Database Engineering Quotes

She was designed to look human, her face the replica of a woman whose image Med’s tissue engineer had licensed from an old Facebook database.

generally speaking, no one should be directly logging in to a database.

At this risk of being redundant, everything must be checked in to code. This includes the following: DB object migrations Triggers Procedures and functions Views Configurations Sample datasets for functionality Data cleanup scripts

Different databases are designed to solve different problems. Using a single database engine for all of the requirements usually leads to non- performant solutions; storing transactional data, caching session information, traversing graph of customers and the products their friends bought are essentially different problems.

In the mid-1990s, a new employee of Sun Microsystems in California kept disappearing from their database. Every time his details were entered, the system seemed to eat him whole; he would disappear without a trace. No one in HR could work out why poor Steve Null was database kryptonite. The staff in HR were entering the surname as “Null,” but they were blissfully unaware that, in a database, NULL represents a lack of data, so Steve became a non-entry. To computers, his name was Steve Zero or Steve McDoesNotExist. Apparently, it took a while to work out what was going on, as HR would happily reenter his details each time the issue was raised, never stopping to consider why the database was routinely removing him.

2006 interview by Jim Gray, Amazon CTO Werner Vogels recalled another watershed moment: We went through a period of serious introspection and concluded that a service-oriented architecture would give us the level of isolation that would allow us to build many software components rapidly and independently. By the way, this was way before service-oriented was a buzzword. For us service orientation means encapsulating the data with the business logic that operates on the data, with the only access through a published service interface. No direct database access is allowed from outside the service, and there’s no data sharing among the services.3 That’s a lot to unpack for non–software engineers, but the basic idea is this: If multiple teams have direct access to a shared block of software code or some part of a database, they slow each other down. Whether they’re allowed to change the way the code works, change how the data are organized, or merely build something that uses the shared code or data, everybody is at risk if anybody makes a change. Managing that risk requires a lot of time spent in coordination. The solution is to encapsulate, that is, assign ownership of a given block of code or part of a database to one team. Anyone else who wants something from that walled-off area must make a well-documented service request via an API.

Radically better software robustness and productivity are to be had only by moving up a level, and making programs by the composition of modules, or objects. An especially promising trend is the use of mass-market packages as the platforms on which richer and more customized products are built. A truck-tracking system is built on a shrink-wrapped database and communications package; so is a student information system. The want ads in computer magazines offer hundreds of Hypercard stacks and customized templates for Excel, dozens of special functions in Pascal for MiniCad or functions in AutoLisp for AutoCad.

Here’s something you may not know: every time you go to Facebook or ESPN.com or wherever, you’re unleashing a mad scramble of money, data, and pixels that involves undersea fiber-optic cables, the world’s best database technologies, and everything that is known about you by greedy strangers. Every. Single. Time. The magic of how this happens is called “real-time bidding” (RTB) exchanges, and we’ll get into the technical details before long. For now, imagine that every time you go to CNN.com, it’s as though a new sell order for one share in your brain is transmitted to a stock exchange. Picture it: individual quanta of human attention sold, bit by bit, like so many million shares of General Motors stock, billions of times a day. Remember Spear, Leeds & Kellogg, Goldman Sachs’s old-school brokerage acquisition, and its disappearing (or disappeared) traders? The company went from hundreds of traders and two programmers to twenty programmers and two traders in a few years. That same process was just starting in the media world circa 2009, and is right now, in 2016, kicking into high gear. As part of that shift, one of the final paroxysms of wasted effort at Adchemy was taking place precisely in the RTB space. An engineer named Matthew McEachen, one of Adchemy’s best, and I built an RTB bidding engine that talked to Google’s huge ad exchange, the figurative New York Stock Exchange of media, and submitted bids and ads at speeds of upwards of one hundred thousand requests per second. We had been ordered to do so only to feed some bullshit line Murthy was laying on potential partners that we were a real-time ads-buying company. Like so much at Adchemy, that technology would be a throwaway, but the knowledge I gained there, from poring over Google’s RTB technical documentation and passing Google’s merciless integration tests with our code, would set me light-years ahead of the clueless product team at Facebook years later.

