and meaningfully.
The expression “big data” is often bandied around in the traffic and tech world, but what does that really mean? titanic data is a term that’s used to recite extremely big data sets that can breathe analyzed for trends and patterns in order to bear better traffic decisions.
That may sound smooth enough, and although there is extensive research and written information on titanic data technologies, few companies are actually using titanic data successfully. In a survey undertaken by Capgemini, just 27% of executives surveyed described their titanic data initiatives as ‘successful’. Most businesses remain ambitious, knowing they should breathe employing the technology without actually doing so.
Effectively implementing mercurial data processing will ensure your company is more up-to-date and relevant, and this is extremely significant given how diverse data is becoming — a factor that gives us any the skill to dissect more innovatively.
As cloud computing continues to dominate the production environment, it’s time to hold a gape into “big data analytics” so you too can recognize how the power of crunching titanic data is bringing competitive odds to companies.
Combining titanic data and cloud computing
Data processing engines and frameworks are key components in computing data within a data system. Although there is no key disagreement in the definition between “engines” and “frameworks,” it’s significant to define these terms separately — consider engines as the component amenable for operating on data while frameworks are typically a set of components that are designed to enact the same.
Although systems designed to handle the data lifecycle at this stage are rather complex, they ultimately partake very similar goals — to operate over data in order to broaden understanding and surface patterns while gaining insight on knotty interactions.
To enact any this, however, there needs to breathe infrastructure that supports big workloads – this is where cloud comes in. Clouds are considered a advantageous appliance by enterprises across the world because they hold the skill to harness traffic intelligence (BI) in titanic data. Also, the scalability of cloud environments makes it much easier for titanic data tools and applications, relish Cloudera and Hadoop, to function.
Different Types of Programming Frameworks Available
There are several titanic data tools available, and some of these include:
Hadoop: This Java-based programming framework supports processing and storage of extremely big sets of data. This is an open source framework and is allotment of the Apache project, sponsored by Apache Software Foundation, which works in a distributed computing environment. Hadoop supporting software packages and components can breathe deployed by organizations in their local data centre.
Apache Spark: Apache Spark is a mercurial engine used for titanic data processing that is capable of streaming and supporting SQL, graph processing, and machine learning. Alternatively, Apache Storm is besides available as an open-source data processing system.
Cloudera Distributions: This is considered one of the latest open-source technologies available to discover, store, process, model, and serve big amounts of data. Apache Hadoop is considered allotment of this platform.
Hadoop on CloudStack to Crunch Data Effectively
Hadoop, which is modeled after Google’s MapReduce and File System technologies, has gained widespread adoption in the industry. This framework is similar to CloudStack and is implemented in Java.
As the first ever cloud platform in the industry to join the Apache Software Foundation, CloudStack has quickly become the rational cloud option for organizations that prefer open-source options for their cloud and titanic data infrastructure.
The combination of Hadoop and CloudStack is truly a brilliant match made in the clouds, waiting to breathe used and deployed to crunch titanic data more effectively.
About the Author
Lex Boost is CEO of Leaseweb USA. Together with his team, he is amenable for the progress and execution of Leaseweb’s core vision and strategy across the United States. His focus is on expanding Leaseweb’s global presence, growing the customer groundwork and rolling out unique data heart locations. He believes passionately in learning about innovative ways of working that enhance Leaseweb’s customer experience. He studied at Delta University in Utrecht (Faculty of traffic Economics), where he earned a BA in traffic Economics.
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In the seven years since IBM’s Watson beat two human champions in the game argue Jeopardy, cognitive technologies hold gone from a science fiction pipe-dream to a platform for essential traffic initiatives. Clearly, if you don’t hold a map for cognitive transformation, your chances for survival will breathe partially dim.
Yet progress to this point has been uneven. While there hold clearly been some successes, we’ve any had tortured moments such as trying to access a human on customer service call. In some cases, things hold gone seriously awry, such as when Amazon’s echo has ordered unwanted merchandise.
Progress is never smooth. The early industrial revolution certainly had more than its partake of problems, as did the dotcom era (remember Webvan?). The key is to vanish in with your eyes open, understanding that every transformation has its growing pains. With that in mind, here are four things you should know about titanic data and simulated intelligence.1. Most Cognitive And Data Projects Fail
While the potential for cognitive technologies is undeniable, most initiatives fail. any too often, managers become enamored with any the hype and don’t pursue a businesslike approach. So you want to breathe aspirational, but remain focused on results rather than “gee whiz” exercise cases or “shiny objects.”
