Biographical Information
Guy Harrison
Guy Harrison is an executive director of R&D at Dell and has more than 20 years of experience in database design, development, administration, and optimization. Harrison is an Oracle ACE, and is the author of the Oracle Performance Survival Guide (Prentice Hall, 2009) and MySQL Stored Procedure Programming (O'Reilly, with Steven Feuerstein), as well as other books, articles and presentations on database technology. He is the architect of Dell's Spotlight family of diagnostic products, and has led the development of Dell's Toad for Cloud Databases.
Harrison can be found on the internet at www.guyharrison.net, on email at guy_harrison@dell.com and is @guyharrisonon Twitter.
Articles By Guy Harrison
NoSQL - probably the hottest term in database technology today - was unheard of only a year ago. And yet, today, there are literally dozens of database systems described as "NoSQL." How did all of this happen so quickly? Although the term "NoSQL" is barely a year old, in reality, most of the databases described as NoSQL have been around a lot longer than the term itself. Many databases described as NoSQL arose over the past few years as reactions to strains placed on traditional relational databases by two other significant trends affecting our industry: big data and cloud computing.
In biology, we are taught that survival favors diversity. Organisms that reproduce without variation die out during periods of rapid change, while organisms that show variation in feature tend to survive and adapt. Likewise, ecosystems consisting of relatively few homogenous species thrive only when conditions stay static. Does IT diversity create a competitive advantage in the business application ecosystem? Predictably, large vendors with vertically integrated stacks argue that mixing software components is a Bad Thing. These vendors claim that reducing the diversity in the application stack leads to better efficiency and maintainability.
The relational database - or RDBMS - is a triumph of computer science. It has provided the data management layer for almost all major applications for more than two decades, and when you consider that the entire IT industry was once described as "data processing," this is a considerable achievement. For the first time in several decades, however, the relational database stranglehold on database management is loosening. The demands of big data and cloud computing have combined to create challenges that the RDBMS may be unable to adequately address.
The promises of public cloud computing - pay as you go, infinite scale and outsourced administration - are compelling. However, for most enterprises, security, geography and risk mitigation concerns make private cloud platforms more desirable. Enterprise customers like the idea of on-demand provisioning, but are often unwilling to take the performance, security and risk drawbacks of moving applications to remote hardware that is not under their direct control.
Although VMware continues to hold the majority share of the commercial virtualization market, other virtualization technologies are increasingly significant, though not necessarily as high profile. Operating system virtualization-sometimes called partial virtualization-allows an operating system such as Solaris to run multiple partitions, each of which appears to contain a distinct running instance of the same operating system. However, these technologies cannot be used to host different operating system versions, making them less appealing to enterprises seeking to consolidate workloads using virtualization.
Open source applications were somewhat niche at the beginning of the decade but now are clearly mainstream. Credible open source alternatives now exist for almost every category of application, as well as every component of the application.
In 1995, Netscape founder Marc Andreessen famously claimed that applications of the future would run within a web browser, relegating the role of the operating system - Windows, in particular - to "a poorly debugged set of device drivers." Fifteen years later, we can see that although rich applications such as Microsoft Office are still dominant, the web browser has become a platform that can deliver almost any conceivable type of business or consumer application.
Until recently, IT professionals have been conditioned to regard response time, or throughput, as the ultimate measure of application performance. It's as though we were building automobiles and only concerned with faster cars and bigger trucks. Yet, just as the automotive industry has come under increasing pressure to develop more fuel-efficient vehicles, so has the IT industry been challenged to reduce the power drain associated with today's data centers.
Spreadsheets, which have long been a disruptive force to enterprise IT, to some extent are the "killer" applications that helped drive the adoption of personal computers (PCs) in the enterprise. Spreadsheet products such as Lotus 1,2,3 - and early versions of Excel on the Mac - saw rapid adoption by business users. Inevitably, these users pushed the boundaries of the spreadsheet model, using spreadsheets as databases, and even to develop simple business applications. In the late 1980s, it was typical to see corporate IT rolling out massively expensive mainframe-based solutions, while departmental users got their real work done on spreadsheets running on cheap PCs.