Symbolist machine learning is an offshoot of the knowledge engineering school of AI. In the 1970s, so-called knowledge-based systems scored some impressive successes, and in the 1980s they spread rapidly, but then they died out. The main reason they did was the infamous knowledge acquisition bottleneck: extracting knowledge from experts and encoding it as rules is just too difficult, labor-intensive, and failure-prone to be viable for most problems. Letting the computer automatically learn to, say, diagnose diseases by looking at databases of past patients’ symptoms and the corresponding outcomes turned out to be much easier than endlessly interviewing doctors. Suddenly, the work of pioneers like Ryszard Michalski, Tom Mitchell, and Ross Quinlan had a new relevance, and the field hasn’t stopped growing since. (Another important problem was that knowledge-based systems had trouble dealing with uncertainty, of which more in Chapter 6.)

Metadata is stored in a variety of retrieval tools (a collective term used to describe bibliographies, catalogs, indexes, finding aids, museum registers, bibliographic databases, search engines, and other tools that help users find information resources).

Cloud computing is the on-demand availability of computing resources—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet.

In a 2006 interview by Jim Gray, Amazon CTO Werner Vogels recalled another watershed moment: We went through a period of serious introspection and concluded that a service-oriented architecture would give us the level of isolation that would allow us to build many software components rapidly and independently. By the way, this was way before service-oriented was a buzzword. For us service orientation means encapsulating the data with the business logic that operates on the data, with the only access through a published service interface. No direct database access is allowed from outside the service, and there’s no data sharing among the services.3 That’s a lot to unpack for non–software engineers, but the basic idea is this: If multiple teams have direct access to a shared block of software code or some part of a database, they slow each other down. Whether they’re allowed to change the way the code works, change how the data are organized, or merely build something that uses the shared code or data, everybody is at risk if anybody makes a change. Managing that risk requires a lot of time spent in coordination. The solution is to encapsulate, that is, assign ownership of a given block of code or part of a database to one team. Anyone else who wants something from that walled-off area must make a well-documented service request via an API.

Eighteen years later, nearly all the people that Ellison brought into the engineering team at that time are still at Oracle and are still, despite middle age and huge wealth, involved in cutting-edge work on the latest versions of the Oracle database.

Lastly, for journalists, the challenge ahead is to be polymaths, able to study and work with audiences, manage active databases, curate other content, and work closely with digital engineers and designers to make it all sing. They will need to be more entrepreneurial in their work arrangements, allowing the collective agreements that once ensured jobs for life to morph into new deals that reward journalists for their output as well as their input, and allow their news organizations to move staff more quickly when the market changes.

This process uniquely identifies the impact of hidden causes, rather than merely exploring the factors that are readily apparent. RCA was created by Sakichi Toyoda, founder of Toyota Industries—who Forbes Magazine ranks as the 13th most influential businessman of all time and is often compared to Thomas Edison for his industry-redefining inventions. Toyoda developed a unique system to identify the (often inconspicuous) source of a problem, then implement solutions that prevent the problem from recurring. It was originally applied in the field of engineering, but has since been widely adopted in many industries. I became acquainted with this methodology as a strategy to find corporate solutions, back in my days as a Senior Database Architect. And upon first introduction, the psychologist in me instantly recognized its potential value in dealing with the cream cheese danish in my left hand. And the rest—was history.

