Trending November 2023 # Etl (Extract, Transform, And Load) Process In Data Warehouse # Suggested December 2023 # Top 17 Popular

You are reading the article Etl (Extract, Transform, And Load) Process In Data Warehouse updated in November 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested December 2023 Etl (Extract, Transform, And Load) Process In Data Warehouse

What is ETL?

It’s tempting to think a creating a Data warehouse is simply extracting data from multiple sources and loading into database of a Data warehouse. This is far from the truth and requires a complex ETL process. The ETL process requires active inputs from various stakeholders including developers, analysts, testers, top executives and is technically challenging.

In order to maintain its value as a tool for decision-makers, Data warehouse system needs to change with business changes. ETL is a recurring activity (daily, weekly, monthly) of a Data warehouse system and needs to be agile, automated, and well documented.

In this ETL tutorial, you will learn-

Why do you need ETL?

There are many reasons for adopting ETL in the organization:

It helps companies to analyze their business data for taking critical business decisions.

Transactional databases cannot answer complex business questions that can be answered by ETL example.

A Data Warehouse provides a common data repository

ETL provides a method of moving the data from various sources into a data warehouse.

As data sources change, the Data Warehouse will automatically update.

Well-designed and documented ETL system is almost essential to the success of a Data Warehouse project.

Allow verification of data transformation, aggregation and calculations rules.

ETL process allows sample data comparison between the source and the target system.

ETL process can perform complex transformations and requires the extra area to store the data.

ETL helps to Migrate data into a Data Warehouse. Convert to the various formats and types to adhere to one consistent system.

ETL is a predefined process for accessing and manipulating source data into the target database.

ETL in data warehouse offers deep historical context for the business.

It helps to improve productivity because it codifies and reuses without a need for technical skills.

ETL Process in Data Warehouses

ETL is a 3-step process

ETL Process

Step 1) Extraction

In this step of ETL architecture, data is extracted from the source system into the staging area. Transformations if any are done in staging area so that performance of source system in not degraded. Also, if corrupted data is copied directly from the source into Data warehouse database, rollback will be a challenge. Staging area gives an opportunity to validate extracted data before it moves into the Data warehouse.

Three Data Extraction methods:

Full Extraction

Partial Extraction- without update notification.

Partial Extraction- with update notification

Irrespective of the method used, extraction should not affect performance and response time of the source systems. These source systems are live production databases. Any slow down or locking could effect company’s bottom line.

Some validations are done during Extraction:

Reconcile records with the source data

Make sure that no spam/unwanted data loaded

Data type check

Remove all types of duplicate/fragmented data

Check whether all the keys are in place or not

Step 2) Transformation

Data extracted from source server is raw and not usable in its original form. Therefore it needs to be cleansed, mapped and transformed. In fact, this is the key step where ETL process adds value and changes data such that insightful BI reports can be generated.

It is one of the important ETL concepts where you apply a set of functions on extracted data. Data that does not require any transformation is called as direct move or pass through data.

In transformation step, you can perform customized operations on data. For instance, if the user wants sum-of-sales revenue which is not in the database. Or if the first name and the last name in a table is in different columns. It is possible to concatenate them before loading.

Data Integration Issues

Following are Data Integrity Problems:

Different spelling of the same person like Jon, John, etc.

There are multiple ways to denote company name like Google, Google Inc.

Use of different names like Cleaveland, Cleveland.

There may be a case that different account numbers are generated by various applications for the same customer.

In some data required files remains blank

Validations are done during this stage

Filtering – Select only certain columns to load

Using rules and lookup tables for Data standardization

Character Set Conversion and encoding handling

Conversion of Units of Measurements like Date Time Conversion, currency conversions, numerical conversions, etc.

Data threshold validation check. For example, age cannot be more than two digits.

Data flow validation from the staging area to the intermediate tables.

Required fields should not be left blank.

Cleaning ( for example, mapping NULL to 0 or Gender Male to “M” and Female to “F” etc.)

Split a column into multiples and merging multiple columns into a single column.

