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Understanding Data Repositories: Data Warehouses, Data Marts, and Data Lakes

Tilak Suryawanshi
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1
Understanding Data Repositories: Data Warehouses, Data Marts, and Data Lakes

In this article, we will talk about Data Warehouses, Data Marts and Data Lakes. All of the data mining repositories have one thing in common. They store data and then report and analyze that data to gain actionable business insights. However, what is it that they are used for, what kind of data is stored there, and how would one be able to get to that data differ a lot. We delve deep into what a data warehouse, a data mart, and a data lake are in this detailed write-up.

Data Warehouses:

A data warehouse is a central location in which data from multiple sources is integrated, cleaned, conformed, and categorized. This ensures that the data is modeled and structured for analysis, so it is ready for analysis upon entry.

Conventional data warehouses sit on top of relational data, which are from transactional systems and operational databases that include CRM, ERP, HR, and finance applications. Lately, non-relational data repositories have also been in practice, primarily due to NoSQL technologies to house Big Data within an organization.

Data Warehouse Architecture
  • Bottom Tier: This consists of the database servers, which could be relational, non-relational, or both, extracting data from multiple sources.Middle Tier: It is the OLAP Server that allows users to process and analyze information from numerous database servers.

    Top Tier: This is the client front-end layer. It consists of all tools and applications for querying, reporting, and data analysis.

Data Marts:

A data mart is usually a subset of a data warehouse and is expressly built to accommodate a particular business function, purpose or community of users. For instance, data marts may be accessed by sales and finance teams for their quarterly reporting and projections.

Data Mart Types

Dependent Data Marts: enterprise data warehouse sub-section, access the analytics functionality for a limited area while providing superior security and performance.

Independent Data Marts: Source built independently of enterprise data warehouse, including internally developed operational systems and other external data sources.

Hybrid Data Marts: Collecting input from warehoused data, plus operational systems and external sources.
The dependent and independent data marts go through different processes of extraction, transformation, and transportation of data.

Also read: Understanding Big Data: The 5 V’s That Drive the Digital Age

Goals of Data Marts:

Data marts are designed so that it is possible:

  • To display data to the user on demand
  • To accelerate the operation of the company through appropriate response time
  • To enable people to make economical and prompt decisions from the data
  • To improve response time for end-users
  • To ensure safe access and control by users

Data Lakes:

The data lake can be explained as a storage repository that holds vast amounts of structured, semi-structured, and unstructured data in its native format. In contrast to data warehouses, these lakes do not need data structuring and schema definition beforehand, at loading time.

Features of and Advantages Brought by Data Lakes
  • It stores every kind of data that exists: documents, emails, structured/relational data, and semi-structured, for example, JSON, XML, and CSV.
  • It scales storage from terabytes to petabytes in a more flexible way.
  • Time-Saving: Structures, Schemas, and Transformations need not be defined at the beginning.
  • Flexibility: Enabling the re-purposing of data in multiple ways for multifarious uses.
  • Data lakes may be implemented using cloud object storage, Relational Database Management Systems, and NoSQL repositories. Some significant vendors are Amazon, Cloudera, Google, IBM, Informatica, Microsoft, Oracle, SAS, Snowflake, Teradata, and Zaloni.

Conclusion

In other words, data warehouses, data marts, and data lakes all have the critical function of providing a residence for data meant to be analyzed for insights. Knowing the strengths, the architectures, and the benefits of each of these is an important point when considering the right data repository for your needs and technology infrastructure.

Step-by-Step Guide to Changing System Fonts in Windows

Tilak Suryawanshi
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0
Step-by-Step Guide to Changing System Fonts in Windows

In this article, we will see how we can change the system font of Windows 11. As the default font can be boring and if you have a font in mind, then it will be great.

Changing the font is very simple, you just need to follow some easy and small steps.

If you don’t know what font you want to change into, you can look for them in Settings > Personalisation > Fonts

Select the font you like and note down the full name of the font.

Now let’s come to the steps:

1. Open Notepad (In Windows search, type Notepad and click open)
2. Copy and paste below code in the notepad. Don’t forget to replace the “Font name” to the font name you have selected.

Windows Registry Editor Version 5.00
[HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Fonts]
"Segoe UI (TrueType)"=""
"Segoe UI Bold (TrueType)"=""
"Segoe UI Bold Italic (TrueType)"=""
"Segoe UI Italic (TrueType)"=""
"Segoe UI Light (TrueType)"=""
"Segoe UI Semibold (TrueType)"=""
"Segoe UI Symbol (TrueType)"=""
HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion\FontSubstitutes]
"Segoe UI"="Font name"

3. Save this file as a “.reg” extension at the end. Example, font.reg
4. Now, close this file and double-click to open it again, this will launch the file, click Yes for registry Editor.
5. Click OK and you’re done.
6. Now Restart the PC and the system font has been changed.

Also read: How To Install Python in Windows

That’s it. If you have any questions, do comment on this and I will make sure to reply. Thank you.

Understanding Big Data: The 5 V’s That Drive the Digital Age

Tilak Suryawanshi
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0
Understanding Big Data: The 5 V’s That Drive the Digital Age

In the digital age, every action we take leaves a digital footprint. From our travel habits to our exercise routines and entertainment choices, many internet-connected devices continuously gather vast amounts of data about us. This phenomenon is known as Big Data.

According to Ernst and Young, “Big Data refers to the dynamic, large, and disparate volumes of data generated by people, tools, and machines. It requires innovative and scalable technology to collect, host, and analyze these vast datasets to provide real-time business insights related to consumers, risk, profit, performance, productivity management, and enhanced shareholder value.”

big-data-TNBSHARE
Image by Tumisu from Pixabay

Although definitions of Big Data vary, they commonly include five key characteristics known as the V’s: velocity, volume, variety, veracity, and value.

  1. 1. Velocity: This refers to the speed at which data is generated and processed. Modern technology enables near-real-time data processing through local and cloud-based systems.
  2. Volume: This indicates the sheer amount of data being stored. The volume of data is increasing due to more data sources, higher-resolution sensors, and scalable infrastructure.
  3. Variety: This encompasses the different types of data, both structured (organized in rows and columns) and unstructured (such as tweets, blog posts, images, and videos). Data comes from various sources, including mobile technologies, social media, wearable devices, and more.
  4. Veracity: This pertains to the accuracy and reliability of data. It includes attributes like consistency, completeness, integrity, and ambiguity. The cost and need for traceability are critical drivers of data veracity.
  5. Value: This is about deriving meaningful insights from data. Big Data is not just about profit; it can also provide medical, social, customer, employee, and personal benefits.

Let’s explore some practical examples of these V’s in action:

  • Velocity: Every minute, hours of video footage are uploaded to YouTube, generating vast amounts of data.
  • Volume: With a global population of around seven billion, most people use digital devices like mobile phones, computers, and wearables, generating approximately 2.5 quintillion bytes of data daily. This volume is equivalent to 10 million Blu-ray DVDs.
  • Variety: Data comes in many forms, such as text, images, videos, sound, and health data from wearables. It also includes data from devices connected to the Internet of Things.
  • Veracity: Since 80% of data is unstructured, developing methods to produce accurate and reliable insights is essential. This data needs to be categorized, analyzed, and visualized effectively.
  • Value: Data scientists extract insights from Big Data to tackle challenges presented by these extensive datasets. Traditional data analysis tools are often inadequate due to the scale of data, necessitating alternative tools that leverage distributed computing power, such as Apache Spark and Hadoop. These tools enable organizations to gain new insights and connect with customers more effectively.

Also Read: Becoming a Successful Data Engineer: A Comprehensive Roadmap and Tips for Aspiring Professionals – TNBS (tnbshare.com)

Next time you use a smartwatch, smartphone, or fitness tracker, remember that your data begins a journey that may travel worldwide, through Big Data analysis, ultimately delivering valuable insights back to you.

Disclaimer: This article is referenced from the IBM Data Engineering course on Coursera.

Step-by-Step Guide: How to Add a URL Link Using HTML

Tilak Suryawanshi
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0
Step-by-Step Guide: How to Add a URL Link Using HTML

In this article, we will discuss how to add a URL using HTML code. Adding URLs into the sentences can be very helpful when we are referencing any other article or web page, There are many use cases where we use URLs to redirect to new sites, to show more detail about certain information etc.

So the first thing is the URL tag will come under the BODY tag. This tag is called HREF which comes under the a tag.

Syntax for the code:

<a href="https://www.example.com">Click here to visit Example.com</a>

In this example:

  •  `<a>` is the anchor tag used to create a hyperlink.
  •  `href` is the attribute that specifies the URL of the page the link goes to.
  • `https://www.example.com` is the URL you want to link to.
  • `Click here to visit Example.com` is the text that will be clickable in the browser.