Each business process is represented by a dimensional model that consists of a fact table containing the event's numeric measurements surrounded by a halo of dimension tables that contain the textual context that was true at the moment the event occurred. This characteristic star-like structure is often called a star join, a term dating back to the earliest days of relational databases. Figure 1.5 Fact and dimension tables in a dimensional model. The first thing to notice about the dimensional schema is its simplicity and symmetry. Obviously, business users benefit from the simplicity because the data is easier to understand and navigate. The charm of the design in Figure 1.5 is that it is highly recognizable to business users. We have observed literally hundreds of instances in which users immediately agree that the dimensional model is their business. Furthermore, the reduced number of tables and use of meaningful business descriptors make it easy to navigate and less likely that mistakes will occur. The simplicity of a dimensional model also has performance benefits. Database optimizers process these simple schemas with fewer joins more efficiently. A database engine can make strong assumptions about first constraining the heavily indexed dimension tables, and then attacking the fact table all at once with the Cartesian product of the dimension table keys satisfying the user's constraints. Amazingly, using this approach, the optimizer can evaluate arbitrary n-way joins to a fact table in a single pass through the fact table's index. Finally, dimensional models are gracefully extensible to accommodate change. The predictable framework of a dimensional model withstands unexpected changes in user behavior. Every dimension is equivalent; all dimensions are symmetrically-equal entry points into the fact table. The dimensional model has no built-in bias regarding expected query patterns. There are no preferences for the business questions asked this month versus the questions asked next month. You certainly don't want to adjust schemas if business users suggest new ways to analyze their business.

Dimensional models implemented in relational database management systems are referred to as star schemas because of their resemblance to a star-like structure. Dimensional models implemented in multidimensional database environments are referred to as online analytical processing (OLAP) cubes, as illustrated in Figure 1.1. Figure 1.1 Star schema versus OLAP cube. If your DW/BI environment includes either star schemas or OLAP cubes, it leverages dimensional concepts. Both stars and cubes have a common logical design with recognizable dimensions; however, the physical implementation differs. When data is loaded into an OLAP cube, it is stored and indexed using formats and techniques that are designed for dimensional data. Performance aggregations or precalculated summary tables are often created and managed by the OLAP cube engine. Consequently, cubes deliver superior query performance because of the precalculations, indexing strategies, and other optimizations. Business users can drill down or up by adding or removing attributes from their analyses with excellent performance without issuing new queries. OLAP cubes also provide more analytically robust functions that exceed those available with SQL. The downside is that you pay a load performance price for these capabilities, especially with large data sets.

Q: How many Java programmers does it take to change a lightbulb? A: One, to generate a “ChangeLightBulb” event to the socket. Q: How many C++ programmers does it take to change a lightbulb? A: You’re still thinking procedurally. A well-designed lightbulb object would inherit a change method from a generic lightbulb class. Q: How many Windows programmers does it take to change a lightbulb? A: Three. One to write WinGetLightBulbHandle, one to write WinQueryStatusLightBulb, and one to write WinGetLight-SwitchHandle. Q: How many database programmers does it take to change a lightbulb? A: Three. One to write the lightbulb removal program, one to write the lightbulb insertion program, and one to act as a lightbulb administrator to make sure nobody else tries to change the light-bulb at the same time. Q: How many software engineers does it take to change a lightbulb? A: None. That’s a hardware problem. Q: How many Microsoft engineers does it take to change a lightbulb? A: None. They will redefine darkness as an industry standard.