Transposing rows and columns,

Use lookups to merge data

Using any complex data validation (e.g., if the first two columns in a row are empty then it automatically reject the row from processing)

Step 3) Loading

Loading data into the target datawarehouse database is the last step of the ETL process. In a typical Data warehouse, huge volume of data needs to be loaded in a relatively short period (nights). Hence, load process should be optimized for performance.

Types of Loading:

Initial Load — populating all the Data Warehouse tables

Incremental Load — applying ongoing changes as when needed periodically.

Full Refresh —erasing the contents of one or more tables and reloading with fresh data.

Load verification

Ensure that the key field data is neither missing nor null.

Test modeling views based on the target tables.

Check that combined values and calculated measures.

Data checks in dimension table as well as history table.

Check the BI reports on the loaded fact and dimension table.

ETL Tools

There are many ETL tools are available in the market. Here, are some most prominent one:

1. MarkLogic:

MarkLogic is a data warehousing solution which makes data integration easier and faster using an array of enterprise features. It can query different types of data like documents, relationships, and metadata.

2. Oracle:

Oracle is the industry-leading database. It offers a wide range of choice of Data Warehouse solutions for both on-premises and in the cloud. It helps to optimize customer experiences by increasing operational efficiency.

3. Amazon RedShift:

Amazon Redshift is Datawarehouse tool. It is a simple and cost-effective tool to analyze all types of data using standard SQL and existing BI tools. It also allows running complex queries against petabytes of structured data.

Here is a complete list of useful Data warehouse Tools.

Best practices ETL process

Following are the best practices for ETL Process steps:

Never try to cleanse all the data:

Every organization would like to have all the data clean, but most of them are not ready to pay to wait or not ready to wait. To clean it all would simply take too long, so it is better not to try to cleanse all the data.

Never cleanse Anything:

Always plan to clean something because the biggest reason for building the Data Warehouse is to offer cleaner and more reliable data.

Determine the cost of cleansing the data:

Before cleansing all the dirty data, it is important for you to determine the cleansing cost for every dirty data element.

To speed up query processing, have auxiliary views and indexes:

To reduce storage costs, store summarized data into disk tapes. Also, the trade-off between the volume of data to be stored and its detailed usage is required. Trade-off at the level of granularity of data to decrease the storage costs.


ETLstands for Extract, Transform and Load.

ETL provides a method of moving the data from various sources into a data warehouse.

In the first step extraction, data is extracted from the source system into the staging area.

In the transformation step, the data extracted from source is cleansed and transformed .

Loading data into the target datawarehouse is the last step of the ETL process.

You're reading Etl (Extract, Transform, And Load) Process In Data Warehouse

Pentaho Data Integration Tutorial: What Is, Pentaho Etl Tool

What is Pentaho BI?

Pentaho is a Business Intelligence tool which provides a wide range of business intelligence solutions to the customers. It is capable of reporting, data analysis, data integration, data mining, etc. Pentaho also offers a comprehensive set of BI features which allows you to improve business performance and efficiency.

In this Pentaho tutorial for beginners, you will learn:

Features of Pentaho

Following, are important features of Pentaho:

ETL capabilities for business intelligence needs

Understanding Pentaho Report Designer

Product Expertise

Offers Side-by-side subreports

Unlocking new capabilities

Professional Support

Query and Reporting

Offers Enhanced Functionality

Full runtime metadata support from data sources

Pentaho BI suite

Now, we will learn about Pentaho BI suite in this Pentaho tutorial:

Pentaho BI Suite

Pentaho BI Suite includes the following components:

Pentaho Reporting

Pentaho Reporting depends on the JFreeReport project. It helps you to fulfill your business reporting needs. This component also offers both scheduled and on-demand report publishing in popular formats such as XLS, PDF, TXT, and HTML.


It offers a wide range of analysis a wide range of features that includes a pivot table view. The tool provides enhanced GUI features (using Flash or SVG), integrated dashboard widgets, portal, and workflow integration.


The dashboard offers Reporting and Analysis, which contribute content to Pentaho Dashboards. The self-service dashboard designer includes extensive built-in dashboard templates and layout. It allows business users to build personalized dashboards with little training.

