TechDogs-"A Simple Guide To Data Transformation"

Data Management

A Simple Guide To Data Transformation

By TechDogs Editorial Team

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Overview

Imagine taking a bunch of random pictures and turning them into a tool where people can post their images, interact with others and find common people. That sounds like a pretty wild idea, right? Almost like taking the entire social experience and putting it online. Well, that's exactly what Mark Zuckerberg did with Facebook, as shown in the movie “The Social Network.” By converting the data he had into a format that his website, "Facemash" could use, he revolutionized the way people networked and interacted. We mean, who doesn't have a Facebook account?
 
Just as Mark Zuckerberg turned data into a usable way, almost every company is transforming data to keep their business going strong. It's not just about moving a bunch of files or applying filters but rather transforming data into something that makes sense. No wonder how big businesses can comprehend and deploy their big ideas in the real world. 
 
So, what is Data Transformation and how does it help your business? Read on and you will find the answers to your questions.
TechDogs-"A Simple Guide To Data Transformation" Lego Of The OLD Data Strategies
No matter how much you love or hate the term ‘data-driven’,  you have to accept it to make strategic decisions for your company. Think about it: why are only 24% of the companies considered a ‘data-driven organization’ according to the annual NewVantage Partners Execution survey? What about the ones that are not relying on insights from data?
 
It is super rare when the dataset meets the requirements of data analysis. Maybe that’s why companies avoid going for that critical and expensive ‘data-driven’ decision-making. Yet, there has to be a way to make it easy, right?
 
There’s a way - Data Transformation. It doesn’t matter if you are from a technical background or someone who is looking to make sense of the endless stream of data online, Data Transformation can make your work a lot easier in understanding data and gaining new information from it.
 
Wondering what it is and how it can boost your business? Buckle up and get ready for a thrilling ride through the exciting world of Data Transformation.
 

What Is Data Transformation?


Data Transformation is like playing with building blocks or LEGOs! You start with a bunch of small
pieces and with each transformation, you add another layer, making it more structured and complex to create something amazing and new.

In technical words, Data Transformation is a computing process in which the system takes raw data and turns it into something more useful and organized. It's like taking a pile of LEGO bricks and building a towering skyscraper with it. The result is a structure that's more visually appealing, understandable and easier to work with.

Whether you're dealing with data wrangling, warehousing or integration, Data Transformation is the foundation for making sense of all data. Just like a skilled architect, a good data transformer knows how to take disparate pieces and turn them into a cohesive whole (from bricks to Burj Khalifa!).

Now you know what Data Transformation is but do you know how it came into existence? That’s an interesting story that you cannot miss – read on.
 

Where Did Data Transformation Come From?


The evolution of Data Transformation started when SQL loaders came into the land of commercial databases about 20 years ago. People used SQL systems to load external data into their business databases but the transformation was limited to one-to-one changes and manipulation was done using traditional PL/SQL functions. It was a decent setup but there was scope for improvement.

Then came other tools that created much-needed competition in the market. These tools brought in the emerging data warehouse as a hot trend of the era. IT service companies such as Informatica, Pentaho and Talend began offering rich Data Transformation capabilities with user interfaces to support ETL (Extract, Transform, Load) processes. Needless to say, these tools were widely accepted. However, they had their drawbacks and the major one was their reusability.

To deal with all the setbacks, modularization was adopted. It was about the time when programmers stepped in to create their own home-grown ETL or open-source technologies. Their goal was to create metadata-driven reusable components to reduce deployment times and improve the maintenance of databases. Nonetheless, handling and transforming large data was still a challenge.

Finally, the machines were here! They brought in more powerful processors, faster flash drives and higher memory capacity. This led to the birth of MPP (massively parallel processing) systems and Hadoop-based distributed computing. These systems transformed data using MapReduce programs and stored it in NoSQL databases. #AdvancementsAllAround

In today’s world, with Machine Learning, start-ups such as Paxata and Trifacta emerged to offer automated Data Transformation capabilities. That’s how we got today’s revolutionary Data Transformation systems with advanced techniques and algorithms that every company wants to adopt.

Now, let’s move on to how Data Transformation works.
 

How Does Data Transformation Work?


