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- In contrast to batch ingestion, real-time ingestion involves the continuous collection and processing of data as it is generated, enabling immediate analysis and response. This method is integral to stream processing and event-driven architectures, where data is processed in small sizes or even on a per-event basis.
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Top results related to what is event ingestion in computer science definition
Top Answer
Answered Jun 13, 2022 · 0 votes
It's an analogy.
I think of staging data like an actors text on a theater stage. As soon as the actor (the ETL job) enters the stage, they need text (data) to play with. Putting data on stage is like giving an actor a new textbook. He knows how to read, interpret and play, but he doesn't know the text, yet. So providing the text ("staging" the data) is quite before the play (the process/job) actually begins, but can also be between the scenes. The picture might be a little odd, but I think you get the point.
- EXTRACT data -> put it onto stage
- TRANSFORM data -> let the actors play and create something new
- LOAD data -> deliver the experience
Actually, I doubt there's something like a precise definition for it, but technically, the staging area, also called landing zone, is the storage area between extracting and loading the data in an ETL process.
Generally this data is defined non-persistent; it's overwritten by or deleted before or after an ETL job. However, there are also cases in which staging data becomes metadata, parameters or comparison data for the next job run, depending on the ETL architecture. I prefer to keeping it non-persistent wherever it's possible.
In git, staging would be the "get on stage and be ready" (think of the theatre stage behind the closed curtain) and committing would be (again) the "delivery" to the audience.
1/5
Top Answer
Answered Feb 04, 2009 · 65 votes
Entropy can mean different things:
Computing
In computing, entropy is the randomness collected by an operating system or application for use in cryptography or other uses that require random data. This randomness is often collected from hardware sources, either pre-existing ones such as mouse movements or specially provided randomness generators.
Information theory
In information theory, entropy is a measure of the uncertainty associated with a random variable. The term by itself in this context usually refers to the Shannon entropy, which quantifies, in the sense of an expected value, the information contained in a message, usually in units such as bits. Equivalently, the Shannon entropy is a measure of the average information content one is missing when one does not know the value of the random variable
Entropy in data compression
Entropy in data compression may denote the randomness of the data that you are inputing to the compression algorithm. The more the entropy, the lesser the compression ratio. That means the more random the text is, the lesser you can compress it.
Shannon's entropy represents an absolute limit on the best possible lossless compression of any communication: treating messages to be encoded as a sequence of independent and identically-distributed random variables, Shannon's source coding theorem shows that, in the limit, the average length of the shortest possible representation to encode the messages in a given alphabet is their entropy divided by the logarithm of the number of symbols in the target alphabet.
Other Answers
Answered Mar 17, 2012 · 22 votes
My favorite definition, with a more practical focus, is found in Chapter 1 of the excellent book The Pragmatic Programmer: From Journeyman to Master by Andrew Hunt and David Thomas:
Software Entropy
While software development is immune from almost all physical laws, entropy hits us hard. Entropy is a term from physics that refers to the amount of "disorder" in a system. Unfortunately, the laws of thermodynamics guarantee that the entropy in the universe tends toward a maximum. When disorder increases in software, programmers call it "software rot."
There are many factors that can contribute to software rot. The most important one seems to be the psychology, or culture, at work on a project. Even if you are a team of one, your project's psychology can be a very delicate thing. Despite the best laid plans and the best people, a project can still experience ruin and decay during its lifetime. Yet there are other projects that, despite enormous difficulties and constant setbacks, successfully fight nature's tendency toward disorder and manage to come out pretty well.
...
...
A broken window.
One broken window, left unrepaired for any substantial length of time, instills in the inhabitants of the building a sense of abandonment—a sense that the powers that be don't care about the building. So another window gets broken. People start littering. Graffiti appears. Serious structural damage begins. In a relatively short space of time, the building becomes damaged beyond the owner's desire to fix it, and the sense of abandonment becomes reality.