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Another recent study, this one on academic research, provides real-world evidence of the way the tools we use to sift information online influence our mental habits and frame our thinking. James Evans, a sociologist at the University of Chicago, assembled an enormous database on 34 million scholarly articles published in academic journals from 1945 through 2005. He analyzed the citations included in the articles to see if patterns of citation, and hence of research, have changed as journals have shifted from being printed on paper to being published online. Considering how much easier it is to search digital text than printed text, the common assumption has been that making journals available on the Net would significantly broaden the scope of scholarly research, leading to a much more diverse set of citations. But that’s not at all what Evans discovered. As more journals moved online, scholars actually cited fewer articles than they had before. And as old issues of printed journals were digitized and uploaded to the Web, scholars cited more recent articles with increasing frequency. A broadening of available information led, as Evans described it, to a “narrowing of science and scholarship.”31 In explaining the counterintuitive findings in a 2008 Science article, Evans noted that automated information-filtering tools, such as search engines, tend to serve as amplifiers of popularity, quickly establishing and then continually reinforcing a consensus about what information is important and what isn’t. The ease of following hyperlinks, moreover, leads online researchers to “bypass many of the marginally related articles that print researchers” would routinely skim as they flipped through the pages of a journal or a book. The quicker that scholars are able to “find prevailing opinion,” wrote Evans, the more likely they are “to follow it, leading to more citations referencing fewer articles.” Though much less efficient than searching the Web, old-fashioned library research probably served to widen scholars’ horizons: “By drawing researchers through unrelated articles, print browsing and perusal may have facilitated broader comparisons and led researchers into the past.”32 The easy way may not always be the best way, but the easy way is the way our computers and search engines encourage us to take.

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Johnny developed a list of what he calls “Google Dorks,” or a string that can be used to search in Google to find out information about a company. For example if you were to type in: site:microsoft.com filetype:pdf you be given a list of every file with the extension of PDF that is on the microsoft.com domain. Being familiar with search terms that can help you locate files on your target is a very important part of information gathering. I make a habit of searching for filetype:pdf, filetype:doc, filetype:xls, and filetype:txt. It is also a good idea to see if employees actually leave files like DAT, CFG, or other database or configuration files open on their servers to be harvested.

Note that there is not a focus on eliminating failures. Systems without failures, although robust, become brittle and fragile. When failures occur, it is more likely that the teams responding will be unprepared, and this could dramatically increase the impact of the incident.

I booted up my laptop and went into the FBI’s VICAP database. The Violent Criminal Apprehension Program was a national Web site with one purpose: to help law enforcement agents link up scattered bits of intel related to serial homicides. The site had a kick-ass search engine, and new information was always being plugged in by cops around the country

Web Application Development In this modern world of computer technology all people are using internet. In particular, to take advantage of this scenario the web provides a way for marketers to get to know the people visiting their sites and start communicating with them. One way of doing this is asking web visitors to subscribe to newsletters, to submit an application form when requesting information on products or provide details to customize their browsing experience when next visiting a particular website. In computing, a web application is a client–server software application in which the client runs in a web browser. HTML5 introduced explicit language support for making applications that are loaded as web pages, but can store data locally and continue to function while offline. Web Applications are dynamic web sites combined with server side programming which provide functionalities such as interacting with users, connecting to back-end databases, and generating results to browsers. Examples of Web Applications are Online Banking, Social Networking, Online Reservations, eCommerce / Shopping Cart Applications, Interactive Games, Online Training, Online Polls, Blogs, Online Forums, Content Management Systems, etc.. Applications are usually broken into logical chunks called “tiers”, where every tier is assigned a role. Traditional applications consist only of 1 tier, which resides on the client machine, but web applications lend themselves to an n-tiered approach by nature. Though many variations are possible, the most common structure is the three-tiered application. In its most common form, the three tiers are called presentation, application and storage, in this order. A web browser is the first tier (presentation), an engine using some dynamic Web content technology (such as ASP, CGI, ColdFusion, Dart, JSP/Java, Node.js, PHP, Python or Ruby on Rails) is the middle tier (application logic), and a database is the third tier (storage).The web browser sends requests to the middle tier, which services them by making queries and updates against the database and generates a user interface. Client Side Scripting / Coding – Client Side Scripting is the type of code that is executed or interpreted by browsers. Client Side Scripting is generally viewable by any visitor to a site (from the view menu click on “View Source” to view the source code). Below are some common Client Side Scripting technologies: HTML (HyperTextMarkup Language) CSS (Cascading Style Sheets) JavaScript Ajax (Asynchronous JavaScript and XML) jQuery (JavaScript Framework Library – commonly used in Ajax development) MooTools (JavaScript Framework Library – commonly used in Ajax development) Dojo Toolkit (JavaScript Framework Library – commonly used in Ajax development) Server Side Scripting / Coding – Server Side Scripting is the type of code that is executed or interpreted by the web server. Server Side Scripting is not viewable or accessible by any visitor or general public. Below are the common Server Side Scripting technologies: PHP (very common Server Side Scripting language – Linux / Unix based Open Source – free redistribution, usually combines with MySQL database) Zend Framework (PHP’s Object Oriented Web Application Framework) ASP (Microsoft Web Server (IIS) Scripting language) ASP.NET (Microsoft’s Web Application Framework – successor of ASP) ColdFusion (Adobe’s Web Application Framework) Ruby on Rails (Ruby programming’s Web Application Framework – free redistribution) Perl (general purpose high-level programming language and Server Side Scripting Language – free redistribution – lost its popularity to PHP) Python (general purpose high-level programming language and Server Side Scripting language – free redistribution). We also provide Training in various Computer Languages. TRIRID provide quality Web Application Development Services. Call us @ 8980010210

