This video on Queue In Data Structure will acquaint you with all the basics of Queue data structure from scratch. In this introduction to queue with example video we will provide you with algorithms of queue operations to make you understand the flow of data. You will also understand the importance of queue data structure through its various applications. So, let’s begin!
The topics covered in this video on Queue in Data Structure are:
What is Queue in Data Structure?
Queue in data structure is an abstract data type that follows the FIFO principle for insertion and deletion. The item that enters the queue at first leaves the queue first, and the item that enters at last will definitely get removed at the end. While developing applications that work on the FCFS approach, it is widely implemented, for example, ready queue in time scheduling algorithms. The queue is also implemented in cases where applications don’t need a synchronous transfer of data. Thus, a queue is an important data structure.
What Is a Data Structure?
The short answer is: a data structure is a specific means of organizing data in a system to access and use. The long answer is a data structure is a blend of data organization, management, retrieval, and storage, brought together into one format that allows efficient access and modification. It’s collecting data values, the relationships they share, and the applicable functions or operations.
Why Is Data Structure Important?
The digital world processes an increasing amount of data every year. According to Forbes, there are 2.5 quintillion bytes of data generated daily. The world created over 90 percent of the existing data in 2018 in the previous two years! The Internet of Things (IoT) is responsible for a significant part of this data explosion. Data structures are necessary to manage the massive amounts of generated data and a critical factor in boosting algorithm efficiency. Finally, since nearly all software applications use data structures and algorithms, your education path needs to include learning data structure and algorithms if you want a career as a data scientist or programmer. Interviewers want qualified candidates who understand how to use data structures and algorithms, so the more you know about the concepts, the more comfortably and confidently you will answer data structure interview questions.
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.
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Welcome to the introduction to data structures tutorial. Have you ever used a DVD case to store multiple DVDs or even a simple register used to store data manually? Both of the above real-life examples are a form of data structures.
Here DVDs in a DVD case and records in the register are examples of data and their arrangement in a particular structure makes them more easily accessible. Combining a similar concept with the digital world gives us data structures in computers.
A set of data in mathematics might be unchanging but in computers, they can grow, shrink or change with the use of algorithms. These are dynamic sets.
A data structure is a specific way of arranging the data in a computer so that its usage is more effective and efficient.
Here are a few standard terms that we will be using frequently in our Data Structure’s learning journey:
1. Data: Data is the elementary value or we can also say that it is a collection of values. For example, employee name and employee ID are data about an employee.
2. Group Item: Data items having sub-data items are known as group items. For example, names. I can have the first name and surname of the employee.
3. Record: Record is the collection of various data items. For example in the case of employee data, the record might consist of name, address, designation, pay scale, and working hours.
4. File: A file is a collection of multiple records of the same entity. For example, a collection of 500 employee records is a file.
5. Entity: Entity is a class of certain objects. Each entity has various attributes.
6. Attribute: Each attribute represents a particular property of the entity.
7. Field: Field is an elementary unit of the information that represents the attribute of an entity.
There are 2 types of data structures:
A primitive data structure or data type is defined by a programming language and the type and size of the variables, values are specific to the language. It does not have any additional methods. For example int, float, double, long, etc. These data types can hold a single value.
These data structures are defined by the programmers and not by the programming languages. These data structures can hold multiple values and make them easily accessible.
Non-primitive data structures can further be classified into two types:
In the linear data structure, as the name suggests, data elements are arranged sequentially or linearly where each element is in connection with its previous and next element.
Since a single level is involved in a linear data structure, therefore, the whole data structure is traversable through all the elements in a single run only. These data structures are easy to implement. For example array, linked list, stack, and queue.
In a non-linear data structure, the elements’ arrangement is not sequential or linear. Instead, they are arranged hierarchically. Hence we cannot traverse through each element in one run.
Non-linear data structures are a little bit more difficult to implement than linear data structures but they use computer memory more efficiently compared to linear data structures. For example graphs and trees.
Data structures classification can also be done in the following two categories :
Static data structures have a specific memory size, the allocation of which is done at the time of compilation. Therefore, these data structures have fix memory size.
An array is the best example of static data structure.
Dynamic data structures have flexible memory sizes since the memory allocation is done at the run time. Hence, dynamic data structures can shrink or grow as and when required by deallocating or allocating the memory respectively.
For example, linked lists, stack, queue, graphs, and trees are dynamic data structures.
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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.
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SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.
Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:
1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.
2. Every database seller needs an approach to separate its item from others.
Right now, contrasts are noted where fitting.
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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.
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