In Greek mythology, Cassandra was granted the gift of prophesy, but cursed with an inability to convince others of her predictions - a sort of unbelievable "oracle," if you like. Ironically, in the database world, the Cassandra system is fast becoming one of the most credible non-relational databases for production use - a believable alternative to Oracle and other relational databases.
The rise of "big data" solutions - often involving the increasingly common Hadoop platform - together with the growing use of sophisticated analytics to drive business value - such as collective intelligence and predictive analytics - has led to a new category of IT professional: the data scientist.
The relational database is primarily oriented toward the modeling of objects (entities) and relationships. Generally, the relational model works best when there are a relatively small and static number of relationships between objects. It has long been a tricky problem in the RDBMS to work with dynamic, recursive or complex relationships. For instance, it's a fairly ordinary business requirement to print out all the parts that make up a product - including parts which, themselves, are made up of smaller parts. However, this "explosion of parts" is not consistently supported by all the relational databases. Oracle, SQL Server and DB2 have special, but inconsistent, syntax for these hierarchical queries, while MySQL and PostgreSQL lack specific support.
One of the funniest moments in the classic Star Trek motion pictures is the scene when the engineer "Scotty" - who has traveled back in time to the 1980s with his comrades - attempts to use a computer. "Computer!" he exclaims, attempting to initiate a dialogue with the PC. Embarrassed, a contemporary engineer hands him a mouse. "Aha," says Scotty who then holds the mouse to his mouth only to again exclaim, "Computer!" The idea that computers in the future would be able to understand human speech was common a few decades ago. Speech generation and recognition is so fundamental to the human experience that we tend to underestimate the incredible complexity of human information processing that makes it possible.
Both HBase and Cassandra can deal with large data sets, and provide high transaction rates and low latency lookups. Both allow map-reduce processing to be run against the database when aggregation or parallel processing is required. Why then, would a merge of Cassandra and Hadoop be a superior solution?
When computers first started to infringe on everyday life, science fiction authors and society in general had high expectations for "intelligent" systems. Isaac Asimov's "I, Robot" series from the 1940s portrayed robots with completely human intelligence and personality, and, in the 1968 movie "2001: A Space Odyssey," the onboard computer HAL (Heuristically programmed ALgorithmic computer) had a sufficiently human personality to suffer a paranoid break and attempt to murder the crew!
Because any database that does not support the SQL language is, by definition, a "NoSQL" database, some very different databases coexist under the NoSQL banner. Massively scalable data stores like Cassandra, Voldemort, and HBase sacrifice structure to achieve scale-out performance. However, the document-oriented NoSQL databases have very different architectures and objectives.
Oracle CEO Larry Ellison has been notoriously critical of cloud computing - or at least of the way in which the term "cloud" has been applied. He often has expressed his frustration when "cloud" is applied to long established patterns such as software as a service (SaaS), especially when this is done by Salesforce.com. While there's widespread agreement that "cloud" has become a faddish, over-hyped and often abused term, some have speculated that Ellison's obvious frustration has been fueled by Oracle's inability to fully engage in the cloud computing excitement prior to the conclusion of the Sun acquisition.
Salesforce.com is well known as the pioneer of software as a service (SaaS) - the provision of hosted applications across the internet. Salesforce launched its SaaS CRM (Customer Relationship Management) product more than 10 years ago, and today claims over 70,000 customers. It's less widely known that Salesforce.com also has been a pioneer in platform as a service (PaaS), and is one of the first to provide a comprehensive internet-based application development stack. In 2007 - way before the current buzz over cloud development platforms such as Microsoft Azure - Salesforce launched the Force.com platform, which allowed developers to run applications on the same multi-tenant architecture that hosts the Salesforce.com CRM.
The NoSQL acronym suggests it's the SQL language that is the key difference between traditional relational and newer non-relational data stores. However, an equally significant divergence is in the NoSQL consistency and transaction models. Indeed, some have suggested that NoSQL databases would be better described as "NoACID" databases - since they avoid the "ACID" transactions of the relational world.
few years ago, it seemed as though the days of the "micro-ISV"-very small Independent software vendors consisting of one or two developers-were over. The role once played by shareware windows applications had been supplanted by free web applications financed by advertising revenue. The start-up costs for such web applications-including funding a scalable and reliable web hosting infrastructure-were beyond the reach of most small software entrepreneurs.