Step-by-step instructions on how to add a URL link to your HTML code:

  1. Open your HTML file: Use a text editor (like Notepad, Sublime Text, VS Code, etc.) to open your existing HTML file or create a new one.
  2. Locate the section where you want to add the link: This could be within the body of your HTML document. For example, between the `<body>` tags.
  3. Insert the anchor tag: Add the `<a>` tag with the `href` attribute at the desired location. Here’s an example:

    <!DOCTYPE html>

    <html lang="en">

    <head>

        <meta charset="UTF-8">

        <meta name="viewport" content="width=device-width, initial-scale=1.0">

        <title>My Web Page</title>

    </head>

    <body>

        <h1>Welcome to My Web Page</h1>

        <p>This is a paragraph with a link to <a href="https://www.example.com">Example.com</a>.</p>

    </body>

    </html>

  1. Save the file: After adding the anchor tag, save your HTML file.
  2. Open the HTML file in a web browser: Double-click the HTML file, or open it via your web browser’s “Open File” option to see the result. The link text should now be clickable and direct you to the specified URL.
  3. Styling the link (optional): If you want to style the link, you can add CSS either inline, within a `<style>` tag in the head, or through an external stylesheet. Here’s an example using inline CSS:

    <a href="https://www.example.com" style="color: blue; text-decoration: none;">Click here to visit Example.com</a>

That’s it! You’ve successfully added a URL link to your HTML code.

Becoming a Successful Data Engineer: A Comprehensive Roadmap and Tips for Aspiring Professionals

Tilak Suryawanshi
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0
Becoming a Successful Data Engineer: A Comprehensive Roadmap and Tips for Aspiring Professionals

In this article, we will talk about the comprehensive roadmap and tips for aspiring Data Engineer Professionals. As I was looking to transition from an entry-level Data analyst to a Data Engineer, this roadmap helping me to achieve the goal and create a mindset to become a successful engineer.

There are different ways to become a data engineer even if you are from a non-technical background. Start from the basics of data engineering, learn a programming language or two, and learn the processing tools, frameworks, and ETL pipelines. Most importantly, apply this learning by doing real-world projects and showcasing them in your portfolio. Following these will surely get you a good-paying job as a Data Engineer.

Roadmap for becoming a Data Engineer:

  1. Understand the Basics of Data Engineering:
    • Learn about databases, data structures, and algorithms.
    • Familiarize yourself with data modelling concepts such as relational, dimensional, and NoSQL databases. (An overview of what is data engineering and its fundamentals will help)
  2. Master Programming Languages:
    • Learn Python: Python is widely used in data engineering for data manipulation, scripting, and building data pipelines. Resources:
      • Online courses: Coursera’s “Python for Everybody” by the University of Michigan, Codecademy’s Python course.
      • Books: “Python Crash Course” by Eric Matthes, “Automate the Boring Stuff with Python” by Al Sweigart.
      • Also read: How to install Python in Windows
    • Learn SQL: SQL is essential for querying and manipulating data in databases. Resources:
      • Online courses: Udemy’s “The Complete SQL Bootcamp” by Jose Portilla, Khan Academy’s SQL course.
      • Books: “SQL Cookbook” by Anthony Molinaro, “Learning SQL” by Alan Beaulieu.
  3. Gain Proficiency in Data Processing Tools and Frameworks:
    • Apache Hadoop: Learn about distributed processing and storage with Hadoop. Resources:
      • Online courses: Coursera’s “Big Data Essentials: HDFS, MapReduce, and Spark RDD” by Yandex, Udacity’s “Intro to Hadoop and MapReduce” course.
    • Apache Spark: Master Spark for big data processing and analytics. Resources:
      • Online courses: edX’s “Big Data Analysis with Spark SQL” by UC Berkeley, DataCamp’s Spark courses.
    • Apache Kafka: Understand real-time data streaming with Kafka. Resources:
      • Online courses: LinkedIn Learning’s “Apache Kafka Essential Training” by Ben Sullins, Udemy’s “Apache Kafka Series” by Stephane Maarek.
  4. Learn Data Pipeline Orchestration and Workflow Management:
    • Apache Airflow: Study workflow automation and scheduling with Airflow. Resources:
      • Online courses: Udemy’s “Apache Airflow: The Hands-On Guide” by John R. Griffiths, Pluralsight’s “Getting Started with Apache Airflow” by Janos Haber.
    •  Luigi: Explore another popular workflow management tool. Resources:
      • Documentation and tutorials are available on the Luigi website
  5. Acquire Knowledge of Data Warehousing and ETL Processes:
    •   Understand ETL (Extract, Transform, Load) processes and techniques.
    •   Learn about data warehousing concepts and tools such as Amazon Redshift, Google BigQuery, and Snowflake.
  6. Develop Skills in Data Visualization and Reporting:
    •  Learn data visualization tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.
    •  Understand principles of effective data visualization and storytelling with data.
  7. Stay Updated and Continuously Learn:
    • Follow industry blogs, forums, and communities like Stack Overflow, Reddit’s r/datascience, and LinkedIn groups.
    • Attend conferences, webinars, and meetups related to data engineering and big data technologies.

Data Engineering roadmap

Tips for Success:

  • Practice regularly with hands-on projects and real-world datasets.
  • Collaborate with peers on data-related projects to gain practical experience.
  • Build a strong online presence through GitHub contributions, blog posts, or participating in relevant discussions.
  • Network with professionals in the field through LinkedIn, conferences, and local meetups.
  • Stay curious and keep exploring new technologies and techniques in data engineering.

Conclusion:

Embarking on the journey to become a successful Data Engineer requires a combination of foundational knowledge, technical skills, and practical experience. By following the roadmap outlined above and leveraging the suggested resources and tips, you can build a solid foundation in data engineering and position yourself for success in this rapidly evolving field.

As you progress on your path to becoming a data engineer, embrace challenges as opportunities for growth and innovation. With dedication, perseverance, and a passion for data, you can carve out a rewarding career in data engineering and contribute to the transformative power of data-driven decision-making in organizations worldwide.

How to download and install Windows 10 using USB

Tilak Suryawanshi
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0
How to download and install Windows 10 using USB

Want to reset the Windows 10 Setup in your PC or transition from Linux Operating system to Windows 10 but don’t know how to install it, in this article you will see how to install Windows 10 using bootable USB.

Now first you need to download the ISO file so you can create a bootable USB of it and install it.

There are some ways you can download the Windows 10 ISO file,

  1. By downloading the Windows 10 installation media tool from the Microsoft site.
  2. By using the Rufus software to download the Windows 10 ISO (Rufus is a software which is used to create a bootable USB and it makes this option more reliable for Windows 10)

In all ways, you can get the Windows 10 ISO file for the installation.

If you choose the 1st method, you will get two more options to do the installation or update the current Windows 10, where you can directly download the ISO file or you can just update or reset the current Windows 10 directly from the tool.

I recommend you to use Rufus for downloading the ISO file which is very easy to do it.

Now you need to download the tool to create a bootable USB.

There are many software to create a bootable USB in the market but my favorite is Rufus.

It is very easy to use and has more advanced settings like partition schemes which are MBR and GPT where you have to select GPT for Bios UEFI Non-CSM for Windows.

Remember, if you are coming from a Linux OS, select MBR partition scheme which will allow the Windows 10 installation after boot or else it will not show any disk drive to install Windows 10 onto.

Step to create a bootable USB for Windows 10:

  1. Open the Rufus software and connect the USB pen drive (at least 16Gb storage)

    Rufus-tnbs
    Source: rufus.ie

  2. Now select the ISO file which will be in the Downloads folder in your User sections by clicking on the Select button in the Boot Selection section.
  3. Keep the Image option untouched and select GPT in the Partition scheme.
  4. Keep everything the same and click on the Start button.
  5. It will ask for permission to delete everything in the USB/Pen Drive to extract and create a Bootable USB; click on Yes. The bootable USB will be created.

After this, you have to go to the Bios menu where you have to change some settings,

First, you have to enable the UEFI boot option and then disable the Secure boot option from the Bios menu and select the Save and Exit option.

Also read: How to Install Python in Windows

(To go to the Bios menu, shut down the PC and after pressing the power button, keep pressing the F2 or F1 key which will land you in the Bios menu. -Function keys to open the bios menu depend on the laptop/PC company, some companies’ devices have F2 for the Bios menu like Lenovo. Check on Google if you don’t know the Bios menu function key.)

Now like the Bios menu, you have to go to the Boot menu where you can select the USB device that has Windows 10. (Same as the Bios menu, to access the boot menu, after starting the computer, keep pressing the F12 key (For Lenovo PC) to access the boot menu and select the USB device.

