What are the best data wrangling tools? What are their pros and cons? Read on to find out.
One of the first skills that any data professional learns is data wrangling. An umbrella term, it’s often used to describe the early stages of the data analytics process. It captures everything from data collection and exploratory data analysis (EDA) to validation, storage, and more. Heard of data cleaning and data mining, too? These are both subsets of data wrangling.
But if you’re new to the field, what data wrangling tools are available to help you? In this post, we highlight a handful of tools you might come across during your early excursions into data. We’ll start with a quick summary of what data wrangling involves (but you can click straight through to the tools if you prefer):
What is data wrangling?
Data wrangling is a commonly used catch-all to describe the early stages of the data analytics process. Somewhat confusingly, the term is also sometimes used to describe the individual steps in this process, too. For instance, you might hear it used synonymously with activities like data mining or data cleaning. In reality, the latter two are actually subsets of data wrangling…something to keep in mind!
So what does data wrangling involve? While the order and number of tasks will differ depending on the dataset, common key steps include:
- Collecting data: The first step is to decide which data you need, where to extract it from, and then, of course, to collect it (or scrape it).
- Exploratory data analysis: Carrying out an initial analysis helps summarize a dataset’s core features and defines its structure (or lack of one). Learn more about exploratory data analysis here.
- Structuring the data: Most raw data is unstructured and text-heavy. You’ll need to parse your data (break it down into its syntactic components) and transform it into a more user-friendly format.
- Data cleaning: Once your data has some structure, it needs cleaning. This involves removing errors, duplicate values, unwanted outliers, and so on.
- Enriching: Next you’ll need to enhance your data, either by filling in missing values or by merging it with additional sources to accumulate additional data points.
- Validation: Then you’ll need to check that your data meets all your requirements and that you’ve properly carried out all the previous steps. This commonly involves using tools like Python.
- Storing the data: Finally, store and publish your data in a dedicated architecture, database, or warehouse so it is accessible to end-users, whoever they might be.
You can learn more about data wrangling in this post.
While all data analysts require the ability to manipulate data manually (using tools like MS Excel and Python) many data wrangling processes can now be automated. There’s an increasing number of proprietary tools available to make data wrangling more accessible to beginner analysts or non-experts. Let’s look at a handful of these now.
What are the best tools for data wrangling?
One of the first steps in the data analytics process is data collection. This is often done on the web. If you’re a total beginner or are still grappling with Python, Parsehub provides a useful alternative. This web scraping and data extraction tool offers an easy-to-use desktop interface that extracts data from a wide range of interactive websites. Code-free, you can simply click on the data you would like to collect and extract and export this into JSON, Excel spreadsheet, or API formats. Parsehub’s main selling point for beginners is that it has a graphical user interface. This offers similar functionality to other web scraping tools, like import.io, but at a lower price.
As a standardized tool, Parsehub does have limitations, though. For instance, many websites have anti-scraping protocols, which Parsehub can’t get around. It’s not ideal for larger projects, either, although its customer support can help solve most issues. In short, if you’re new to web scraping, Parsehub is great for getting to grips with the fundamentals. But once you’ve done that, you might want to progress to something more sophisticated, such as…
Another popular web scraping tool, Scrapy is more complex than code-free tools like Parsehub. The trade-off is that it’s much more versatile. An open-source web scraping framework built in Python, it’s also completely free to use. Scrapy is fast and scalable, making it suitable for projects of any size. And because it’s open-source, it has a great repository on GitHub. This is stuffed with all the code you could need to alter its functionality and remove or extend its modules in a vast variety of ways.
Scrapy’s main drawback is its learning curve. Because it’s written in Python, you’ll need a solid grasp of the language before using it. And while its functionality is wide-ranging, a drawback of this is that you’ll need to learn what each module can do before deciding whether or not to use it! If you’re committed to an open-source web scraping tool and find Scrapy too difficult, alternatives are available. Another Python package that you may find easier to get to grips with is BeautifulSoup. Check that one out, too!
Talend is a suite of tools for various data wrangling, data prep, and data cleaning activities. An enterprise-friendly, browser-based platform, it uses a straightforward point and click interface. This makes data wrangling much easier than it would be using heavily code-based packages. However, if you prefer coding from scratch, rather than using the inbuilt extract, transform, load (ETL) functionality, this is also possible. This makes it a good option for those who want to build their expertise as they go.
Functionality-wise, Talend lets you apply rules to all kinds of datasets, saving these and sharing them across teams. It also comes with in-built processes for tasks like enrichment and integration, and interoperates with a variety of other common enterprise platforms.
