There are several important skills that an analyst must have. The fundamental knowledge that all analysts must have is typically described, followed by the specializations that will distinguish an analyst.
Data Parsing is a skill that data analysts should consider developing. Unstructured data must be converted to organized data or new data before it can be used. A data parser typically performs data parsing to transform raw data into types that are easier to understand, use, or store.
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What is Data Parsing?
Data parsing involves: converting data from one format to another. They are often used in compilers when we need to read computer code and generate machine code.
This happens frequently when programmers create code that executes on hardware. SQL engines also include parsers. A SQL query is parsed by SQL engines before it is executed and the result is produced.
This usually occurs when: web scraping data is retrieved from a web page through web scraping. Once you pull data from the web, making it easier to read and better for analysis is the next step to ensuring your team can use the results correctly.
Who Will Use Data Parsing?
Data analysis, data management, and data collection greatly benefit from data parsing, which can be accomplished through APIs or libraries.
A data parser can be used to: split large data sets into manageable chunks, extract specific data from raw sources, and convert data from one format to another.
For example, a properly programmed data parser can convert data contained in an HTML website into a more readable and understandable format such as CSV.
Data parsing is regularly used in a variety of sectors, from business to higher education to education. From Big Data to e-commerce. A well-designed data parser mechanically extracts important details from raw information without the need for manual labor.
The information may be used for price comparisons, market evaluation and other purposes. Now let’s examine the operation of a data parser.
Why Should You Use a Data Parser?
A program known as a data parser converts data from one type to another. As a result, a data parser retrieves data as it expands the input and then exports the data in a new structure.
Data parsers, which can be created in various programming languages, are the basis of the data parsing procedure.
It should be noted that there are numerous tools or APIs for data parsing. To better understand how a data parser works, let’s look at an example.
The HTML processor will then:
- Take an HTML file as input.
- Examine the document’s HTML code and save it as an array.
- Get the relevant data and parse the HTML data string.
If necessary, expand, process or clean the data that interests you during parsing. Export the processed data to a JSON, CSV or YAML file or to a SQL or NoSQL database.
It is important to take into account that the way a data parser parses data and converts it to a format depends on how the parser is instructed or defined. This depends on rules provided as input variables to a parsing API or software.
A custom script example determines how the data parser is coded. In both scenarios, there is no need for human intervention and the data is processed automatically by the parser.
Let’s take a look at why data parsing is so important.
Benefits of Data Parsing
Data parsing has several advantages that apply across many industries. Let’s take a look at the top five reasons you should use data processing.
1. Affordable and Less Time Consuming
You can save a lot of time and effort by automating repetitive tasks with data parsing. Additionally, converting data into more readable types allows your team to comprehend data faster and perform their tasks more easily.
2. Greater Data Versatility
You can reuse data that has been parsed and transformed into a human-friendly version for a variety of reasons. In summary, data parsing expands the scope of your data operations.
3. High Quality Data
Often, converting data into more organized formats requires cleaning and standardizing the data. This means that data parsing improves the overall quality.
4. Simplified Data Integration
Data parsing allows you to transform data from different sources into a unique format. This allows you to combine various data sources into a single target, which can be an application, technique or procedure.
5. Advanced data analysis
Working with organized data makes it simple to review and analyze data. This also results in more in-depth and precise analysis.
Data Parsing Challenges
Dealing with data can be difficult, and data parsing is no exception. The explanation for this is that a data parser must overcome a number of challenges. Let’s look at three challenges to keep in mind.
1. Managing Inconsistencies and Errors
A data parsing process usually takes raw, unorganized or semi-structured data as input. As a result, errors, errors, and inconsistencies in the input data are likely to occur.
HTML documents are one of the most common sources of such problems. This is because most contemporary browsers are smart enough to render HTML pages properly regardless of whether they contain syntax errors or not.
As a result, your input HTML pages may contain unclosed tags, W3C-invalid HTML content, or just special HTML characters. Parse such data requires an intelligent parsing engine that can handle these issues automatically.
2. Managing large amounts of Data
Data parsing consumes effort and system resources. As a result, parsing can cause performance issues, especially when dealing with Big Data. As a result, you may need to combine your processed data to parse various input papers simultaneously and save time.
On the other hand, this can increase resource consumption and total confusion. As a result, parsing large amounts of data is a difficult task that requires the use of advanced tools.
3. Managing Various Data Formats
An effective data parser must be able to process a variety of input and output data. This is because data formats change at the same rate as the entire IT industry.
In simple terms, you must keep your data parser up to date and able to handle a variety of formats. A data parser must also be able to import and export data in multi-character encodings.
This way you will be allowed to use parsed data on macOS as well as Windows.
Comparison of Creating and Purchasing a Data Parsing Tool
As should be obvious, the effectiveness of a data parsing operation is determined by the type of parser used.
As a result, the question arises as to whether it would be preferable to let technical staff create a data parser or simply use an existing business solution like this. Bright Data is born.
Developing your own parser is more customizable but requires more time and effort, while buying one is faster but gives you fewer options. Obviously, the situation is more complicated than that.
So, let’s try to understand whether you need to develop or buy a data parser.
Creating a Data Processor
In this case, your business has an internal development team that can create a custom data parser.
Pros:
- You can modify it to meet your specific needs.
- You own the data parser code and have full authority over its development.
- If used frequently, it may be cheaper in the future than buying a ready-made product.
Cons:
- It is impossible to overlook the costs of development, program management and server hosting.
- Your dev team will need to dedicate a significant amount of time to designing, building, and maintaining it.
- Performance issues may arise, especially if the spending plan for an efficient server is limited.
There are always advantages to building a parsing tool from scratch, especially if it needs to meet particularly complex or specific requirements.
At the same time, this requires a significant amount of work and resources. As a result, you may not be able to fund it or you may not want your highly talented team to waste time developing such a tool.