Data Source : A Complete Guide


Analyzing Survey Data2
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In data analytics, the word data source often comes up in relation to working with data and databases. But what exactly is it? A data source refers to where you got your data from. It is a system that retrieves data from an external source and makes it available to an application or simply, a place where you get data.

A data source can be anything from a small spreadsheet to an entire online database. In fact, the term data source encompasses anything that gives access to data! It provides information or raw material that can be used to create charts, graphs, and reports—anything visual on the report page will use data from one or more of the data sources.

This definition seems pretty simple, but actually, this definition does not distinguish different kinds of data sources and it doesn’t describe what kinds of data sources exist or how they work. To make the definition clear, in this article, we’ll try to answer the question – what is a Data Source?

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What is a data source?

The foundation of data analytics is data. Nothing can be done without data. A data source is a place where data originates. It is an interface for capturing, storing, accessing, and sometimes modifying information. 

A data source is typically used as the input for analysis. Whether you’re looking to scrape data from the web, take it from an API, or import it manually into your computer, there are many ways to get the data.

For example, when you’re using Google Analytics to track your website traffic, Google Analytics is the data source.

A data source is where you store information related to your business, such as customer contact details, customer purchases, and customer preferences. It’s an essential part of any business that depends on data to make money, be it online or offline.

Types of data source

Most data sources can be classified into two categories: Machine data sources and File data sources.

Machine Data Sources

Each physical machine such as sensors, mobile devices, and the Internet of Things (IoT) has its own set of machine data sources. Many machine data sources are saved in the Windows Registry on a single desktop. These sources cannot be moved from one machine to another.

They cannot be shared easily. To trigger the connection or query the data, users merely need to use the Data service name (DSN) as a shortcut. Machine data sources are usually stored in a database and are typically structured.

File Data Sources 

In File Data Sources the data is stored in separate text files. They aren’t exclusive to each computer and are shared across many devices. The name of the file data sources is not assigned by the user as these files are not assigned to a single user. File data sources do not have a data source name (DNS). 

It’s not the same file that we see on the desktop. For instance, An Excel sheet can be used as a data source for self-service apps, but it is not a machine or file data source. These are two different approaches. 

A .dsn file can be unshareable as well. A single machine can have an unshareable DSN file that links to a machine data source.

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How does data source work?

Data sources are basically where we get our raw data from. Once you’ve compiled all of the information, it’s time to turn it into something usable. But what actually goes into that process? 

Once data is obtained from a data source, it can be widely used in a variety of ways. Data can be transferred to websites or systems’ network protocols such as FTP (File transfer protocol), HTTP (Hypertext Transfer Protocol), an API (​​application programming interface). While transferring the data SFTP (SSH File Transfer Protocol ) can be used to Encrypt content as well as to obfuscate the username and password. 

NFS, SMB, SOAP, REST, and WebDAV are some more protocols for transferring data from sources to locations, particularly on the web. APIs frequently use these protocols.

The purpose of Data sources is to help users to move data from one place to another where data is needed and also to make the information available in a user-friendly and structured format for efficient use. 

One of the most important steps in data analytics is transforming your source data into something more usable. That’s where cleaning and preparing the data comes in and the process is called data processing. (Add link to Data Processing – What Exactly Does It Mean? Blog 21 Feb 2022)

With knowledge of what data sources are, you will know how to locate them, how to get your hands on that data, and, most importantly, how to make good use of it. The data source is where your data comes from, and knowing where you’re getting your data from can make or break your organization’s success!

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