Steps to Install Windows 10:

windows 10 - TNBS
source: binaryfork.com
  1. After the boot into the Windows 10 setup, a window will open, here you have to select the Language, Time and Currency and the Keyboard Layout and click on the Next button.
  2. Then it will ask for the type of installation, if you want to freshly install Windows 10, Select Custom installation; If you want to upgrade your previous Windows installation to a new one, select the Upgrade option and continue.
  3. After selecting the Custom option, the Disk drive table will appear. Here you have to select the C drive and click on format. Keep the selected drive and click on Next. (It will install Windows 10 on C drive after format)
  4. Wait for all the work and process until it’s done.
  5. When the PC boots it will ask you for some personal information like your Name, and Microsoft account to log in (Which is recommended) then set up the Cortana (Optional) and then select the Keyboard layout and location again and done.

That’s the setup. You have successfully installed Windows 10 on your devices.

So in conclusion, Rufus is the main software for both, downloading the Windows 10 ISO file and creating a bootable USB drive for Windows 10 installation.

But if you have a Linux system, you need to download the Windows 10 ISO file from other sources which I have mentioned above. Also to create a bootable USB, download and install Unetbootin software which is similar to Rufus and easy to use in Linux systems. The rest is the same.

Please let me know in the comment section below if you have any questions.

How To Install Python in Windows

Tilak Suryawanshi
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0
How To Install Python in Windows

Python is the world’s most used and versatile language. As python can be used in almost any technology field, such as Data Science, Web Development, GUI Development, Software Development or System Administration; using python libraries and frameworks like Django, Pyramid, tkinter, PyQt, SciPy, Pandas, iPython, Ansible etc. To open/write the Python files, you can use any IDE or Text Editors but it will not run or debug the file without Python installed on your system.

In this article, we will see how to install Python in Windows. To operate or use any Python files in the Windows operating system, we need to have Python installed to open the files, write code, and run it. The new version of Python is ver.3 with the latest version to download and use is version 3.12.2.

Steps to Install Python

  1. Go to the Python.org
  2. On the menu, click on the Downloads buttonIt will open the Python downloads page.

    How to install python

  3. Click on the Download Python 3.12.2 button, file will be downloaded.
  4. Find the file and open it, follow the instructions shown on the installation wizard and install.
    How to install python1
  5. That’s it. The Python version 3.12.2 is now installed on your system.

Now, while running the Python files in VS code or any other editor, you can select Python 3.12.2 for the Python kernel. Also, you can install Anaconda Distribution which has all the independencies and applications to write, run and debug the Python files. To download, click here.

That’s it. let me know if you have any queries by commenting down, will sure help on the same. Thank you.

How To Delete Instagram Account Permanently

Tilak Suryawanshi
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0
How To Delete Instagram Account Permanently

As we know, using social media can be overwhelming and literally can cause a lot of anxiety or make us depressed. Taking a small break or deleting one or two social media apps from your phone may give you some air.  Deleting Instagram would be the best choice you will make for taking a new look at life or connecting with people more offline.

Before deleting Instagram, you can deactivate it or delete it temporarily.

 

Instagram - TNBS
Image by Dennis from Pixabay

How to delete an Instagram Account temporarily (From the App):

  • Log in to your Instagram app.
  • Click on your profile picture and click on Edit Profile.
  • Find Temporarily Deactivate my account at the bottom right corner and click on it.
  • Select the reason why you are deactivating the account and enter your password. (Note that the option to deactivate your account will only appear when you have selected the reason, that’s how it is.)
  • Click Temporarily deactivate my account and click on Yes to continue or No to cancel.

How to delete an Instagram Account temporarily (From the Accounts Centre):

  • Click on the More button then click on Settings.
  • Click on Accounts Centre then click on Personal details.
  • Click on Account Ownership and Control, then click Deactivation and Deletion.
  • Click the account you would like to deactivate.
  • Click on continue.

How to delete an Instagram Account permanently (From a computer):

  • Go to this page and log in to your Instagram account if you have not logged in yet.
  • Select the reason why you are deactivating the account and enter your password. (Note that the option to deactivate your account will only appear when you have selected the reason, that’s how it is.)
  • Click Delete (username)

How to delete an Instagram Account (From the Accounts Centre):

  • Click on the More button then click on Settings.
  • Click on Accounts Centre then click on Personal details.
  • Click on Account Ownership and Control, then click Deactivation and Deletion.
  • Click the account you would like to delete.
  • Click Delete account and then click Continue.

As per the above process, you will be able to delete or deactivate your Instagram account temporarily or permanently.

If you want to know more about the same process or if the account centre is not working for the deletion/deactivation, check out the Instagram Help page.

 

 

 

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