It’s impossible to get good outcomes using low-quality data. Data wrangling aims to eliminate faulty or incomplete data before analysis. By cleaning up and augmenting existing data prior to publication, business teams can make better, more-informed decisions. In this article, we explore the process of data wrangling and how Snowflake empowers developers to easily deploy custom data wrangling workflows in their cloud data warehouse.
What Is Data Wrangling?
Data wrangling is the process of correcting, cleaning up, and/or removing inaccurate data from a data set and then structuring and augmenting it to ensure that only high-quality, complete data is used for analysis. In practice, data wrangling may involve merging data from several different sources, removing data not relevant to what’s being analyzed, and identifying and addressing potential gaps in the data. Data wrangling is also known as data munging and data remediation.
Data wrangling has become increasingly important as the volume of data that organizations collect rapidly expands. Businesses now collect data from diverse sources, including SaaS applications, spreadsheets, websites, and IoT devices, and often store it in unstructured or semi-structured data formats. These trends have increased the need for more-robust, automated tools capable of wrangling massive data sets quickly. Modern data wrangling applications ensure business teams are able to take advantage of time-sensitive business opportunities that require data-informed decision-making.
Steps Involved in Data Wrangling
Although the data wrangling process is customized based on the data’s intended use, most projects follow a similar framework. Here’s the journey that raw data must traverse before it’s ready for decision-makers to analyze.
The first step for any data wrangling project is to gain a better understanding of the data you’re working with. Familiarizing yourself with what’s available will make it easier to plan how to put the data to best use. For example, if you’re planning to work with online sales data, gathering purchase history data and customer location data will be essential and will inform your data wrangling approach. At this stage, you may notice where data is missing or incomplete.
Before it can be useful, raw data must be transformed. The exact form it takes will depend on the type of data you’re working with and the analytical model you’re using. When data transformation occurs will also vary depending on your workflow and the tools you’re using.
The data cleaning stage is one of the most important parts of the data wrangling process. Cleaning the data ensures that it doesn’t contain errors or misformatted values and that it isn’t missing information that may result in less-accurate analysis. Cleaning data involves removing or filling missing cells or rows, standardizing input variations, and removing or noting outliers that can skew the data in misleading ways. The cleaner the data, the more accurate your final analysis and resulting business decisions will be.
Now that your initial data has been cleaned and transformed into an accurate and usable format, it’s time to decide if the data you have is sufficient or if you need to pull in additional data from other sources. Depending on what you’re seeking to accomplish, you may determine that pulling data from other sources would provide more depth or clarity. If you do decide to supplement your existing data, you’ll need to clean and transform the new data before adding it to the data you’ve already prepared.
Data validation ensures that the data is high-quality, consistent, and secure enough to be released for publication and analysis. The data validation process uses repetitive data programming sequences designed to help uncover inconsistencies in the data. One example of data validation is confirming that attributes that should be uniformly distributed (such as birth dates) are as such.
At this stage, the wrangled data is ready to be shared with others. The format it's provided in depends on how it’s intended to be used and who’s accessing it. The form published data takes could include a written report or, more commonly, accessed via an analytics dashboard.
Custom Data Wrangling Workflows with Snowpark
Besides the SQL capabilities of Snowflake, when working with the extremely large and varied data sets that are common in today’s organizations, the data wrangling process requires robust tools for speed and scalability. Snowflake’s Snowpark framework allows companies to deploy custom data wrangling workflows in other languages directly on data stored in the Snowflake Data Cloud. Here’s what you can do with Snowpark.
Deploy custom code within Snowflake’s Data Cloud
Using Snowpark, developers can deploy custom code directly on data stored in Snowflake. This capability allows users to perform various information management tasks.
Support for structured, unstructured, and semi-structured data
Snowflake supports the storage of unstructured, semi-structured, and structured data, including raw data such as audio files, video, and images. Thanks to the efficiency and scalability of the cloud, large amounts of potentially valuable data can be stored and await the data wrangling process when needed.
Create ETL and ELT workflows
Snowpark makes it simpler for developers to create custom ETL and ELT workflows for data to be stored in Snowflake. Extract/transform/load (ETL), or extract/load/transform (ELT) are two separate processes of extracting data and then transforming it either before or after it’s loaded into Snowflake’s Cloud Data Warehouse.
Create workflows for data preparation
Snowpark also supports the creation of workflows for data preparation, making it easy to remove errors from data sets before transforming them into a format that’s more conducive for analysis.
Efficient Data Wrangling for Greater Agility
For agile data analysis, today’s organizations must be able to quickly and efficiently wrangle large data sets in various formats. Relying on outdated processes designed before the advent of big data, when on-premises systems were common, reduces a company’s ability to take advantage of opportunities as they arise and address risks in a timely manner. With modern data wrangling capabilities, however, organizations can be agile.
To test-drive Snowflake and explore Snowpark’s capabilities, sign up for a free trial.