Data Mining

Data mining tool discovers hidden patterns and indicators of future performance. It offers the most comprehensive set of machine learning algorithms from the Weka project, which includes clustering, decision trees, random forests, principal component analysis, neural networks.

It allows you to view data graphically, interact with it programmatically, or use multiple data sources for reports, further analysis, and other processes.

Pentaho Data Integration

This component is used to integrate data wherever it exists.

Rich transformation library with over 150 out-of-the-box mapping objects.

It supports a wide range of data source which includes more than 30 open source and proprietary database platforms, flat files. It also helps Big Data analytics with integration and management of Hadoop data.

Who are using Pentaho BI?

Pentaho BI is a widely used tool by may software professionals like:

Open source software programs

Business analyst and researcher

College students

Business intelligence councilor

How to Install Pentaho in AWS

Following is a step by step process on How to Install Pentaho in AWS.

On next page, Accept License Agreement

Proceed for Configuration

Check the usage instructions and wait

Copy Public IP of the instance.

Paste public IP of the instance to access Pentaho.

Prerequisite of Pentaho

Hardware requirements

Software requirements

Downloading and installing Bl suite

Starting the Bl suite

Administration of the Bl suite

Hardware requirement:

The Pentaho Bl Suite software does not have any fix limits on a computer or network hardware as long as you can meet the minimum software requirements. It is easy to install this Business intelligence tool. However, a recommended set of system specifications:

RAM Minimum 2GB

Hard drive space Minimum 1GB

Processor Dual-core EM64T or AMD64

Software requirements

Installation of Sun JRE 5.0

The environment can be either 32-bit or 64-bit

Supported Operating systems: Linux, Solaris, Windows, Mac

A workstation that has a modern web browser interface such as Chrome, Internet Explorer, Firefox

To start Bl-server

On Linux OS run start-pentaho script on /biserver-ce/directory

To start the administrator server:

For Linux: goto the command window and run the start-up script in /biserver-ce/administration-console/directory.

To Stop administrator server:

On Linux. You need to go to the terminal and goto installed directory and run stop.bat

Pentaho Administration Console Report Designer: Design Studio:

It is an Eclipse-based tool. It allows you to hand-edit a report or analysis. It is widely used to add modifications to an existing report that cannot be added with Report Designer.

Aggregation Designer:

This graphical tool allows you to improve Mondrian cube efficiency.

Metadata Editor:

It is used to add custom metadata layer to any existing data source.

Pentaho Data Integration:

The Kettle extract, transform, and load (ETL) tool, which enables

Pentaho Tool vs. BI stack

Pentaho Tool BI Stack

Data Integration (PDI) ETL

It offers metadata Editor It provides metadata management

Pentaho BA Analytics

Reports Designer Operational Reporting

Saiku Ad-hoc Reporting

CDE Dashboards

Pentaho User Console (PUC) Governance/Monitoring

Advantages of Pentaho

Pentaho BI is a very intuitive tool. With some basic concepts, you can work with it.

Simple and easy to use Business Intelligence tool

Offers a wide range of BI capabilities which includes reporting, dashboard, interactive analysis, data integration, data mining, etc.

Comes with a user-friendly interface and provides various tools to Retrieve data from multiple data sources

Offers single package to work on Data

Has a community edition with a lot of contributors along with Enterprise edition.

The capability of running on the Hadoop cluster

JavaScript code written in the step components can be reused in other components.

Here, are cons/drawbacks of using Pentaho BI tool:

The design of the interface can be weak, and there is no unified interface for all components.

Much slower tool evolution compared to other BI tools.

Pentaho Business analytics offers a limited number of components.

Poor community support. So, if you don’t get a working component, you need to wait till the next version is released.


Pentaho is a Business Intelligence tool which provides a wide range of business intelligence solutions to the customers

It offers ETL capabilities for business intelligence needs.

Pentaho suites offer components like Report, Analysis, Dashboard, and Data Mining

Pentaho Business Intelligence is widely used by 1) Business analyst 2) Open source software programmers 3) Researcher and 4) College Students.

The installation process of Pentaho includes: 1)Hardware requirements 2) Software requirements, 3) Downloading Bl suite, 4) Starting the Bl suite, and 5) Administration of the Bl suite

Important components of Pentaho Administration console are 1) Report Designer, 2) Design Studio, 3) Aggregation Designer 4) Metadata Editor 5) Pentaho Data Integration

Pentaho is a Data Integration (PDI) tool while BI stack is an ETL Tool.

The main drawback of Pentaho is that it is a much slower tool evolution compared to other BI tools

How To Extract Tabular Data From Doc Files Using Python?

This article was published as a part of the Data Science Blogathon


Data is present everywhere. Any action we perform generates some or the other form of data. But this data might not be present in a structured form. A beginner starting with the data field is often trained for datasets in standard formats like CSV, TSV, or some text files. CSV files are the most preferred files as they can be loaded into a pandas dataframe and manipulated more easily. The text files can be loaded using naive Python file handling modules.

But in the real world, any type of document can have the data needed for analysis. While I was applying for an internship position in a company, my assignment was to draw analysis out of the data present in the Doc file. In this article, I will explain the ETL process for a Doc file, the difference between Doc and Docx extensions, conversion of Doc to Docx, and at the end, I will show you how I created some interactive plots from that data.

Image by Author, Made in Canva

Difference Between Doc and Docx

While dealing with doc files, you will come across these two extensions: ‘.doc’ and ‘.docx’. Both the extensions are used for Microsoft word documents that can be created using Microsoft Word or any other word processing tool. The difference lies in the fact that till word 2007, the “doc” extension was used extensively.

After this version, Microsoft introduced a new extension, “Docx”, which is a Microsft Word Open XML Format Document. This extension allowed files to be smaller, easy to store, and less corrupted. It also opened doors to online tools like Google Sheets which can easily manage these Docx files.

Conversion of Doc to Docx in Python

Today, all the files are by default created with the extension Docx but there are still many old files with Doc extension. A Docx file is a better solution to store and share data but we can’t neglect the data stored in Doc files. It might be of great value. Therefore, to retrieve data from Doc files, we need to convert the Doc file to Docx format. Depending on the platform, Windows or Linux, we have different ways for this conversion.

For Windows

Manually, for a word file to be saved as Docx, you simply need to save the file with the extension “.docx”

We will perform this task using Python. Window’s Component Object Model (COM) allows Windows applications to be controlled by other applications. pywin32 is the Python wrapper module that can interact with this COM and automate any windows application using Python. Therefore, the implementation code goes like this:

from win32com import client as wc w = wc.Dispatch('Word.Application') doc = w.Documents.Open("file_name.doc") doc.SaveAs("file_name.docx", 16)

Breakdown of the code:

First, we are importing the client from the win32com package which is preinstalled module during Python installation.

Next, we are creating a Dispatch object for the Word Application.

Then, we are opening this document and saving it with the Docx extension.

For Linux

We can directly use LibreOffice in-build converter:

lowriter --convert-to docx testdoc.doc Reading Docx files in Python

Python has a module for reading and manipulating Docx files. It’s called “python-docx”. Here, all the essential functions have been already implemented. You can install this module via pip:

pip install python-docx

I won’t go into detail about how a Docx document is structured but on an abstract level, it has 3 parts: Run, paragraph, and Document objects. For this tutorial, we will be dealing with paragraph and Document objects. Before moving to the actual code implementation, let us see the data will be extracting:

Data in new Docx file

The new Docx file contains the glucose level of a patient after several intervals. Each data row has an Id, Timestamp, type, and glucose level reading. To maintain anonymity, I have blurred out the Patient’s name. Procedure to extract this data:

1. Import the module

import docx

2. Create a Docx file document object and pass the path to the Docx file.

Text = docx.Document('file_name.docx')

3. Create an empty data dictionary

data = {}

4. Create a paragraph object out of the document object. This object can access all the paragraphs of the document

paragraphs = Text.paragraphs

5. Now, we will iterate over all the paragraphs, access the text, and save them into a data dictionary

for i in range(2, len(Text.paragraphs)): data[i] = tuple(Text.paragraphs[i].text.split('t'))

Here I had to split the text at “t” as if you look at one of the rows, it had the tab separator.