The "Broken Window Theory" has inspired police departments in New York and other major cities to crack down on the small stuff in order to keep out the big stuff. It works: keeping on top of broken windows, graffiti, and other small infractions has reduced the serious crime level.
Tip 4
Don't Live with Broken Windows
Don't leave "broken windows" (bad designs, wrong decisions, or poor code) unrepaired. Fix each one as soon as it is discovered. If there is insufficient time to fix it properly, then board it up. Perhaps you can comment out the offending code, or display a "Not Implemented" message, or substitute dummy data instead. Take some action to prevent further damage and to show that you're on top of the situation.
Text taken from: http://pragprog.com/the-pragmatic-programmer/extracts/software-entropy
Other Answers
Answered Feb 04, 2009 · 12 votes
I always encountered entropy in the sense of Shannon Entropy.
From http://en.wikipedia.org/wiki/Information_entropy:
In information theory, entropy is a measure of the uncertainty associated with a random variable. The term by itself in this context usually refers to the Shannon entropy, which quantifies, in the sense of an expected value, the information contained in a message, usually in units such as bits. Equivalently, the Shannon entropy is a measure of the average information content one is missing when one does not know the value of the random variable.
2/5
Top Answer
Answered Nov 12, 2017 · 13 votes
Solution: Dedicated dispatcher for blocking operations One of the most efficient methods of isolating the blocking behaviour such that it does not impact the rest of the system is to prepare and use a dedicated dispatcher for all those blocking operations. This technique is often referred to as as “bulk-heading” or simply “isolating blocking”.
3/5
Top Answer
Answered Mar 07, 2015 · 2 votes
If I understand correctly, you're asking how many bits/bytes are used to represent a given number or character. I'll try to cover the common cases:
Integer (whole number) values
Since most systems use 8-bits per byte, integer numbers are usually represented as a multiple of 8 bits:
- 8 bits (1 byte) is typical for the C char datatype.
- 16 bits (2 bytes) is typical for int or short values.
- 32 bits (4 bytes) is typical for int or long values.
Each successive bit is used to represent a value twice the size of the previous one, so the first bit represents one, the second bit represents two, the third represents four, and so on. If a bit is set to 1, the value it represents is added to the "total" value of the number as a whole, so the 4-bit value 1101 (8, 4, 2, 1) is 8+4+1 = 13.
Note that the zeroes are still counted as bits, even for numbers such as 3, because they're still necessary to represent the number. For example:
- 00000011 represents a decimal value of 3 as an 8-bit binary number.
- 00000111 represents a decimal value of 7 as an 8-bit binary number.
The zero in the first number is used to distinguish it from the second, even if it's not "set" as 1.
An "unsigned" 8-bit variable can represent 2^8 (256) values, in the range 0 to 255 inclusive. "Signed" values (i.e. numbers which can be negative) are often described as using a single bit to indicate whether the value is positive (0) or negative (1), which would give an effective range of 2^7 (-127 to +127) either way, but since there's not much point in having two different ways to represent zero (-0 and +0), two's complement is commonly used to allow a slightly greater storage range: -128 to +127.
Decimal (fractional) values
Numbers such as 1.5 are usually represented as IEEE floating point values. A 32-bit IEEE floating point value uses 32 bits like a typical int value, but will use those bits differently. I'd suggest reading the Wikipedia article if you're interested in the technical details of how it works - I hope that you like mathematics.
Alternatively, non-integer numbers may be represented using a fixed point format; this was a fairly common occurrence in the early days of DOS gaming, before FPUs became a standard feature of desktop machines, and fixed point arithmetic is still used today in some situations, such as embedded systems.
Text
Simple ASCII or Latin-1 text is usually represented as a series of 8-bit bytes - in other words it's a series of integers, with each numeric value representing a single character code. For example, an 8-bit value of 00100000 (32) represents the ASCII space () character.
Alternative 8-bit encodings (such as JIS X 0201) map those 2^8 number values to different visible characters, whilst yet other encodings may use 16-bit or 32-bit values for each character instead.