TechDogs-"How Does Data Transformation Work?" A GIF Showing Data From Star Trek
You know that Data Transformation can be a wild and thrilling adventure that requires careful planning. Whether you're a developer or a data analyst, you must know how this actually works. So, have a look at each step:
 
  • Discover The Data

    Everything starts from searching for structured and unstructured data. This is done by exploring your databases and using tools to know their structure and characteristics. This helps you decide how to turn it into the valuable information you're after. Think of it as mapping out the landscape so you know where to dig for gold!

  • Map Out Data Analysis

    It's the process of turning raw data into something usable by defining how individual fields are transformed into the final product. Developers and data wizards are the ones who get to work their magic in this step by mapping out the rules for the transformation.

  • Generate The Code

    The next step is code generation which transforms the existing data according to the mapping rules defined in the above step. The transformation takes the defined metadata and generates the necessary code in languages such as SQL, Python or R to create the desired output.

  • Execute The Code

    Now, it's show time! The generated code takes center stage and transforms the data into the desired output. This code can either be automatically executed by the tool or a developer might have to put on their conductor hat and manually drive the process.

  • Review The Output Data

    Data review is the final step in the Data Transformation process to ensure the output meets the requirements. Business users or end-users usually perform this step and report any anomalies or errors found to the developer or data analyst.


Data Transformation sounds easy now that you know the steps, right? However, what difference does it bring to your business? The next part is all about that!
 

Why Do We Need Data Transformation?


Businesses rely on data and that means a lot of data-driven processes, such as migrating data to the cloud, cleaning up duplicate data and making sure business data is formatted in a usable way. Transformations can make data even cooler by checking to make sure everything is in order before sending it on its way to different teams and processes.

Imagine a small town where two ice cream shops exist - Scoops and Swirls. Scoops use a fancy computer system to store all of its employee information, while Swirls is old-school and writes everything down on paper. One day, the two shops merge to become the ultimate ice cream paradise and they need to create one payroll for all employees. However, there's a problem - the information is stored in different formats! How will they make it work?

Enter the superhero Data Transformation! With its magic wand, this tool can easily take the employee information from Swirls’ paper-based system and merge it in the same format as the information from Scoops' computerized records. Voila! The once separate and discrete employee information is now unified in a single, shiny payroll. This is just one use case for Data Transformation.

It is a lifesaver for businesses that need to make sense of their data and use it effectively. With the right tools, you too can transform data like a boss! Yet, where else can you use this awesome data power?
 

What Are The Types Of Data Transformation?


TechDogs-"What Are The Types Of Data Transformation?" An Image Of A Man Using Data Transformation
Data Transformation is transformational for your business but there is more than one way of using it. So, here are the types of Data Transformation you can experiment with to see which one works best for you:
 
  • On-premise Data Transformation

    On-premise Data Transformations are like having a sidekick for your data! With a quick flick of the wrist, you can extract, transform and load important information in a flash. Plus, having a trusty on-premise tool by your side means you'll stay compliant with the law and keep your data secure. What’s the best part?

    Even non-technical folk can use these transformation tools. It has a drag-and-drop feature for people with different levels of skills. Just point, click and voila! Data transformed.

    However, before you start your transformation journey, make sure your systems are ready for the ride. On-premise tools live on-site, so you'll need enough storage and the right setup to keep them running smoothly.

  • Cloud-based Data Transformation

    This transformation works on a pay-as-you-go basis using the SaaS model. With just a click, you can scale up or down as your Data Transformation needs change. No wonder these tools are so popular!

    There's a catch though: storing and handling crucial business data on third-party cloud services can rightly make you nervous. Don't worry though, cloud-based transformations are encrypted so you can transform your data without any threats to data security.

  • Manual Transformation

    Manual Data Transformations are like solving a jigsaw puzzle. It takes a dedicated puzzle master to get the job done. Solving this puzzle can take a long, especially when you're dealing with different file formats. Also, the risk of making a mistake is pretty high. It's like accidentally losing a piece of the puzzle and being unable to solve it. However, these methods offer higher control over the process.


These were the types of Data Transformations that your business can use according to the various needs and requirements. With that done, let’s see what advantages it has!
 

How Data Transformation Benefits Your Business?


