Edward Jackson

Edward Jackson

1560952713

Difference Between Stack and Queue Data Structures in JavaScript

Queues and stacks are two common data structures leveraged on technical interviews. Due to the fact that they’re quite similar in structure, they can be a bit confusing to differentiate. So today we’ll build a stack and a queue in JavaScript.

Stacks

Stacks are data structures that follow the “last-in-first-out” or “LIFO” paradigm. We can think of them like a stack of books. In order to retrieve the third book in the stack, we have to take the fifth book off first, then the fourth book, until we retrieve the third book.

JavaScript doesn’t provide a native stack data structure, so we have to build our own with an array and a closure or a class.

 

Benefits

Stacks allow for constant-time adding and removing of an item. This is due to the fact that we don’t need to shift items around to add and remove them from the stack.

Constraints

Stacks, unfortunately, don’t offer constant-time access to the nth item in the stack, unlike an array. This means it can possible take O(n) where n is the number of elements in the stack, time to retrieve an item.

Methods

Stacks leverage the following methods:

  • pop(): Remove the top item from the stack
  • push(item): Add an item to the top of the stack
  • peek(): Return the item at the top of the stack
  • isEmpty(): Returns true if the stack is empty

Let’s Build

Let’s build a BookStack which will contain a stack of our favorite novels. What’s great about stacks is that the push and pop methods are the same name as the corresponding array methods we’ll use.

Constructor

We’ll define a class BookStack and give it a constructor method that has one property:

  • this.stack = [];
constructor() {
  this.stack = [];
}

Get

I’ll be adding a getter which returns the length of the stack. We’ll use this throughout our other methods.

get length() {
  return this.stack.length;
}

Push

We want to add the item to the end of the array, so we can use the array.push() method. The array.push() method returns the new length array.

push(item) {
  return this.stack.push(item);
}

Pop

We want to remove the last item in the array, so we can use the array.pop() method. The array.pop() method returns the item which was added, or undefined if the array is now empty.

pop() {
  return this.stack.pop();
}

Peek

We want to return, or peek at, the last item in the stack. Thus we just need to access the value at the last index.

peek() {
  return this.stack[this.length - 1];
}

isEmpty

We want to return true if there are no items in the stack. So if the length is zero, return true.

isEmpty() {
  return this.length === 0;
}

Putting It All Together

Our final BookStack code looks like this:

 

You can also create this with a closure.

 

Let’s test it out with some book data.

 

You can view the CodePen here.

Queues

A queue is similar to a stack in structure and methods, however the paradigm is different. Queues use the “first-in-first-out” or “FIFO” method. This can be thought of like a queue, or line, of people waiting to buy movie tickets.

The person who’s been waiting the longest in line gets served before the person who just joined.

Use Cases

Queues are very similar to linked lists and are typically used in breadth-first searches or when implementing a cache.

Constraints

Queues are much harder to update when adding and removing nodes.

Methods

Queues leverage the following methods:

  • enqueue(item): Remove the top item from the queue
  • dequeue(): Add an item to the top of the queue
  • peek(): Return the item at the top of the queue
  • isEmpty(): Returns true if the queue is empty

Let’s Build

For this example, we’ll be using JavaScript classes. Please refer to the stack section if you’d like to see the function closure in action.

Constructor

We’ll define a class MovieQueue and give it a constructor method that has one property:

  • this.queue = [];
constructor() {
  this.queue = [];
}

Get

I’ll be adding a getter which returns the length of the queue. We’ll use this throughout our other methods.

get length() {
  return this.queue.length;
}

Enqueue

We want to add an item to the first index in an array (the back of the queue). So let’s use the array.unshift() method.

enqueue(item) {
  return queue.unshift(item);
}

Dequeue

We want to remove the first item in the queue, or the last item in the array. We can simply use the array.pop() method to do this.

dequeue() {
  return queue.pop();
}

Peek

We want to see what the first item in the queue is. Remember this is the last item in the array. We’ll use queue[this.length — 1] to grab this value.

peek() {
  return queue[this.length - 1];
}

isEmpty

We want to return true if the queue is empty. We can use the length method to grab this information.

isEmpty() {
  return this.length === 0;
}

Putting It All Together

Our final MovieQueue code looks like this:

 

Let’s test it out with some names.

 

You can view the CodePen here.

 

I hope this tutorial gave you a better view on the differences between queues and stacks!

#javascript #datastructures

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Difference Between Stack and Queue Data Structures in JavaScript
 iOS App Dev

iOS App Dev

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink

1624272463

How Are Data analysis and Data science Different From Each Other

With possibly everything that one can think of which revolves around data, the need for people who can transform data into a manner that helps in making the best of the available data is at its peak. This brings our attention to two major aspects of data – data science and data analysis. Many tend to get confused between the two and often misuse one in place of the other. In reality, they are different from each other in a couple of aspects. Read on to find how data analysis and data science are different from each other.

Before jumping straight into the differences between the two, it is critical to understand the commonalities between data analysis and data science. First things first – both these areas revolve primarily around data. Next, the prime objective of both of them remains the same – to meet the business objective and aid in the decision-making ability. Also, both these fields demand the person be well acquainted with the business problems, market size, opportunities, risks and a rough idea of what could be the possible solutions.

Now, addressing the main topic of interest – how are data analysis and data science different from each other.

As far as data science is concerned, it is nothing but drawing actionable insights from raw data. Data science has most of the work done in these three areas –

  • Building/collecting data
  • Cleaning/filtering data
  • Organizing data

#big data #latest news #how are data analysis and data science different from each other #data science #data analysis #data analysis and data science different

Gerhard  Brink

Gerhard Brink

1620629020

Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.

Introduction

As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).


This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Cyrus  Kreiger

Cyrus Kreiger

1617959340

4 Tips To Become A Successful Entry-Level Data Analyst

Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.

If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?

Let’s take a look at the most important things you need to know.

#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company

Shawn  Durgan

Shawn Durgan

1620888000

Queue - Data Structures in JavaScript

Introduction to queue data structure in JavaScript. We will discuss a bunch of things:

  1. The definition
  2. Real world cases of queue-like interactions
  3. Main and additional queue methods
  4. Big O for insertion, deletion and search
  5. Two implementations in Javascript

#javascript #data-structures #queue