In the classic comedy, "The Hitchhikers' Guide to the Galaxy," a frustrated Ford Prefect can't understand why a bunch of marketing consultants shipwrecked on prehistoric earth can't invent the wheel.
The business intelligence (BI) market is big: at least $10 billion in 2008 and much more if you include data warehousing projects. The tough economic environment may slow the growth of the BI market, but cost constraints, compliance and similar measures demanded by the current economy require accurate and timely business data, so BI is expected to remain a vigorous market segment regardless of the macro-economic situation.
Way back in 2003, Walmart announced that it would require Radio Frequency ID (RFID) tags—so-called "electronic barcodes"—to be attached to virtually all merchandise. Walmart pioneered the use of the printed bar code back in the 1970s, and many—myself included—became convinced that the company's directive would be the tipping point leading to universal adoption of RFID tabs in consumer goods and elsewhere.
MOORE'S law—first expressed by Intel cofounder Gordon Moore in 1965—predicts that computing power will increase exponentially, doubling roughly every 18 months. Moore's law has proved remarkably accurate and we have all benefited from the rapid growth in CPU and computer memory available for our desktop computers.
Non-relational cloud databases such as Google's BigTable, Amazon's SimpleDB and Microsoft'sSQL Server Data Services (SSDS) have emerged. But while these new data stores may well fill a niche in cloud-based applications, they lack most of the features demanded by enterprise applications - in particular, transactional support and business intelligence capabilities.
For the first time in over 20 years, there appear to be cracks forming in the relational model's dominance of the database management systems market. The relational database management system (RDBMS) of today is increasingly being seen as an obstacle to the IT architectures of tomorrow, and - for the first time - credible alternatives to the relational database are emerging. While it would be reckless to predict the demise of the relational database as a critical component of IT architectures, it is certainly feasible to imagine the relational database as just one of several choices for data storage in next-generation applications.
Both open source software ( OSS) and cloud computing continue to experience strong interest and growth despite the economic downturn. Clearly, both provide the promise of reduced operating and software licensing costs. For instance, corporations looking to reduce the cost incurred by Microsoft Office licensing are looking more closely at the open source OpenOffice alternative, or at Google's online application suite, Google Apps. There's understandable resistance to moving from the rich experience offered by Microsoft to these lower-cost alternatives, but resistance has a way of disappearing in the face of financial imperatives.
There's an old but clever internet parody describing the "Built-in Orderly Organized Knowledge device (BOOK)." This device is described as a "revolutionary breakthrough in technology" that is compact and portable, never crashes and supports both sequential and indexed information access. Though satirical, the article makes excellent points: the printed book is indeed an information technology device, arguably the oldest in widespread use today
The idea of "virtual" reality—immersive computer simulations almost indistinguishable from reality—has been a mainstay of modern "cyberpunk" science fiction since the early 1980s, popularized in movies such as The Thirteenth Floor and The Matrix. Typically, a virtual reality environment produces computer simulated sensory inputs which include at least sight and sound, and, perhaps, touch, taste and smell. These inputs are presented to the user through goggles, earphones and gloves or—in the true cyberpunk sci-fi—via direct brain interfaces.
Google's first "secret sauce" for web search was the innovative PageRank link analysis algorithm which successfully identifies the most relevant pages matching a search term. Google's superior search results were a huge factor in their early success. However, Google could never have achieved their current market dominance without an ability to reliably and quickly return those results. From the beginning, Google needed to handle volumes of data that exceeded the capabilities of existing commercial technologies. Instead, Google leveraged clusters of inexpensive commodity hardware, and created their own software frameworks to sift and index the data. Over time, these techniques evolved into the MapReduce algorithm. MapReduce allows data stored on a distributed file system - such as the Google File System (GFS) - to be processed in parallel by hundreds of thousands of inexpensive computers. Using MapReduce, Google is able to process more than a petabyte (one million GB) of new web data every hour.
