How We Manage Massive Extraction While Ensuring Data Quality
In tandem with the growth of goods and services that depend on data to function, there is an increase in the need for high-quality data. Even if the amount and caliber of information on the web are growing, it is still difficult for most firms to extract data in a clear, useable style. We’ve been in the web data extraction business long enough to have identified the best procedures and strategies that would guarantee reliable data from the internet.
At HIR infotech, we make sure that the information is organize in a well manner, of high quality, and easy for all users to find and use. Here is how we keep the quality of zettabytes of data for many clients from around the world.
Manual QA Method:
1. Crawler Evaluation
Each online data extraction project begins with the configuration of the crawler. Here, the stability and quality of the crawler code are of utmost importance since they will directly affect the caliber of the data. Our tech team members, who have extensive technical knowledge and expertise, program the crawlers. Once the crawler has been created, two peers analyze the code to confirm that the best extraction strategy is being utilized and to check for any inherent bugs. The crawler is then installed on our dedicated servers when this is finished.
2. Data Review
When the crawler is first launched, the first set of data begins to arrive. Before the setup is complete, this data is personally reviewed by the engineering team and then by one of our business representatives. This meticulous manual quality check eliminates any potential problems with the crawler or its interactions with the website. Before the setup is declared complete, any faults are checked for and fixed in the crawler.
1. Errors in Data Validation
There is a specific value type for each data piece. The data point “Price,” for instance, will always have a number value rather than words. Class name inconsistencies that occur when a website updates may lead the crawler to retrieve the incorrect data for a particular field. The data monitoring system will verify that each data point corresponds to its corresponding value type simultaneously. The system quickly notifies the team members working on that project whenever an inconsistency is discovered and the problem is swiftly resolved.
2. Volume-Based Variations
There may be instances where the number of records dramatically decreases or fluctuates. In terms of web crawling, this is a warning indicator. At the same time the monitoring system will already have the anticipated number of records for various projects. The technology promptly notifies users if it discovers any discrepancies in the data quantities.
It is acknowledged that web crawling requires high-performance machines and is a resource-intensive procedure. The quality of the servers will affect how smoothly the crawling proceeds, which in turn affects the quality of the data. We install and execute our crawlers on high-end servers because of our personal experience with this. This aids in preventing situations when crawlers malfunction as a result of a server’s severe workload.
The material that was first crawled can contain extraneous items like HTML tags. In that regard, these data may be described as rough. Our data cleansing system does a remarkable job of removing these components and properly cleaning the data. The result is pure data free of any undesirable components.
Frequently asked question:
How you can manage data quality?
A context-specific strategy for enhancing the suitability of data used for analysis and decision-making is provided by data quality management. Utilizing a variety of techniques and technologies on progressively greater and more intricate data sets, the objective is to derive insights into the state of that data.
Why is data extraction important?
Data extraction and study quality evaluation are related since they are frequently done at the same time. Standardized data extraction forms can improve validity and reliability, reduce bias, and offer uniformity for systematic reviews.
What is a method of extraction?
The initial stage in separating the desired natural products from the base materials is extraction. However, according to the extraction principle, there are several extraction procedures, including solvent extraction, distillation, pressing, and sublimation. The technique with the most usage is solvent extraction.
What is data extraction in big data?
Data extraction is the process of collecting or extracting data from several sources, so far, many of which may be unstructured.
At Hir Infotech, we know that every dollar you spend on your business is an investment, and when you don’t get a return on that investment, it’s money down the drain. To ensure that we’re the right business with you before you spend a single dollar, and to make working with us as easy as possible, we offer free quotes for your project.