IN THIS ARTICLE
- What Is the Difference Between ETL and Data Wrangling?
- ETL vs. Data Wrangling: An Overview
- ETL vs Data Wrangling: The Key Differences
- ETL vs. Data Wrangling: Which One Should You Use?
- ETL vs. Data Wrangling FAQs
Get expert insights straight to your inbox.
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.
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.
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
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.
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.
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.
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 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 Integrate.io 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 Integrate.io, including Salesforce, Shopify, and even Snapchat.
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.
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 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.
Get To Know Other Data Science Students
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.
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.
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.