1598508720

As programmers, we often find ourselves asking the same two questions over and over again:

*How much time does this algorithm need to complete?**How much space does this algorithm need for computing?*

To put it in other words, in computer programming, there are often multiple ways to solve a problem, so

*How do we know which solution is the right one?**How do we compare one algorithm against another?*

The big picture is that we are trying to compare how quickly the runtime of algorithms grows with respect to the size of their input. We think of the runtime of an algorithm as a function of the size of the input, where the output is how much work is required to run the algorithm.

To answer those questions, we come up with a concept called **Big O notation**.

- Big O describes how the time is taken, or memory is used, by a program scales with the amount of data it has to work on
**Big O notation**gives us an upper bound of the complexity in the worst case, helping us to quantify performance as the input size becomes arbitrarily large- In short,
**Big O notation**helps us to measure the scalability of our code

When talking about **Big O Notation** it’s important that we understand the concepts of time and space complexity, mainly because_ Big O Notation_ is a way to indicate complexities.

Complexity is an approximate measurement of how efficient (or how fast) an algorithm is and it’s associated with every algorithm we develop. This is something all developers have to be aware of. **There are 2 kinds of complexities: time complexity and space complexity.** Time and space complexities are approximations of how much time and space an algorithm will take to process certain inputs respectively.

Typically, there are three tiers to solve for (best case scenario, average-case scenario, and worst-case scenario) which are known as asymptotic notations. These notations allow us to answer questions such as: Does the algorithm suddenly become incredibly slow when the input size grows? Does it mostly maintain its fast run time performance as the input size increases?

#performance #development #big o complexity #big o notation #big data

1598508720

As programmers, we often find ourselves asking the same two questions over and over again:

*How much time does this algorithm need to complete?**How much space does this algorithm need for computing?*

To put it in other words, in computer programming, there are often multiple ways to solve a problem, so

*How do we know which solution is the right one?**How do we compare one algorithm against another?*

The big picture is that we are trying to compare how quickly the runtime of algorithms grows with respect to the size of their input. We think of the runtime of an algorithm as a function of the size of the input, where the output is how much work is required to run the algorithm.

To answer those questions, we come up with a concept called **Big O notation**.

- Big O describes how the time is taken, or memory is used, by a program scales with the amount of data it has to work on
**Big O notation**gives us an upper bound of the complexity in the worst case, helping us to quantify performance as the input size becomes arbitrarily large- In short,
**Big O notation**helps us to measure the scalability of our code

When talking about **Big O Notation** it’s important that we understand the concepts of time and space complexity, mainly because_ Big O Notation_ is a way to indicate complexities.

Complexity is an approximate measurement of how efficient (or how fast) an algorithm is and it’s associated with every algorithm we develop. This is something all developers have to be aware of. **There are 2 kinds of complexities: time complexity and space complexity.** Time and space complexities are approximations of how much time and space an algorithm will take to process certain inputs respectively.

Typically, there are three tiers to solve for (best case scenario, average-case scenario, and worst-case scenario) which are known as asymptotic notations. These notations allow us to answer questions such as: Does the algorithm suddenly become incredibly slow when the input size grows? Does it mostly maintain its fast run time performance as the input size increases?

#performance #development #big o complexity #big o notation #big data

1625640780

Hey guys, In this video, we’ll be talking about Time complexity and Big O notation. This is the first video of our DSA-One Course. We’ll also learn how to find the time complexity of Recursive problems.

Practice here: https://www.interviewbit.com/courses/programming/topics/time-complexity/

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Ignore these tags:

time complexity,time complexity of algorithms,time complexity analysis,complexity,time complexity tutorial,time and space complexity,time complexity explained,examples of time complexity,time,space complexity,time complexity in hindi,time complexity examples,analysis of time complexity,time complexity calculation,how to calculate time complexity,time and space complexity in hindi,time complexity of algorithms in hindi,what is time complexity in data structure,all time complexity

#big o #big o notation #time complexity

1601012358

Big Data has played a major role in defining the expansion of businesses of all kinds as it helps the companies to understand their audience and devise their business techniques in accordance with the requirement.

The importance of ‘Data’ has been spoken very highly in the modern-day business. Thus, while using big data analysis, the companies must keep away from these minor mistakes otherwise it could have a major impact on their performances. Big Data analysis can be the silver bullet that can answer your questions and help your business to scale newer heights.

**Read More:** Silly mistakes that can cost ‘Big’ in Big Data Analytics

#top big data analytics companies #best big data service providers #big data for business #big data technology #big data mistakes #big data analytics

1595315143

The rapid growth of technology has led to many people opting for online services, and thus the collection and maintenance of data becomes a significant factor for any company. Big data analytics service providers can help the companies get a massive edge over their competitors as they would manage the data well and allow the businesses to make better business decisions. It will provide you with a combination of increased customer experience, revenue, and reduced cost and thus will create a win-win situation for your business. Big data technologies will be your perfect ally in excelling in the cut-throat business environment and come out with flying colors.

Read More: Big Data can be The ‘Big’ boon for The Modern Age Businesses

#big data analytics service providers #top big data analytics companies #impact of big data on businesses #best big data consulting firms #big data #big data for businesses

1624614422

Traditional data processing application has limitations of its own in terms of processing the large chunk of complex data and this is where the big data processing application comes into play. Big data processing app can easily process complex and large information with their advanced capabilities.

**Want to develop a Big Data Processing Application?**

WebClues Infotech with its years of experience and serving 350+ clients since our inception is the agency to trust for the Big Data Processing Application development services. With a team that is skilled in the latest technologies, there can be no one better for fulfilling your development requirements.

**Want to know more about our Big Data Processing App development services?**

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