When a company like Microsoft talks about the future of computing, you can expect a fair bit of self-serving market positioning - public software companies need to be careful to sell a vision of the future that doesn't jeopardize today's revenue streams. But, when a company like Microsoft releases a new version of its fundamental development framework - .NET, in this case - you can see more clearly the company's technical vision for the future of computing.
Google introduced the MapReduce algorithm to perform massively parallel processing of very large data sets using clusters of commodity hardware. MapReduce is a core Google technology and key to maintaining Google's website indexes.
Virtualization has changed the IT landscape more dramatically than perhaps any other technology introduced over the past decade. Virtualized environments are omnipresent in the modern data center due to their economic advantages in hardware consolidation and manageability.
Both open source software (OSS) and cloud computing continue to experience strong interest and growth despite the economic downturn. Clearly, both provide the promise of reduced operating and software licensing costs.
Attendees at the O'Reilly Velocity conference in June were treated to the unusual phenomenon of a joint presentation by Google and Microsoft. The presentation outlined the results of studies by the two companies on the effects of search response time. Aside from the novelty of Microsoft-Google cooperation, the presentation was notable both in terms of its conclusions and its methodology.
Predictive Analytics - sometimes referred to as Predictive Data Mining - is a branch of Business Intelligence that attempts to use historical data to make predictions about future events. At its simplest, predictive analytics utilizes statistical techniques, such as correlation and regression, which many of us have encountered in college or even high school. Correlation analysis determines if there is a statistically significant relationship between two variables. For instance, height and age are highly correlated, while IQ and height are very weakly correlated. Regression attempts to find an equation between the two or more variables, so that you can predict one from the other.
Michael Stonebraker is widely recognized as one of the pioneers of the relational database. While at Berkeley, he co-founded the INGRES project, which implemented the relational principles published by Edgar Codd in his seminal papers. The INGRES project became the basis for the commercial Ingres RDBMS, which, during the 1980s, provided some of the most significant competition to Oracle.
Google is the pioneer of big data. Technologies such as Google File System (GFS), BigTable and MapReduce formed the basis for open source Hadoop, which, more than any other technology, has brought big data within reach of the modern enterprise.
It's in the nature of hype bubbles to obscure important new paradigms behind a cloud of excitement and exaggerated claims. For example, the phrase "big data" has been so widely and poorly applied that the term has become almost meaningless. Nevertheless, beneath the hype of big data there is a real revolution in progress, and more than anything else it revolves around Apache Hadoop. Let's look at why Hadoop is creating such a stir in database management circles, and identify the obstacles that must be overcome before Hadoop can become part of mainstream enterprise architecture.
Five years ago, Radio Frequency ID (RFID) seemed posed to revolutionize commerce. Way back in 2003, Wal-Mart announced that it would be requiring that RFID tags - so called "electronic barcodes" - be attached to virtually all merchandise. Many- myself included - became convinced that the Wal-Mart directive would be the tipping point leading to universal adoption of RFID tabs in consumer goods and elsewhere.
Google is the pioneer of big data. Technologies such as Google File System (GFS), BigTable and MapReduce formed the basis for open source Hadoop, which, more than any other technology, has brought big data within reach of the modern enterprise.
Google is the pioneer of big data. Technologies such as Google File System (GFS), BigTable and MapReduce formed the basis for open source Hadoop, which, more than any other technology, has brought big data within reach of the modern enterprise.
Seriously chronic geeks like me usually were raised on a strong diet of science fiction that shaped our expectations of the future. Reading Heinlein and Asimov as a boy led me to expect flying cars and robot servants. Reading William Gibson and other "cyberpunk" authors as a young man led me to expect heads-up virtual reality glasses and neural interfaces. Flying cars and robot companions don't seem to be coming anytime soon, but we are definitely approaching a world in which virtual - or at least augmented - reality headsets and brain control interfaces become mainstream.
Seriously chronic geeks like me usually were raised on a strong diet of science fiction that shaped our expectations of the future. Reading Heinlein and Asimov as a boy led me to expect flying cars and robot servants. Reading William Gibson and other "cyberpunk" authors as a young man led me to expect heads-up virtual reality glasses and neural interfaces. Flying cars and robot companions don't seem to be coming anytime soon, but we are definitely approaching a world in which virtual - or at least augmented - reality headsets and brain control interfaces become mainstream.
