What Is Data Extraction?

What is data extraction? Data extraction is exactly what it sounds like—culling through your spreadsheet to pull out specific data for a new column or spreadsheet, following the standard data entry and scraping processes. For example, if you have a dataset around population density, you may want to extract cities with more than 10,000 people per sq. mile and only those that are in California so that you can analyze the demographic makeup of those cities. Sure, if you’re working with a small dataset, you might be able to get by with copy/paste, but extracting data  by hand can quickly become overwhelming. Data extraction is most efficient when you’re able to define the data you need, but use efficient, machine learning software to retrieve it. 

It’s no surprise that many businesses use Excel as a data extraction tool to extract data. In order to extract data from Excel columns, you can use some combination of the VLOOKUP, MATCH, and INDEX functions. The VLOOKUP function is perhaps best equipped for data extraction, allowing you to look up and retrieve data from a specific column. The MATCH and INDEX function work somewhat inversely; MATCH finds the position of a given value, while INDEX retrieves the value from a known position. All three are helpful to know (and relatively easy to learn) in order to extract data from Excel. But is Excel the best system for extracting data in the modern landscape?

Challenges with extracting data

For simple, structured data, extracting data in Excel is fairly straightforward. However, as the scale and complexity of modern data grows, data extraction in Excel is becoming more challenging for users. Extensive, complicated, and unstructured data can make extracting data time consuming and challenging. Say, for example, you want to extract and analyze specific words from a series of tweets. That means you’re working with JSON data, and in order to extract data from Excel, you’ll first need to unnest it and then proceed with the steps involved in extracting data. Excel wasn’t built for JSON data or most complex data types and systems, which means data extraction not only becomes a more demanding process but a significantly longer one, too. Companies are turning to data extraction tools to combat many of these challenges. When a business needs multiple tools for every piece of the data preparation process, most management teams want another solution. But are there other solutions?

New platforms for data extraction

To more efficiently extract data with complex data types—as well as to complete other data preparation tasks—many analysts are adopting data preparation platforms. Data preparation platforms were built to handle data of any type and any size to accelerate the painstaking process of preparing data for analysis. Designer Cloud, routinely named the leader in data preparation by analysts and its extensive customer base alike, also uses machine learning to further accelerate the data preparation process, predicting a user’s next steps with his or her every interaction. Add in its visual nature, and the overall result is a data preparation experience that is interactive, intelligent, and encourages experimentation with data extraction and other data preparation tasks alike. Unlike other data extraction tools, Trifacta comes with many other data prep capabilities that prepares data for analysis.

Types of data extraction

3 Primary Ways of Data Extraction

Notification

The easiest way for data extraction is to allow for a system notification when a record has been changed.  Most platforms offer a notification mechanism or similar functions so they can support data replication.

Incremental Data Extraction

Systems that are unable to provide alerts when an update has occurred, can typically still provide an update as to which data was modified and provide data extraction from that. The one downfall to incremental data extraction is that it is unable to provide an update to deleted data since it no longer exists.

Full Data Extraction

Some platforms have no way to identify if any data has changed. In this instance, it calls for a full data extraction. This is helpful if you have to replicate whole data tables, however, full data extraction involves high data transfer volumes which can put a hold on your network. Use at your own risk.

Data Extraction in Designer Cloud

To perform data extraction in Designer Cloud, start with the “Extract” transform. This will allow you to specify the column in your dataset that contains the pattern that you want to extract, as well as tailor your transform to fit your desired extraction process.  

Beyond data extraction tools, Designer Cloud has a lot to offer, including countless data preparation capabilities and a unique, guided experience that reduces the overall time of preparing data by end up to 90%. Our tool performs more than extracting data and can save valuable time and expenses. We’d love to chat with you about your data preparation and integration needs and how you can perform data extraction in Designer Cloud. Contact us for more information. Or, try it out for yourself by signing up for the free Designer Cloud trial here.