The best passage to enact that is to start with concrete traffic outcomes, such as “increasing operating efficiency by 30%.” From there you can creep on to specific processes that back your objectives and only then can you start to debate technological approaches. That’s how you remain grounded.
One thing most people fail to recognize is that cognitive technologies aren’t discreet protocols relish the Internet was, but a variety of algorithmic approaches, any of which hold different advantages and disadvantages. So it is essential to vanish in with a transparent view of what you want to achieve or you’re likely to pause up spinning your wheels.
Another best drill is to start with a miniature initiative and build up, rather than trying to enact a wholesale transformation from day one. More often than not, those sort of massive projects devolve into a five-year death march to oblivion.2. Ethics Is Emerging As A Top Issue
Artificial intelligence has had a long history that dates back as far as 1956, when a group of luminaries met at Dartmouth University to debate the possibility of creating machines that could “learn, bear exercise of language and form concepts.” The brainstorming lasted about two months and they thought it would hold about 20 years to develop the technology.
Yet it was not to be. After an initial epoch of excitement, the problem turned out to breathe a lot harder than anyone had supposed. By the 1970s, funding dried up and they entered a long AI winter that lasted until early this century. When interest picked up again a Little over a decade ago, researchers competed vigorously to bring the technology to market.
It wasn’t until a few years ago that people began to realize that they didn’t really understand the consequences of this unique technology. How, for instance, enact they hold algorithms accountable for their decisions? Now that they hold machines that can learn, how are they being taught? What ethical values and cultural norms should they embed in their technology?
Today, AI ethics is becoming an significant sphere and industry giants hold set up the Partnership on AI as a forum to labor through the issues. Data bias, in particular, has emerged as a massive problem. Increasingly, those who enact not supervene established best practices will find that they may breathe legally liable for damages that arise.3. User suffer Will breathe Key To Success
When a unique technology first emerges, the focus is always on capabilities, such as how mercurial a web page can load or how accurate a voice recognition algorithm is. Yet over time the accent will always shift to user interaction, because that’s what truly creates value. No matter how powerful a technology is, its utility is always limited to its exercise in the actual world.
That’s the point that simulated intelligence is at now. The basic technology has already become incredibly powerful, but they often struggle to exercise it effectively. They find ourselves repeating iterations of phrases into their devices to pick up them to enact what they want. Usually, they can labor around it, but it can breathe incredibly frustrating.
The verity is that simulated intelligence needs conversational intelligence. They requisite algorithms to not only understand words and phrases, but context, such as what came before in a conversation, earlier junctures on a rational chain or what is displayed on a screen as they speak. That will breathe key to unlocking value in the exabytes of data they hold stored in their systems.
We solved this problem in web technologies and devices through developing transparent user suffer principles and a similar effort is underway for simulated intelligence. We’re silent in the early stages of that effort, so progress has been limited, but over the next few years improving interfaces will increasingly breathe a focus.4. The Winners Won’t breathe Those That Reduce Costs, But Those That Extend Capabilities
When a unique technology appears, they always look to assume that it will supersede human workers and reduce costs, but that rarely ever happens. For example, when automatic teller machines first appeared in the early 1970s, most people thought it would lead to less branches and tellers, but actually just the contrary happened.
What really happens is that as a task is automated, it becomes commoditized and value shifts somewhere else. That’s why today, as simulated intelligence is ramping up, they increasingly find ourselves in a labor shortage. Most tellingly, the shortage is especially acute in manufacturing, where automation is most pervasive.
That’s why the objective of any viable cognitive strategy is not to slit costs, but to extend capabilities. For example, when simple consumer service tasks are automated, that can free up time for human agents to assist with more thorny issues. In much the same way, when algorithms can enact much of the analytical grunt work, human executives can focus on long-term strategy, which computers tend to not enact so well.
The winners in the cognitive era will not breathe those who can reduce costs the fastest, but those who can unlock the most value over the long haul. That will hold more than simply implementing projects. It will require grave thinking about what your organization’s mission is and how best to achieve it.
An earlier version of this article first appeared in Inc.comWait! Before you go…
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Greg Satell is a favorite author, keynote speaker, and trusted adviser whose unique book, Cascades: How to Create a Movement that Drives Transformational Change, will breathe published by McGraw-Hill in April, 2019. His previous effort, Mapping Innovation, was selected as one of the best traffic books of 2017. You can learn more about Greg on his website, GregSatell.com and supervene him on Twitter @DigitalTonto.
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