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Data Wrangling vs ETL
Data Science

ETL or Data Wrangling? Which One Should You Use?

10 minute read | June 23, 2023
Sakshi Gupta

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Sakshi Gupta

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Today’s businesses have massive amounts of data available to them. This includes data that is produced both publicly (social media, blog posts, etc) and privately from their own operations. All of this data is massively valuable, as it can be analyzed to produce valuable insights that can drive profits.

The problem is that these complex data pipelines don’t deliver data in a structured format that can be easily analyzed. That’s why data from these raw data sources is put through a data preparation process prior to analysis.

Data wrangling and ETL (extract, transform, and load) are the two most popular techniques that are used for the data preparation and cleaning process. In this article, we’ll dive deeper into the complete process behind these techniques and the major differences between them.

What Is the Difference Between ETL and Data Wrangling?

To put it simply, data wrangling refers to the process of extracting data from a source and converting it into a format that’s amenable to analysis. ETL, on the other hand, involves a transformation process to prepare data and then an integration process to load it into a data warehouse.

ETL vs. Data Wrangling: An Overview

Here are the major differences between ETL and data wrangling.


Below, we’ll cover what ETL is, what the process looks like, the tools you’ll need for ETL, and some examples of it too.

What Is ETL?

ETL is used when you need to stitch data together from multiple sources and bring it into a centralized location like a target database. Let’s take a look at the individual terms to get a better understanding of them.

What Does the ETL Process Look Like?

The ETL process involves unorganized complex data sets and refining them so that they can be placed in a data warehouse. An added advantage of the ETL approach is that it becomes easy to move data around for different steps of the analytics initiative.

Extraction Process

The ETL process starts, of course, at the extraction step. Various data sources are scouted to produce data for the initial dataset. That includes sales and marketing applications, CRM systems, social media sites, mobile applications, and so on. The data is sourced from those places using various ETL tools.

Transformation Process

The transformation step involves applying various rules to ensure that undesirable data are eliminated from the complex datasets being used for the project. Here are a few ways that data is transformed.

  • Cleansing: Removing inconsistent and erroneous data values
  • Deduplication: Eliminating duplicate values that have crept into the dataset
  • Standardization: Formatting the data in a standard manner based on specific rules
  • Sorting: Applying sorting procedures to organize the data
Loading Process

The final step in the ETL process is loading. There are two main kinds of loading that are carried out as part of the loading process.

Full Loading

This involves taking the entire dataset from the transformation step’s current data volumes and placing it in a data warehouse. There are very few cases where this is done because fully loading data results in very large datasets.

Incremental Loading

With incremental loading, all of the data coming from the transformation step is compared with what’s already in the data warehouse. Only the new and unique data values are placed in the warehouse and the rest of the data is eliminated. This makes the dataset from the incremental loading process a lot more tractable.

ETL Tools

Data scientists can write code to manually carry out the ETL process. However, that is a painstaking process and one that can easily be bypassed by taking advantage of various ETL tools. Here are a few that are worth your consideration.

IBM DataStage

IBM DataStage focuses on making it easy to construct robust data pipelines for different stages of the ETL process. It is built on RedHat and OpenShift. The tool is popular because it uses an intelligent system to orchestrate data movements in a distributed development environment.

Oracle Data Integrator

Oracle Data Integrator makes it easy to carry out data integrations in the ETL process. The tool easily combines with the Oracle Warehouse Builder (OWB). So if you’re already in the Oracle ecosystem, then this is the tool that you should go with.

No-code tools are becoming more popular by the day and is a popular choice for that reason. This is an ETL tool that polls your data sources every 60 seconds and channels data over a REST API. You can source data from over 150 sources in, including Salesforce, Shopify, and even Snapchat.

ETL Examples

Say that an e-commerce website needs to source data from enterprise resource planning (ERP) software. Doing this manually would be a huge time sink which could also lead to various errors creeping in. Putting an ETL tool in place to source data from the ERP and transform it can make things a lot easier. The resulting data would be placed in the database of the e-commerce website.

Another common example of an ETL in action is when data needs to be migrated from a legacy system to a new system. This is often the case with banks and government software, where legacy tools are used for prolonged periods. When new systems are brought into place, ETL tools are used to migrate the data from the old system.

Data Wrangling

Below, we’ll cover what data wrangling is, what the process looks like, the tools you’ll need for it, and some examples of too.

What Is Data Wrangling?

Data wrangling is the process of taking raw, complex data structures and converting them into data volumes that are amenable to the data analytics process. The data is transformed in various ways, including removing errors and combining data from disparate sources.

It’s often said that data scientists spend about 80% of their work time cleaning and organizing data. There is clearly a lot of effort that goes into the process of cleaning data into a state where it can be used for analysis. Data wrangling makes exactly that possible and increasingly, with greater efficiency.

Here are some of the other benefits of data wrangling:

  • Enhances data usability across the analytics process
  • Makes it simple to construct data flows
  • Integrates information coming in from a variety of sources efficiently

What Does the Data Wrangling Process Look Like?

The following are the steps involved in the data wrangling process.


You can’t work with a dataset that you don’t understand well. The discovery process is when you learn about important things like the size of the dataset, the different data formats it houses, and any recurring issues (like duplicates in dataset).


Most complex datasets house unstructured data. It needs to be taken through a structuring process so that it is well-organized. That includes things like data being organized in columns and rows and being labeled accurately.


The cleaning process is when you eliminate outliers, remedy errors and verify that all of the data present in complex datasets are relevant. It ensures that any data that could skew your results is taken out of the picture.


