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Big Data Explained: 5 Steps to Collect Data

Tuesday, March 22, 2022

Big data is a term that describes diverse and large sets of structured and unstructured data. This data is so voluminous and fast-paced that had made it hard to manage and extract with traditional data processing software.

 

Big data aims to solve questions that have not been answered before by leveraging new technologies such as artificial intelligence, machine learning, and more. For businesses, the data that inundates on a day-to-day basis are goldmines for new insights that take it to the next level.  

 

Without a doubt, there are numerous ways to implement data collection which lays the foundation for all the work ahead. Each of the data collection approaches has its cons and pros but they all share something in common and if you are just about to kick it off it's worthwhile to check them out.  

 

 

5 Steps to Collect Big Data

Raw and random data by itself is nothing of value. Messy data doesn't tell us anything new or meaningful. Big data creates great value for businesses and enterprises by harnessing well-structured (ready to be analyzed by software), cleaned (unwanted parts are well-trimmed), and validated data. 

 

Step 1: Gather data

There are many ways to gather data according to different purposes. For example, you can buy data from Data-as-Service companies or use a data collection tool to gather data from websites. 

 

Step 2: Store data

After gathering the data, you can put the data into databases or storage for further processing. Usually, this step requires investment in physical servers as well as cloud services. Some data collection tools come with cloud storage after data is gathered, which greatly saves local resources and makes data easy to access from anywhere.

 

Step 3: Clean data

Data cleaning is important for effective data analytics. Since there may be noisy information you don’t need, you need to pick up the one that meets your needs. This step is to sort the data, including cleaning up, concatenating, and merging the data. 

 

Step 4: Reorganize data

You need to reorganize the data after cleaning it up for further use. Usually, you need to turn the unstructured or semi-unstructured formats into structured formats like Hadoop and HDFS. 

 

Step 5: Verify data

To make sure the data you get is right and makes sense, you need to verify the data. Test with samples of data to see whether it works. Make sure that you are in the right direction so you can apply these techniques to manage your source data.

 

 

Big Data Collection Tools

These are the general steps to collect the data required for big data analytics. However, collecting the data, analyzing it, and gleaning insights into markets is not as easy as it seems. Data collection tools like Octoparse help make this process so much easier. They allow users to gather clean and structured data automatically so there is no need to clean it up or reorganize it. After the data is collected, it can be stored in cloud databases, which can be accessed anytime from anywhere. If you haven't used data extraction tools, try it for free now.

 

2022 Updated

 

Related resources

Big Data: What Is Web Scraping

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10 Best Universities for Big Data Analytics and Data Science

Web Scraping in the Big Data Solution

Video:How to Scrape Websites Without Getting Blacklisted or Blocked

 

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