Did you know that 70% of organizations are working on Digital Transformation plans or already have started? This transformation also includes Data Transformation, which has several advantages, such as:
 
  • Improve The Quality Of Data

    Data Transformation streamlines data processes, removes duplicates and fixes issues such as missing values or outliers. It also standardizes the structure of the data to ensure compatibility with the destination system for easy querying and accessibility.

  • Wider Data Access

    Data Transformation can maximize your data usage across the enterprise as it standardizes the data format, making it more user-friendly and ready for employees to get the most out of it.

  • Better Data Unification

    Transforming your data to be stored in one place is like having all your LEGO blocks in one neat and tidy stack. No more searching through multiple databases to find what you need. With Data Transformation, all your records will be in one place, all neat and tidy in the same format.

  • Answer Queries Faster

    With properly standardized and stored data, querying and analysis tools can reach warp speed. No more slow and frustrating data searches. Just seamless and speedy insights!

  • Simple Data Governance

    Data Transformation can whip all your scattered data sources into shape by generating sweet, sweet metadata. With this, you'll know exactly which data is sensitive and deserves a VIP pass for regulation. Think of metadata as your trusty data sidekick, making management a breeze.


Data Transformation is good – but you already know that! What about its future? Here’s what the future holds for Data Transformation.
 

What Is The Future Of Data Transformation?


TechDogs-"What Is The Future Of Data Transformation?" A Woman Running Towards The Future
To succeed in today's digital landscape, businesses must be agile, adaptable and data-driven. That's why the latest trends in Data Transformation will help businesses implement better upgrades in advance.

Data Transformation will bring in better security of customer data. The security of data is always a top priority, especially when it comes to sensitive customer information such as addresses, credit card numbers and emails. Just one data breach can result in a permanent loss of customer trust, along with potential reputational damage, revenue loss or legal action. To guard against these risks, companies with vast amounts of data can adopt a "zero trust model" to help keep customer information safe and secure.

Organizations are ramping up their digital transformation efforts as well by automating more of their processes. Automation is the driving force behind this change, leading to a complete digital transformation of the organization. According to a study by the World Economic Forum, 80% of CEOs are rapidly adopting new technologies and digitizing work processes. This trend is also evident in cybersecurity, where automated Data Transformation will play a key role.
 

Summing It Up


Now you see how Data Transformation can convert raw data into useful information by extracting, structuring and making data more accessible. This surely makes data analysis a lot easier and helps in better decision-making for businesses. So, the next time you hear about Data Transformation, think of it as a powerful tool for turning chaos into clarity!

Frequently Asked Questions

What Is Data Transformation?


Data Transformation is akin to assembling building blocks or LEGOs. It involves taking raw data and systematically converting it into a more structured and useful format. Think of it as starting with a pile of bricks and gradually constructing a complex and visually appealing skyscraper. This process enhances the data's organization and usability, making it easier to work with and derive insights from. Whether it's data wrangling, warehousing, or integration, Data Transformation serves as the foundation for making sense of vast datasets.

Where Did Data Transformation Come From?


The evolution of Data Transformation began roughly two decades ago with the emergence of SQL loaders in commercial databases. Initially, transformation was limited to basic one-to-one changes using traditional PL/SQL functions. However, the landscape evolved with the introduction of tools supporting Extract, Transform, Load (ETL) processes, offered by companies like Informatica, Pentaho, and Talend. These tools marked a significant advancement, albeit with limitations in reusability. To address these limitations, modularization was adopted, allowing for the creation of metadata-driven reusable components. Subsequently, advancements in technology, including massively parallel processing (MPP) systems and Hadoop-based distributed computing, revolutionized data transformation. These systems enabled the processing and storage of large datasets with enhanced efficiency and scalability.

How Does Data Transformation Work?


Data Transformation is a methodical process that involves several key steps. Firstly, it begins with the discovery of data, where structured and unstructured data sources are explored to understand their characteristics. Next, data analysis is performed to define transformation rules and map out how individual fields will be transformed. Subsequently, code generation translates these rules into executable code, typically in languages such as SQL, Python, or R. The generated code is then executed to transform the data according to the defined rules. This transformation may occur automatically within the tool or require manual intervention by developers. Finally, the output data is reviewed to ensure it meets the desired requirements. This iterative process ensures that raw data is refined into a usable and valuable asset for the business.

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