Unicode character sets (such a the 8-bit UTF-8 or 16-bit UTF-16) are more complicated; a single UTF-16 character might be represented as a single 16-bit value or a pair of 16-bit values, whilst UTF-8 characters can be anywhere from one 8-bit byte to four 8-bit bytes!
Endian-ness
You should also be aware that values spanning more than a single 8-bit byte are typically byte-ordered in one of two ways: little endian, or big endian.
- Little Endian: A 16-bit value of 512 would be represented as 11111111 00000001 (i.e. smallest-value bits come first).
- Big Endian: A 16-bit value of 512 would be represented as 00000001 11111111 (i.e. largest-value bits come first).
You may also hear of mixed-endian, middle-endian, or bi-endian representations - see the Wikipedia article for further information.
4/5
Top Answer
Answered Dec 12, 2019 · 237 votes
NP stands for Non-deterministic Polynomial time.
This means that the problem can be solved in Polynomial time using a Non-deterministic Turing machine (like a regular Turing machine but also including a non-deterministic "choice" function). Basically, a solution has to be testable in poly time. If that's the case, and a known NP problem can be solved using the given problem with modified input (an NP problem can be reduced to the given problem) then the problem is NP complete.
The main thing to take away from an NP-complete problem is that it cannot be solved in polynomial time in any known way. NP-Hard/NP-Complete is a way of showing that certain classes of problems are not solvable in realistic time.
Edit: As others have noted, there are often approximate solutions for NP-Complete problems. In this case, the approximate solution usually gives an approximation bound using special notation which tells us how close the approximation is.
Other Answers
Answered Mar 17, 2012 · 22 votes
My favorite definition, with a more practical focus, is found in Chapter 1 of the excellent book The Pragmatic Programmer: From Journeyman to Master by Andrew Hunt and David Thomas:
Software Entropy
While software development is immune from almost all physical laws, entropy hits us hard. Entropy is a term from physics that refers to the amount of "disorder" in a system. Unfortunately, the laws of thermodynamics guarantee that the entropy in the universe tends toward a maximum. When disorder increases in software, programmers call it "software rot."
There are many factors that can contribute to software rot. The most important one seems to be the psychology, or culture, at work on a project. Even if you are a team of one, your project's psychology can be a very delicate thing. Despite the best laid plans and the best people, a project can still experience ruin and decay during its lifetime. Yet there are other projects that, despite enormous difficulties and constant setbacks, successfully fight nature's tendency toward disorder and manage to come out pretty well.
...
...
A broken window.
One broken window, left unrepaired for any substantial length of time, instills in the inhabitants of the building a sense of abandonment—a sense that the powers that be don't care about the building. So another window gets broken. People start littering. Graffiti appears. Serious structural damage begins. In a relatively short space of time, the building becomes damaged beyond the owner's desire to fix it, and the sense of abandonment becomes reality.
The "Broken Window Theory" has inspired police departments in New York and other major cities to crack down on the small stuff in order to keep out the big stuff. It works: keeping on top of broken windows, graffiti, and other small infractions has reduced the serious crime level.
Tip 4
Don't Live with Broken Windows
Don't leave "broken windows" (bad designs, wrong decisions, or poor code) unrepaired. Fix each one as soon as it is discovered. If there is insufficient time to fix it properly, then board it up. Perhaps you can comment out the offending code, or display a "Not Implemented" message, or substitute dummy data instead. Take some action to prevent further damage and to show that you're on top of the situation.
Text taken from: http://pragprog.com/the-pragmatic-programmer/extracts/software-entropy
Other Answers
Answered Feb 04, 2009 · 12 votes
I always encountered entropy in the sense of Shannon Entropy.
From http://en.wikipedia.org/wiki/Information_entropy:
In information theory, entropy is a measure of the uncertainty associated with a random variable. The term by itself in this context usually refers to the Shannon entropy, which quantifies, in the sense of an expected value, the information contained in a message, usually in units such as bits. Equivalently, the Shannon entropy is a measure of the average information content one is missing when one does not know the value of the random variable.
5/5
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