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ACL - Accelerated Contact Linguistics - was, Scile told me, a speciality crossbred from pedagogics, receptivity, programming and cryptography. It was used by the scholar-explorers of Bremen's pioneer ships to effect very fast communication with indigenes they encountered or which encountered them. In the logs of those early journeys, the excitement of the ACLers is moving. On continents, on worlds vivid and drab, they record first moments of understanding with menageries of exots. Tactile languages, bioluminescent words, all varieties of sounds that organisms can make. Dialects comprehensible only as palimpsests of references to everything already said, or in which adjectives are rude and verbs unholy. I've seen the trid diary of an ACLer barricaded in his cabin, whose vessel has been boarded by what we didn't then know as Corscans - it was first contact. He's afraid, as he should be, of the huge things battering at his door, but he's recording his excitement at having just understood the tonal structures of their speech. When the ACLers and the crews came to Arieka, there started more than 250 kilohours of bewilderment. It wasn't that the Host language is particularly difficult to understand, or changeable, or excessively various. There were startlingly few Hosts or Arieka, scattered around the one city, and all spoke the same language. With the linguists' earware and drives it wasn't hard to amass a database of sound-words (the newcomers thought of them as words, though where they divided one from the next the Ariekei might not recognize fissures). The scholars made pretty quick sense of syntax. Like all exot languages it had its share of astonishments. But there was nothing so alien that trumped the ACLers or their machines. The Hosts were patient, seemed intrigued by and, insofar as anyone could tell through their polite opacity, welcoming to their guests. They had no access to immer, nor exotic drives or even sublux engines; they never left their atmosphere, but they were otherwise advanced. They manipulated life with astounding finesse, and they seemed unsurprised that there was sentience elsewhere. The Hosts did not learn out Anglo-Ubiq. Did not seem to try. But within a few thousand hours, Terre linguists could understand much of what the Hosts said, and synthesised responses and questions in the one Ariekene language. The phonetic structure of the sentences they had their machines speak - the tonal shifts, the vowels and the rhythm of consonants - were precise, accurate to the very limits of testing. The Hosts listened, and did not understand a single sound.

With the MAC address of a wireless access point, we can now also print out the physical location of the access point as well. Quite a few databases, both open-source and proprietary, contain enormous listings of wireless access points correlated to their physical locations.

As opposed to a database that maintains a client/ server relationship, SQLite stores the entire database as a single flat file on the host.

He murdered one of us.” Chapter 11 I SPENT THE day working both cases. I’d ransacked the missing-persons databases for a match to our long-haired Jane Doe. After that, Brady and I checked names of cops who had access to the property-room floor and compared those cops’ time sheets with the times drug dealers had been killed with one of our vouchered-and-stolen .22s. The list of cops was very long and Brady was still working on the project when I left him. I got back to the Ellsworth compound as the sun was setting, flying pink flags over the bay. TV satellite vans were double-parked along Vallejo, their engines running and their lights