The first computer program I ever wrote (in 1979, if you must know) was in the statistical package SPSS (Statistical Package for the Social Sciences), and the second computer platform I used was SAS (Statistical Analysis System). Both of these systems are still around today—SPSS was acquired by IBM as part of its BI portfolio, and SAS is now the world's largest privately held software company. The longevity of these platforms—they have essentially outlived almost all contemporary software packages—speaks to the perennial importance of data analysis to computing.
The first computer program I ever wrote (in 1979, if you must know) was in the statistical package SPSS (Statistical Package for the Social Sciences), and the second computer platform I used was SAS (Statistical Analysis System). Both of these systems are still around today—SPSS was acquired by IBM as part of its BI portfolio, and SAS is now the world's largest privately held software company. The longevity of these platforms—they have essentially outlived almost all contemporary software packages—speaks to the perennial importance of data analysis to computing.
Five years ago, Radio Frequency ID (RFID) seemed posed to revolutionize commerce. Way back in 2003, Wal-Mart announced that it would be requiring that RFID tags - so called "electronic barcodes" - be attached to virtually all merchandise. Many- myself included - became convinced that the Wal-Mart directive would be the tipping point leading to universal adoption of RFID tabs in consumer goods and elsewhere.
Google's dominance of internet search has been uncontested for more than 12 years now. Before Google, search engines such as AltaVista indexed web pages and allowed for keyword search with an interface and functionality superficially similar to that provided by Google. However, these first-generation search engines provided relatively poor ordering of results. Because an internet search would return pages ranked by the number of times a term appeared on the website, unpopular or irrelevant sites would be just as likely to achieve top rank as popular sites.
Hadoop is the most significant concrete technology behind the so called "Big Data" revolution. Hadoop combines an economical model for storing massive quantities of data - the Hadoop Distributed File System - with a flexible model for programming massively scalable programs - MapReduce. However, as powerful and flexible as MapReduce might be, it is hardly a productive programming model. Programming in MapReduce reminds one of programming in Assembly language - the simplest operations require substantial code.
The term "NoSQL" is widely acknowledged as an unfortunate and inaccurate tag for the non-relational databases that have emerged in the past five years. The databases that are associated with the NoSQL label have a wide variety of characteristics, but most reject the strict transactions and stringent relational model that are explicitly part of the relational design. The ACID (Atomic-Consistent-Independent-Durable) transactions of the relational model make it virtually impossible to scale across data centers while maintaining high availability, and the fixed schemas defined by the relational model are often inappropriate in today's world of unstructured and rapidly mutating data.
Google's dominance of internet search has been uncontested for more than 12 years now. Before Google, search engines such as AltaVista indexed web pages and allowed for keyword search with an interface and functionality superficially similar to that provided by Google. However, these first-generation search engines provided relatively poor ordering of results. Because an internet search would return pages ranked by the number of times a term appeared on the website, unpopular or irrelevant sites would be just as likely to achieve top rank as popular sites.
Hadoop is the most significant concrete technology behind the so called "Big Data" revolution. Hadoop combines an economical model for storing massive quantities of data - the Hadoop Distributed File System - with a flexible model for programming massively scalable programs - MapReduce. However, as powerful and flexible as MapReduce might be, it is hardly a productive programming model. Programming in MapReduce reminds one of programming in Assembly language - the simplest operations require substantial code.
As the undisputed pioneer of big data, Google established most of the key technologies underlying Hadoop and many of the NoSQL databases. The Google File System (GFS) allowed clusters of commodity servers to present their internal disk storage as a unified file system and inspired the Hadoop Distributed File System (HDFS). Google's column-oriented key value store BigTable influenced many NoSQL systems such as Apache HBase, Cassandra and HyperTable. And, of course, the Google Map-Reduce algorithm became the foundation computing model for Hadoop and was widely implemented in other NoSQL systems such as MongoDB.
