Agnes  Sauer

Agnes Sauer


Understanding the Queue Data Structure and Its Implementations

queue is a collection of items whereby its operations work in a FIFO — First In First Out manner. The two primary operations associated with them are enqueue and dequeue.

This lesson was originally published at, where I maintain a technical interview course and write think-pieces for ambitious developers.

Lesson Objectives: At the end of this lesson, you will be able to:

  1. Know what the queue data structure is and appreciate it’s real-world use cases.
  2. Learn how queues work and their operations.
  3. Know and implement queues with two different approaches.

I’m sure all of us have been in queues before — perhaps at billing counters, shopping centers, or cafes. The first person in the line is usually serviced first, then the second, third, and so forth.

We have this concept in computer science as well. Take the example of a printer. Suppose we have a shared printer, and several jobs are to be printed at once. The printer maintains a printing “queue” internally, and prints the jobs in sequence based on which came first.

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Another instance where queues are extensively used is in the operating system of our machines. An OS maintains several queues such as a job queue, a ready queue, and a device queue for each of the processes. If you’re interested, refer to this link to know more about them.

I hope we’ve got a solid high-level understanding about what queues are. Let’s go ahead and understand how they work!

How do queues work?

Consider a pipe, perhaps a metal one in your bathroom or elsewhere in the house. Naturally, it has two open ends. Imagine that we have some elements in the pipe, and we’re trying to get them out. There will be one end through which we have inserted the elements, and there’s another end from which we’re getting them out. As seen in the figure below, this is precisely how the queue data structure is shaped.

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Unlike the stack data structure that we primarily think of with one “open end”, the queue has two open ends: the front and rear. They have different purposes — with the rear being the point of insertion and the front being that of removal. However, internally, the front and rear are treated as pointers. We’ll learn more about them in the subsequent sections programmatically.

Note that the element that got inside first is the initial one to be serviced, and removed from the queue. Hence the name: First In First Out (FIFO).

Queue operations and Implementation of queues

Similar to how a stack has push and pop operations, a queue also has two pairwise operations:

  1. Enqueue: To add elements
  2. Dequeue: To remove elements.

Let’s move on and cover each.

Click here to check out our lesson on the stack data structure!

1. Enqueue

The enqueue operation, as said earlier, adds elements to your queue from the rear end. Initially, when the queue is empty, both our front (sometimes called head) and rear (sometimes called tail) pointers are NULL.

#computer-science #technical-interview #algorithms #data-structures #programming-languages #algorithms

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Understanding the Queue Data Structure and Its Implementations
Siphiwe  Nair

Siphiwe Nair


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


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.


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


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

Cyrus  Kreiger

Cyrus Kreiger


How Has COVID-19 Impacted Data Science?

The COVID-19 pandemic disrupted supply chains and brought economies around the world to a standstill. In turn, businesses need access to accurate, timely data more than ever before. As a result, the demand for data analytics is skyrocketing as businesses try to navigate an uncertain future. However, the sudden surge in demand comes with its own set of challenges.

Here is how the COVID-19 pandemic is affecting the data industry and how enterprises can prepare for the data challenges to come in 2021 and beyond.

#big data #data #data analysis #data security #data integration #etl #data warehouse #data breach #elt

Macey  Kling

Macey Kling


Applications Of Data Science On 3D Imagery Data

CVDC 2020, the Computer Vision conference of the year, is scheduled for 13th and 14th of August to bring together the leading experts on Computer Vision from around the world. Organised by the Association of Data Scientists (ADaSCi), the premier global professional body of data science and machine learning professionals, it is a first-of-its-kind virtual conference on Computer Vision.

The second day of the conference started with quite an informative talk on the current pandemic situation. Speaking of talks, the second session “Application of Data Science Algorithms on 3D Imagery Data” was presented by Ramana M, who is the Principal Data Scientist in Analytics at Cyient Ltd.

Ramana talked about one of the most important assets of organisations, data and how the digital world is moving from using 2D data to 3D data for highly accurate information along with realistic user experiences.

The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment, 3D data for object detection and two general case studies, which are-

  • Industrial metrology for quality assurance.
  • 3d object detection and its volumetric analysis.

This talk discussed the recent advances in 3D data processing, feature extraction methods, object type detection, object segmentation, and object measurements in different body cross-sections. It also covered the 3D imagery concepts, the various algorithms for faster data processing on the GPU environment, and the application of deep learning techniques for object detection and segmentation.

#developers corner #3d data #3d data alignment #applications of data science on 3d imagery data #computer vision #cvdc 2020 #deep learning techniques for 3d data #mesh data #point cloud data #uav data