Data Parsing: The Pros And Cons Of Buying VS. Building Your Own Software
In this article you will learn:
What is data parsing?
Data parsing is a method of converting data from one format to another format. For example, converting from HTML to JSON.
Parsing is also the next step after data extraction. Many times, the extracted data is in one format and needs to be converted to a different format in order to save it in your database. This is where the parser comes into play – by transforming a data set’s format, parsing helps businesses, data scientists and developers analyze and work with data more easily.
Why do you need data parsing?
When extracting or collecting data, the data structure is a critical variable. If the structure of the data doesn’t match the structure or desired format in the destination, the data may become hard, expensive, or timely to prepare for use. For instance, when the system you used to collect the data is missing specific fields or input information. You will need to move the data to another system that is compatible to be able to start structuring and working with it. Humans also tend to make mistakes when entering information such as inputting incorrect fields leading to data set inaccuracies. A data parser can help eliminate these types of human or machine-generated errors before your DevOps team starts working on them.
Looking for a more efficient way to perform data collection?
What can you use data parsing for?
As the need for data grows, the applications of data parsing are growing, as more companies discover it is inseparable from their data collection and analytics ‘production line’. Some common use cases may include:
- Marketing: Compiling a database of prospects from emails sent to an organization.
- eCommerce: Filtering and restructuring data for databases, for example, h1/h2 hierarchy of data collected on an eCom marketplace.
As well as for specific tasks or data pool characteristics including:
- Data translation: Transforming HTML files into CSV or other formats for database usage.
- Big Data: Analyzing data collected, especially useful when working with large data in large quantities.
Market research, pricing comparison, and a host of other data extraction use cases also require data parsing to actually translate collected data into a useful and actionable data set.
How a data parser works
A parser doesn’t convert every single data sequence but identifies what information in the HTML string needs to be converted. It does so according to predefined rules and commands, and then takes the target data and converts it into CSV, JSON, or any other desired format.
Data parsing is typically accomplished in two steps:
#1 Primary parsing: Here the parser allocates the desired structures of collected data.
#2 Secondary parsing: Is when the parser executes the allocated data based on the parser’s code-based instructions.
You can learn more about how to use the above commands here. Alternatively, you can opt for a paid parser software that does the hard work for you. But what are the pros and cons? The next section will help you make a better-informed decision.
Building vs buying data parsing software
Many people just starting out with parsing consider building a data parser themselves since it may be more cost-efficient and customizable. However, besides the cost, there are other factors to consider.
Pros and cons of building a data parser independently
- You can tailor the parser to your specific needs
- You control when updates and maintenance take place so that it does not interfere with your day-to-day operations
- It is relatively more cost-effective than pre-built tools
- Incurring overhead costs on developers as well as additional server costs
- Your team will need to spend precious time planning, maintaining, and testing the parser when they could be developing other crucial projects
Pros and cons of paying for a parsing solution
For some, the advantages of building a data parser independently do not justify the cons. In this case scenario, they opt to pay for a ready-made solution. Here are the main advantages and disadvantages:
- No additional overhead costs (other than the cost of the parser itself)
- The tool provider will take care of parser maintenance and updating
- Saves you and your team crucial times so that you can focus on the core of your business operations
- Not all parser tools can be customized to your specific needs
- Limited control over the parsers functions, update and maintenance times which may clash with crucial internal operations
- This option may be pricier than building it yourself
Data parsing is a method that helps turn extracted data into a format that can practically be used and integrated with your systems, websites, algorithms, and applications. This enables data scientists and companies to gain access to accurate information so that they can analyze and implement data sets quicker and more efficiently. Choosing whether buying or building a parser is the right choice for you depends on your budget, assignment frequency, manpower, and level of desired customization – take all of these into consideration before making a final decision.
Is dealing with data parsing too time-consuming for your business’s needs?