6. Access the values of the dictionary

data_values = list(data.values())

Now, these values are transformed as a list and we can pass them into a pandas dataframe. According to my use case, I had to follow some additional steps such as dropping unnecessary columns and timestamp conversion. Here is the final pandas dataframe I got from the initial Doc file:

There are a lot of things that can be done using the python-docx module. Apart from loading the file, one can create a Docx file using this module. You can add headings, paragraphs, make text bold, italics, add images, tables, and much more! Here is the link to the full documentation of the module.

Bonus Step: Plot using Plotly

The main aim of this article was to show you how to extract tabular data from a doc file into a pandas dataframe. Let’s complete the ELT cycle and transform this data into beautiful visualizations using the Plotly library! If you don’t know, Plotly is an amazing visualization library that helps in creating interactive plots.

These plots don’t require much effort as most of the things can be customized. There are many articles on Analytics Vidhya describing the usage of this library. For my use case, here is the configuration for the plot:

import plotly.graph_objects as go fig = go.Figure() fig.add_trace(go.Scatter(x=doc_data.index, y=doc_data['Historic Glucose (mg/dL)'].rolling(5).mean(), mode='lines', marker=dict( size=20, line_width=2, colorscale='Rainbow', showscale=True, ), name = 'Historic Glucose (mg/dL)' )) fig.update_layout(xaxis_tickangle=-45, font=dict(size=15), yaxis={'visible': True}, xaxis_title='Dates', yaxis_title='Glucose', template='plotly_dark', title='Glucose Level Over Time' ) fig.update_layout(hovermode="x")

Image by Author


In this article, I explained what are doc files, the difference between Doc and Docx file extensions, conversion of Doc files into Docx files, loading and manipulation of Docx files, and finally how to load this tabular data into a pandas dataframe.

If you want to read/explore every article of mine, then head over to my master article list which gets updated every time I publish a new article on any platform!

For any doubts, queries, or potential opportunities, you can reach out to me via:

1. Linkedin — in/kaustubh-gupta/

2. Twitter — @Kaustubh1828

3. GitHub — kaustubhgupta

4. Medium — @kaustubhgupta1828

The media shown in this article on Interactive Dashboard using Bokeh are not owned by Analytics Vidhya and are used at the Author’s discretion.


Selecting The Best Warehouse And Distribution Solution

Here are three key areas that your warehouse and distribution operations will benefit from, when you select the right warehouse partner.

Warehousing and distribution benefits you need

Are you currently confronting merchandise surges, experiencing overflow or anticipating a job following year?

If you are not prepared to make the capital investments in to your warehouse area, you are probably looking for a spouse or a public warehouse to deal with your merchandise flow.

When deciding upon the proper warehouse and distribution spouse , the choice boils down to trust. Who would you trust with your own products?

Who would you trust to become a genuine partner in your warehousing and supply requirements?

Also read: Top 10 IT Skills in Demand for 2023

Simplify your inventory surges with quick resolution to better handle business demands

Flexibility permits you to create adjustments on demand. And if those seasonal jobs return about, are you can scale quickly?

A warehouse community with demonstrated agility, one which is unable to expect changes, are going to have the ability to handle inventory cycles and optimize the rate of delivering your goods to your clients.

Ensure customer satisfaction with extended order times

Customer satisfaction. With customer satisfaction comes client loyalty and secured company development.

Warehouse and distribution approaches that provide extended order occasions and provide next day service to your clients, could separate you from the contest.

Partner using a warehouse that will incorporate logistics services and dependable transportation solutions together with your supply approaches. Here is the very best approach to make sure late in daily pick up times and next day service to the fast-moving freight.

Also read: Best Online Courses to get highest paid in 2023

Better inventory management resulting in improved efficiencies and peace of mind

Maybe the very best warehouse and supply approach is one which you do not need to consider. One which is so effective that you are ready to concentrate on the rise of your core business.

