Lost in a library of epic proportions, you desperately search for the key that unlocks your next chapter.
Mountains of information surround you, and finding the crucial piece feels like seeking a needle in a haystack.
Given this context, web scraping is essential to collect data from the vast volumes of information across the World Wide Web. But that’s not all – data extraction is just the tip of the iceberg. The objective is to have clean, relevant, and meaningful data that can deliver value to you.
You can realize the significance of data quality by looking at findings such as the one by Gartner, which states that poor data quality costs organizations an average of $12.9 million. Ensuring data quality is where the extract, transform, load (ETL) process comes in.
What is Extract, Transform, Load (ETL)?
Extract, Transform, Load (ETL) is a 3-step process starting with data extraction from multiple sources. After data extraction, you transform it to suit business needs.
In the third step, you load the transformed data into an output database or data warehouse. As a result, the output database or data warehouse then hosts consistent data that is reliable for analysis.
Extract: This is the first phase in which you extract data from various sources. An apt example is web scraping through bots operating on the vast online digital content.
Transform: This is the second phase, where you pool the data collected in staging for processing. The processing includes cleaning, handling missing values, removing duplicates, defining data integrity, transforming data types, etc.
Depending on the business requirements, you can derive specific metrics from the existing data. These metrics enable more straightforward, faster, and meaningful reporting and analysis. The transform phase is critical since it massages the data to bring it into a form relevant for analysis. It is of massive value in the case of data from web scraping – more on this topic later.
Load: This is the third phase, wherein you load the transformed data into a database, data mart, data warehouse, or other storage system. It is essential to have the correct database design to store the data for easy retrieval and meaningful analysis.
You can imagine how inefficient and unreliable the data analyses would be if you do not carry out the above 3 phases properly. The absence of proper ETL could be a real problem. However, if ETL is addressed right and well in time, it provides a single source of truth for the business.
Why is ETL essential for processing data from Web Scraping?
Web scraping is essential but can often lead to a dump of raw, unstructured information, which could be of little use. A well-designed ETL process ensures that the data is massaged and transformed into a meaningful source of insights:
- Data Integration: ETL integrates data from various sources and processes and transforms the data into a consistent shape. In the case of web scraping, you can have data pulled from multiple websites. Proper data integration ensures the data is unified, ruling out inconsistent definitions.
- Data Quality: The internet can source inaccurate and misleading data. A carefully crafted ETL design cleans up inconsistencies, removes duplicates, and handles missing data to ensure high quality.
- Automation at Scale: You can automate the entire Extract, Transform, Load process. Automation enables clean, reliable data at scale, continuously and efficiently. The business stakeholders do not have to wait hours and days if the ETL pipelines are automated to handle a constant inflow of data.
- Enhanced Analyses: ETL converts the input data into a structured format, which makes it easy to consume through queries, reports, advanced analytics, or machine learning models.
How ETL mends the Mess
To dig a little deeper, the very nature of data from web scraping makes it prone to errors. Data obtained from web scraping is unstructured, and the role of ETL in handling such data is significant:
- Sub-optimal Website Code: Most developers do not code websites well, leading to issues in web scraping. For instance, scraping data from poor HTML code can lead to inaccurate and misleading scenarios. The transform phase of ETL takes care of this by harmonizing the data into an accurate and consistent state.
- Dynamic Content Loading: Websites often use dynamic content loading, which might make it difficult for web scrapers to collect all the relevant data. ETL processes can handle this by transforming dynamic content appropriately
- Text Processing: Extracting information from unstructured text, such as blogs and articles, can be challenging. Modern-day ETL scripts equipped with Natural Language Processing (NLP) capabilities can analyze and process textual data, making it eligible for reliable analyses.
How do you set up ETL for Web Scraping?
We have spoken at length about the need for ETL to achieve fruitful outcomes from data scraping. How do you set up the proper ETL process for web scraping? Let’s dwell upon that for a bit. The following are the key steps you may want to adopt to get the proper ETL process going:
1) Define requirements: Determine the websites or web pages from which you want to extract data. Strategize on the data formats you want to extract, the transformation procedures factoring the business rules, the output data formats, and the target database design.
2) Identify toolset/technology stack: Identify the technologies and tools required for each of the three phases in ETL. For instance, you could choose Selenium for data extraction, Python for data transformation, and MySQL for the target data warehouse.
3) Drive seamless execution:
- Leverage web scraping libraries for data extraction involving sending HTTP requests, parsing HTML, and extracting relevant information. Be aligned with website terms and conditions to comply with legal and ethical considerations while extracting data.
- Transform data, including converting data types, renaming columns, handling missing values, creating derived data based on business requirements, etc.
- Load the data into the target database or data warehouse using optimal queries. The optimal queries provide fast responses to users looking for reports and analyses.
4) Automating ETL Process: Automate the ETL process at scheduled intervals using tools such as Apache Airflow.
5) Monitoring and refinement: Monitor the ETL process for errors and issues and log such incidents. Fix the errors and issues to make the ETL process more robust
In summary, you need a well-thought-through ETL process and seamless execution to get good outcomes from web scraping. Compared to the benefit of reliable data for decision support, the cost of poor-quality data presents a vast chasm that needs ETL to bridge the gap and get the best possible results.