As the undisputed pioneer of big data, Google established most of the key technologies underlying Hadoop and many of the NoSQL databases. The Google File System (GFS) allowed clusters of commodity servers to present their internal disk storage as a unified file system and inspired the Hadoop Distributed File System (HDFS). Google's column-oriented key value store BigTable influenced many NoSQL systems such as Apache HBase, Cassandra and HyperTable. And, of course, the Google Map-Reduce algorithm became the foundation computing model for Hadoop and was widely implemented in other NoSQL systems such as MongoDB.
Coverage of Windows 8 has understandably focused on the revolutionary Metro interface. Many believe that this new interface, while fine for tablets and phones, is a step backwards for desktop productivity. By forcing users to switch between two modes of operation - desktop and Metro, Windows 8 diminishes productivity and imposes steep learning curve on new users. The Metro interface itself supports only very limited multi-tasking, so, serious work often must be done in the traditional Windows desktop. Microsoft implicitly acknowledges these limitations by providing the latest version of Microsoft Office, not in Metro format, but as traditional "desktop" applications.
Coverage of Windows 8 has understandably focused on the revolutionary Metro interface. Many believe that this new interface, while fine for tablets and phones, is a step backwards for desktop productivity. By forcing users to switch between two modes of operation - desktop and Metro, Windows 8 diminishes productivity and imposes steep learning curve on new users. The Metro interface itself supports only very limited multi-tasking, so, serious work often must be done in the traditional Windows desktop. Microsoft implicitly acknowledges these limitations by providing the latest version of Microsoft Office, not in Metro format, but as traditional "desktop" applications.
In years to come, we might remember October 2011 as the month the big database vendors gave in to the dark side and embraced Hadoop. In October, both Microsoft and Oracle announced product offerings which included and embraced Hadoop as the enabler of their "big data" solution. The last of the big three database vendors - IBM - embraced Hadoop back in 2010.
Along with thousands of IT professionals, I was in the San Francisco Moscone Center main hall last October listening to Larry Ellison's 2011 Oracle Open world keynote. Larry can always be relied upon to give an entertaining presentation, a unique blend of both technology insights and amusingly disparaging remarks about competitors.
Knowing how your customers feel about your products is arguably as important as actual sales data but often much harder to determine. Traditionally, companies have used surveys, focus groups, customer visits, and similar active sampling techniques to perform this sort of market research. Opposition or lack of faith in market research takes a number of forms. Henry Ford once said, "If I had asked people what they wanted, they would have said faster horses," while Steve Jobs said, "People don't know what they want until you show it to them." The real problem with market research is more pragmatic: It's difficult and expensive to find out what people think.
Knowing how your customers feel about your products is arguably as important as actual sales data but often much harder to determine. Traditionally, companies have used surveys, focus groups, customer visits, and similar active sampling techniques to perform this sort of market research. Opposition or lack of faith in market research takes a number of forms. Henry Ford once said, "If I had asked people what they wanted, they would have said faster horses," while Steve Jobs said, "People don't know what they want until you show it to them." The real problem with market research is more pragmatic: It's difficult and expensive to find out what people think.
As the leading provider of relational database software, it's hardly surprising that Oracle initially gave little or no credence to the NoSQL movement that emerged in 2009. Indeed, an Oracle white paper from May 2011 concluded with the recommendation to "Go for the tried and true path," and avoid NoSQL databases.
One of the greatest achievements in artificial intelligence occurred earlier this year when IBM's Watson supercomputer defeated the two reigning human champions in the popular Jeopardy! TV show. Named after the IBM founder Thomas Watson and not - as you may have thought - Sherlock Holmes' famous assistant, Watson was the result of almost 5 years of intensive effort by IBM, and the intellectual successor to "Deep Blue," the first computer to beat a chess grand master.
The term "machine learning" evokes visions of massive super computers that eventually turn on and enslave humanity - think SkyNet from Terminator or HAL from 2001: A Space Odyssey. But the truth is that machine learning algorithms are common in web applications that we use every day and have a growing relevance to enterprise applications.
My 20-year-old daughter recently remarked that Facebook isn't as cool as it used to be. Sure, everyone has to be on Facebook, but that very ubiquity removes its mystique. The recently released Google+ is clearly targeted at Facebook and adds some features - particularly "Circles" - that are not available on Facebook. Facebook dominance may be indisputable today, but it is not guaranteed for all time. If I were Mark Zuckerberg, I would fear losing my cool status more than anything else.