By working in this way with your warehouse management group, you may be assured that they have a complete comprehension of your stock and company requirements. Which will be the best guarantee your warehouse staff is about to adopt and adapt to your company routines.

Inventory direction , enhanced efficiencies and your reassurance is best accomplished by decreasing danger in the security and handling of your merchandise. If you associate with a business who owns and manages their own spaces with experienced employees, it is possible to minimize these dangers.

Dekker’S Algorithm In Process Synchronization


Process synchronization is a critical concept in computer science, especially in operating systems. It involves coordinating the activities of multiple processes to ensure that they run correctly and avoid conflicts. Mutual exclusion is a fundamental problem in process synchronization that arises when multiple processes need to access a shared resource or critical section. If two or more processes simultaneously access the same shared resource, it can lead to incorrect results or data corruption.

To solve this problem, various algorithms have been developed over the years. One of the most popular of these is Dekker’s algorithm, which was proposed by Cornelis Dekker in 1965. It is a simple and efficient algorithm that allows only one process to access a shared resource at a time. The algorithm achieves mutual exclusion by using two flags that indicate each process’s intent to enter the critical section. By alternating the flags’ use and checking if the other process’s flag is set, the algorithm ensures that only one process enters the critical section at a time.


The algorithm uses flags to indicate the intention of each process to enter a critical section, and a turn variable to determine which process is allowed to enter the critical section first.

Here are the detailed steps involved in Dekker’s Algorithm −

Initialization − Each process sets its flag to false initially, indicating that it does not intend to enter the critical section. The turn variable is also set to the value of either 0 or 1, indicating which process is allowed to enter the critical section first.

Process A enters the critical section − Process A sets its flag to true, indicating its intention to enter the critical section. It then checks if Process B’s flag is also true, indicating that Process B also wants to enter the critical section. If so, Process A sets the turn variable to 1, indicating that it is Process B’s turn to enter the critical section first. Process A then enters a busy-wait loop, repeatedly checking if it is its turn to enter the critical section.

Process B enters the critical section − Process B sets its flag to true, indicating its intention to enter the critical section. It then checks if Process A’s flag is also true, indicating that Process A also wants to enter the critical section. If so, Process B sets the turn variable to 0, indicating that it is Process A’s turn to enter the critical section first. Process B then enters a busy-wait loop, repeatedly checking if it is its turn to enter the critical section.

Process A exits the critical section − Once Process A is allowed to enter the critical section, it executes the critical section code and then sets its flag to false, indicating that it is done with the critical section. It then sets the turn variable to 1, indicating that it is now Process B’s turn to enter the critical section.

Process B exits the critical section − Once Process B is allowed to enter the critical section, it executes the critical section code and then sets its flag to false, indicating that it is done with the critical section. It then sets the turn variable to 0, indicating that it is now Process A’s turn to enter the critical section.

Repeat − The two processes then repeat the above steps, alternating between entering and exiting the critical section as determined by the turn variable and the state of each process’s flag.

Use cases

Dekker’s Algorithm can be applied in various systems and platforms that require mutual exclusion.

Here are some examples −

Operating systems − Dekker’s Algorithm can be used in operating systems to prevent multiple processes from accessing a shared resource simultaneously. For example, if two processes need to access a file, Dekker’s Algorithm can be used to ensure that only one process accesses the file at any given time.

Robotics − In robotics, multiple processes may need to control the movement of a robot. Dekker’s Algorithm can be used to ensure that only one process controls the robot’s movement at any given time, preventing collisions or other issues.

Real-time Systems − In real-time systems, timing constraints are critical, and processes need to execute within a specific deadline. Dekker’s Algorithm can be used to ensure that only one process at a time can access a critical section of code that affects the system’s timing behavior. This can prevent timing violations, priority inversion, and other real-time synchronization problems.

