Data source is defined as the location where the data is coming from. This could be a database, a social media platform, or another online source. The data source can also be offline, such as an Excel spreadsheet.
When talking about data sources outside of the text analytics industry, the term can take on a different meaning. For example, in business intelligence, a data source might refer to the software that collects and stores the data. In marketing, a data source might be the company that provides customer data.
It’s important to understand the context in which the term data source is being used in order to avoid confusion. Below, we will compare data source to similar terms that are used in different industries.
Data Source Format
When choosing a data source, it’s important to consider the following:
- the format of the data (structured, unstructured, or both)
- the size of the data (small, medium, or large)
- the source of the data (internal, external, or both)
- the frequency of updates (static, real-time, or both)
The next step when you have chosen a data source is to connect to it so that you can start extracting data. This is usually done through an API or a web scraping tool. API (Application Programming Interface) is a set of protocols and routines for accessing a web-based software application. Web scraping is the process of extracting data from websites. It can be done manually, but it is more commonly done with the help of a tool.
In order to choose the right tool for the job, you need to think about what you want to achieve. Do you want to create a dashboard? Do you want to conduct some statistical analysis? Once you know what you want to do, you can choose the right tool for the job.
Common Data Sources are :
- databases (MySQL, MongoDB, PostgreSQL, etc.)
- social media platforms (Twitter, Facebook, LinkedIn, etc.)
- online sources (websites, forums, news outlets, etc.)
- offline sources (Excel spreadsheets, CSV files, JSON files)
The Next Step When Data Sources Are Identified
When it comes to data analysis, it’s important to think about what you want to achieve. Do you want to create a dashboard? Do you want to conduct some statistical analysis? Once you know what you want to do, you can choose the right tool for the job.