My 20-year-old daughter recently remarked that Facebook isn't as cool as it used to be. Sure, everyone has to be on Facebook, but that very ubiquity removes its mystique. The recently released Google+ is clearly targeted at Facebook and adds some features - particularly "Circles" - that are not available on Facebook. Facebook dominance may be indisputable today, but it is not guaranteed for all time. If I were Mark Zuckerberg, I would fear losing my cool status more than anything else.
It's hard to overestimate Amazon's influence on cloud computing and on NoSQL databases. Amazon Web Services (AWS) was the first and still is the leading concrete example of an infrastructure as a service (IaaS) cloud - a collection of cloud-based services such as compute (EC2), storage (S3) and other application building blocks.
Throughout the 2000s, a huge number of website developers rejected the Enterprise Java or .NET platforms for web development in favor of the "LAMP" stack - Linux, Apache, MySQL and Perl/Python/PHP. Although the LAMP stack was arguably less scalable or powerful than the Java or .NET frameworks, it was typically easier to learn, faster in early stages of development - and definitely cheaper. When enterprise architects designed systems, they often chose commercial application servers and databases (Oracle, Microsoft, IBM). But, when web developers or startups faced these decisions, the LAMP stack was often the default choice.
Websites such as MySpace, Facebook, and LinkedIn have brought social networking and the concept of online community to a huge cross-section of our society. Penetration and usage of these platforms may vary depending on demographic (age and geography, in particular), but no one can debate the impact of Facebook and Twitter on both everyday life and on society in general.
Throughout the 2000s, a huge number of website developers rejected the Enterprise Java or .NET platforms for web development in favor of the "LAMP" stack - Linux, Apache, MySQL and Perl/Python/PHP. Although the LAMP stack was arguably less scalable or powerful than the Java or .NET frameworks, it was typically easier to learn, faster in early stages of development - and definitely cheaper. When enterprise architects designed systems, they often chose commercial application servers and databases (Oracle, Microsoft, IBM). But, when web developers or startups faced these decisions, the LAMP stack was often the default choice.
Websites such as MySpace, Facebook, and LinkedIn have brought social networking and the concept of online community to a huge cross-section of our society. Penetration and usage of these platforms may vary depending on demographic (age and geography, in particular), but no one can debate the impact of Facebook and Twitter on both everyday life and on society in general.
Seriously chronic geeks like me usually were raised on a strong diet of science fiction that shaped our expectations of the future. Reading Heinlein and Asimov as a boy led me to expect flying cars and robot servants. Reading William Gibson and other "cyberpunk" authors as a young man led me to expect heads-up virtual reality glasses and neural interfaces. Flying cars and robot companions don't seem to be coming anytime soon, but we are definitely approaching a world in which virtual - or at least augmented - reality headsets and brain control interfaces become mainstream.
Websites such as MySpace, Facebook, and LinkedIn have brought social networking and the concept of online community to a huge cross-section of our society. Penetration and usage of these platforms may vary depending on demographic (age and geography, in particular), but no one can debate the impact of Facebook and Twitter on both everyday life and on society in general.
It's hard to overestimate Amazon's influence on cloud computing and on NoSQL databases. Amazon Web Services (AWS) was the first and still is the leading concrete example of an infrastructure as a service (IaaS) cloud - a collection of cloud-based services such as compute (EC2), storage (S3) and other application building blocks.
One of the earliest of the new generation of non-relational databases was CouchDB. CouchDB was born in 2005 when former Lotus Notes developer Damien Katz foresaw the nonrelational wave that only fully arrived in 2009. Katz imagined a database that was fully compatible with web architectures — and more than a little influenced by Lotus Notes document database concepts.
Websites such as MySpace, Facebook, and LinkedIn have brought social networking and the concept of online community to a huge cross-section of our society. Penetration and usage of these platforms may vary depending on demographic (age and geography, in particular), but no one can debate the impact of Facebook and Twitter on both everyday life and on society in general.