Dekker’s Algorithm can be implemented in code using any programming language

Here is an example implementation of Dekker’s Algorithm in Python −

import threading class Dekker: def __init__(self): chúng tôi = [False, False] chúng tôi = 0 def lock(self, i): self.flag[i] = True while self.flag[1-i]: if chúng tôi == 1-i: self.flag[i] = False while chúng tôi == 1-i: pass self.flag[i] = True chúng tôi = 1-i def unlock(self, i): self.flag[i] = False # Sample usage dekker = Dekker() def critical_section(thread_num): print("Thread", thread_num, "entered critical section") # Perform critical section operations here print("Thread", thread_num, "exited critical section") dekker.unlock(thread_num) def thread_function(thread_num): dekker.lock(thread_num) critical_section(thread_num) thread_1 = threading.Thread(target=thread_function, args=(0,)) thread_2 = threading.Thread(target=thread_function, args=(1,)) thread_1.start() thread_2.start() thread_1.join() thread_2.join()

The thread_function function represents the code executed by each thread, which first acquires the lock using the lock method and then enters the critical section to perform operations. Once the thread has finished its critical section, it releases the lock using the unlock method.

Strengths and Weaknesses of the algorithm Strengths

Dekker’s Algorithm is simple and easy to understand.

The algorithm guarantees mutual exclusion and progress, meaning that at least one process will eventually enter the critical section.

The algorithm does not require hardware support and can be implemented in software.


The algorithm is prone to starvation since it does not ensure fairness, meaning that one process could continuously enter the critical section while the other process waits indefinitely.

The algorithm requires busy waiting, which can lead to high CPU usage and inefficiency.

The algorithm is susceptible to race conditions and may fail under certain conditions.

Time complexity

The time complexity of the algorithm is O(n^2) in the worst case, where n is the number of processes.

This is because each process may need to wait for the other process to finish its critical section, leading to a potential infinite loop.

Space complexity

The space complexity of the algorithm is O(1), as it only requires a few flags and turn variables.

Comparison with other mutual exclusion algorithms

Peterson’s Algorithm is another classic mutual exclusion algorithm that is similar to Dekker’s Algorithm. Peterson’s Algorithm also uses flags and turn variables, but it avoids busy waiting by using turn variables to determine which process should go first. Peterson’s Algorithm is fairer than Dekker’s Algorithm but may require hardware support in some cases.

Bakery Algorithm is another mutual exclusion algorithm that is more complex than Dekker’s Algorithm. It avoids busy waiting and starvation by assigning each process a number and comparing the numbers to determine which process should enter the critical section first. The Bakery Algorithm is fair and efficient but may require more memory than Dekker’s Algorithm.


Dekker’s Algorithm is a classic algorithm for solving the mutual exclusion problem in process synchronization. It provides a simple and effective software-based solution for two processes to share a critical section of code without interfering with each other.Although the algorithm has some limitations, such as its scalability and potential wastage of CPU time, it remains a popular choice for teaching the basics of concurrency and synchronization in computer science courses. Dekker’s Algorithm has also inspired the development of other algorithms and techniques that address the mutual exclusion problem in more complex scenarios.

Use Quicktime To Extract Audio From Video Files In Mac

When it comes to QuickTime, most people only think of it as a video player. The truth is, it is more than just a simple media player. It can also be used to record movie, audio and screen. And if you want to extract the audio (or music) from the music video file, QuickTime can do that as well, easily and quickly.

The steps to extract audio from video file are easy.

1. Open QuickTime in your Mac and load the video file. If it is playing, you can pause or stop it. The video doesn’t need to be playing to extract the audio file.

That’s it.


Damien Oh started writing tech articles since 2007 and has over 10 years of experience in the tech industry. He is proficient in Windows, Linux, Mac, Android and iOS, and worked as a part time WordPress Developer. He is currently the owner and Editor-in-Chief of Make Tech Easier.

Subscribe to our newsletter!

Our latest tutorials delivered straight to your inbox

Sign up for all newsletters.

By signing up, you agree to our Privacy Policy and European users agree to the data transfer policy. We will not share your data and you can unsubscribe at any time.

Update the detailed information about Etl (Extract, Transform, And Load) Process In Data Warehouse on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!