The enriching step adds context to the data after the cleaning process. Techniques like downsample and auguring are used to enrich the data so that a data analyst can easily understand and process the data.


The data wrangling process starts off with a specific vision of what the resulting dataset should look like. The validation step is when you verify that the dataset does in fact look like you envisioned. You would also ensure that the data meets absolute standards such as consistency and accuracy.

Data Wrangling Tools

Here are a few popular data wrangling tools that you should check out.


Talend is a complete data management tool with powerful data wrangling features. It is highly flexible since it can be deployed both on-premises and in cloud environments. Companies as large as AstraZeneca, Lenovo, and Dominos use Talend, so you can trust that you’re working with a tool that can handle large datasets.

Altair Monarch

Altair Monarch is a data wrangling tool that can even extract data from notoriously tough sources like text reports and PDFs. You can use this tool to provide specific rules based on which you want to transform the data and then channel it into an SQL database.


Trifacta is a data wrangling tool that’s very easy to work with because of its visual approach to the process. But don’t let the approachable interface fool you; this is a powerful software that can profile data quickly and build pipelines to data warehouses. It supports integration with cloud platforms and API-based systems.

Data Wrangling Examples

Data wrangling is commonly used when data from multiple sources need to be collected for analysis. Let’s consider an example where you have order data coming in from sources such as your own website, shopping platforms like Amazon, and white-label vendors. This is a case where data wrangling would help you integrate data from all those different sources so that you can manage orders effectively.

Another instance where data wrangling is used is to eliminate erroneous or outlier data entries. This is especially useful in data analysis, where having outliers can skew your conclusions.

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ETL vs Data Wrangling: The Key Differences

While data preparation and ETL may seem similar based on their definitions, there are some key differences between the two.

Target Users

While data preparation is made for business analysts, ETL tools are aimed towards IT professionals. Data preparation tools are based on the idea that those who know data the best (analysts) should be the ones prepping it too. Organizations can’t expect to gain accurate analytics if data preparation is handled by only a few highly technical employees.

Mapping-Based Process vs. Visualization

ETL tools are designed for IT teams to effectively handle well-defined data wrangling and business intelligence processes. But these mapping-based processes make it difficult to manage iterative and agile data preparation as well as exploration.

On the contrary, data wrangling or data preparation is powered by machine learning and HCI (human-computer interaction), which allows business users to seamlessly explore and prepare data. Data preparation solutions also offer powerful visualizations to make it easier for users to identify hidden patterns in data and make accurate business decisions.

Support for Complex Data

As companies work with an increasing amount of data, and the complexity of data grows, there is a need for more sophisticated tools that can keep up with its complex nature. An ETL system is only effective when the data you have is structured, regularly updated, and batch-oriented. ETL systems start faltering when they are handling time-sensitive streaming data unless you can modify the system with custom programming. But even after tweaks, an ETL system can struggle to maintain a high availability and low latency.

While there are many commercially viable ETL tools with the capability of handling complex data, they still have longer learning curves and require extra process implementations in order to make the data usable before it can be loaded. Also, it’s important to note that ETL technology was never designed to be put into the hands of business analysts—it was originally designed for IT professionals.

Data preparation tools can handle complex data seamlessly with no extra tweaks, and their short learning curve and easy-to-use interface allow business users to prep and analyze data easily. While there are clear differences between ETL and data preparation tools, the right choice between them will depend on your business’s unique requirements and end-users.

Use Cases

Data wrangling and ETL come to the fore in different kinds of situations and projects. Data wrangling becomes especially relevant when the project is exploratory in nature. This usually happens when teams are working with new datasets and want to figure out how they can be made useful in an analytical context.

ETL is recommended more commonly in situations that relate to business intelligence and IT. These are not exploratory use cases; rather, there is a clear goal and the data professionals know exactly what they’re trying to achieve. This is why ETL is used quite often for applications like migrations or business reporting.

ETL vs. Data Wrangling: Which One Should You Use?

Here are a few final thoughts on how to decide whether you should use ETL or data wrangling for a project.

When Should You Use ETL?

ETL is perfect when you have structured data that you might want to transform in a particular way and load into a new data repository. It is easy for teams with proficiency in building pipelines to carry out ETL projects. It’s also important to note that ETL tools work especially well in instances where structured data is available.

When Should You Use Data Wrangling?

Data wrangling is a useful process to go through if you’re exploring how to use a particular data set in your analysis project. Whereas IT professionals use ETL more, business managers and data analysts are able to use data wrangling to uncover valuable business insights using data wrangling.

ETL vs. Data Wrangling FAQs

We’ve got the answers to your most frequently asked questions.

How Do I Learn Data Wrangling?

Data wrangling is a complex process that involves the use of various data-handling systems. You can start off learning Python and focus especially on libraries that handle data. You should also study database systems and be able to work with a query language such as SQL.

What Is the Difference Between Data Wrangling and Data Cleaning?

Data cleaning is a process that focuses narrowly on eliminating inaccurate data from a dataset. Data wrangling is a more wide-ranging activity that involves transforming data, formatting it, and converting it into a state where it can be used for data analysis.

Is ETL the Same Thing As Data Mining?

ETL and data mining are not the same thing. Data mining is simply the process of sourcing data that can serve as the raw material for data analysis. ETL, on the other, goes beyond sourcing data. It also deals with transforming data and loading it into a data warehouse so that it can be easily channeled through different steps of the analysis process.

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!

About Sakshi Gupta

Sakshi is a Managing Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer with experience in the Indian and US markets.