One drawback of Talend is that its machine learning functionality is not always on top form. This means more sophisticated tasks like fuzzy matching (identifying matching data points that don’t match 100%) can be less than ideal. With such a lot of functionality, it’s also high on memory consumption and can be a bit buggy at times. In general, though, these pitfalls are balanced by the level of data manipulation that even a novice can achieve using the platform.
4. Alteryx APA Platform
The Alteryx APAP platform not only provides tools for the data wrangling process but extends to broader data analytics and data science requirements, too. This is great if you want everything in one location. In terms of data wrangling, Alteryx offers over 100 pre-existing tools, covering everything from data profiling and find-and-replace to fuzzy matching. One of its stand-out features, though, is the sheer number of sources it works with—all without any reduction speed. You can extract data from practically any kind of spreadsheet or file, platforms like Salesforce, third-party sites, social media, mobile apps, or census databases. You name it!
Alteryx also handles these data sources far more readily than tools like MS Excel, which tend to slow down with very large datasets. It can also export your data into any kind of system and integrates well with other data wrangling tools like Tableau (see number seven on our list). This makes producing great data visualizations much more straightforward.
On the downside, Alteryx’s drag and drop interface sometimes complicates matters—every step in the process has to be incorporated into the visual workflow. The interface is generally a bit tired-looking, which is a shame because this doesn’t reflect the power of the platform. By far the biggest pinch point, though, is the price. It has an expensive license-based payment plan, meaning a fee for each user. And there’s no free trial version—oops! So, while Alteryx remains a powerful tool, open-source alternatives offer much of the same functionality, as long as you’re happy to bypass the convenience of unifying everything in one place.
5. Altair Monarch
Altair Monarch is another data preparation platform that transforms complex, unstructured data into a more usable format. It claims to be able to extract data from any source, including difficult and unstructured formats like PDFs and text-based reports. It then transforms these according to your chosen rules, before inputting data directly into your SQL database. Notably, the platform has a lot of in-built tools geared specifically towards the reporting regulations of the accounting and healthcare industries. It’s very popular in these sectors.
Altair Monarch started life as a simpler tool but has since evolved much more functionality. While this is great if you have complex needs, it’s made the tool less user-friendly for those with less intensive requirements. Most users now require training before using it. The additional functionality can also make it a bit laggy with larger datasets, and its PDF import function is glitchier than maybe they like to admit. Besides all this, it’s a pretty solid tool.
6. Microsoft Power Query
As the creator of one of the most popular electronic data manipulation tools, MS Excel, we couldn’t leave Microsoft off our list! While Microsoft offers a whole suite of tools, and for data wrangling, MS Power Query stands out. It offers much of the same ETL functionality as the other tools on our list. But what sets Power Query apart is that it is built directly into MS Excel. This makes it the perfect next step for those with advanced Excel skills who want to take things to the next level.
Ultimately, what makes Power Query so useful is also perhaps its biggest flaw: the fact that it’s a Microsoft product. While it’s a solid tool, its key selling point is that it integrates with other Microsoft tools like Power BI (their data viz tool) and Power Automate (their workflow software). The fact that it requires no coding is a benefit, but if you don’t use any of their other software then there might be better options available.
7. Tableau Desktop
We’ve covered web scraping and transformation tools, but we couldn’t finish this list without at least one data visualization tool. Enter Tableau Desktop. Tableau offers a range of striking visualizations, from treemaps and Gantt charts to histograms and motion charts. We must stress that it’s not primarily a data pre-processing tool, but it does have some inbuilt data prepping and cleaning features that help produce the snazzy visualizations that it’s famed for.
The data preview pane lets you view a dataset’s core features at a glance. And the data interpreter allows you to identify columns, headings, and rows. Among other functionality, you can also split string values into multiple columns, or group continuous values into sections (known as bins) which helps for creating histograms.
While Tableau’s data prep functionality is not as wide-ranging as other wrangling tools, it’s perfect for preparing data for solid visualizations. It also supports unstructured data. However, you might find it useful to tidy this first using something like Alteryx or Python. Overall, Tableau creates visually gripping and interactive reports and dashboards. This makes the whole process of data wrangling a lot clearer (and frankly, more fun, too!)
Related reading: What is Tableau and how is it used by data analysts?
Wrap up and further reading
In this post, we’ve explored a small handful of the many data wrangling tools you might encounter in your career. From free, open-source software like the Scrapy Python package, to enterprise solutions like Talend, you’ll find that there are pros and cons for each. Ultimately, each tool is suited to individual preferences, use cases, and organizations. As you explore the options out there, ask yourself: which tool is right for me?
If you enjoyed this post, dive deeper into data analytics with this free, 5-day data analytics short course, and check out the following for more data-related topics: