How to Automate Data Scraping from PDFs Using Airbyte?

Portable Document Formats (PDFs) are versatile documents that help you to store and share information effectively. They support various elements such as text, images, and tables in their original format. This allows you to use PDFs across diverse industries, including legal, finance, retail, and public sector.

For effective usage, you can transfer the data in PDF to another data system or file format. To achieve this, you can scrape the data using a suitable solution instead of opting for manual data entry. Let’s learn about how to automate data scraping from PDFs to store, query, and analyze the data for business intelligence.

What is PDF Data Scraping?

PDF data scraping is an automated technique of extracting semi-structured or unstructured data from PDF documents. You can store the retrieved data in CSV, Excel, JSON, or an SQL database. Such data can then be transformed and analyzed for applications like document processing, resume parsing, or scientific literature analysis.

How Data Scraping From PDFs Work?

To start scraping data from a PDF, you must first identify the type of data you want to extract. It can be text blocks, images, or tables. Then, you can use a suitable data scraper to retrieve relevant content directly. You can also use an OCR tool to convert images containing textual information into machine-readable form.

Once scraped, the raw data requires cleaning. This usually involves removing extra whitespaces, special characters, or other unwanted elements to ensure data consistency. You can then store this standardized data in a file format or databases of your choice.

How to Automate Data Scraping From PDFs Using Airbyte?

There are various methods of extracting data from PDFs. You can either copy and paste data manually, hire a data entry expert, or use an AI-powered data scraping solution. However, to effectively transfer unstructured data from PDF for downstream operations, a data movement platform like Airbyte can be a more effective choice. 

Airbyte offers an extensive library of 550+ pre-built connectors. With the help of these connectors, you can extract data from any source and load it to a suitable destination data system for further analysis.

To facilitate data scraping from PDF files, Airbyte supports Document File Type Format in some source connectors. It is currently an experimental feature that allows you to scrape data from  PDF or Word documents stored in S3, Azure Blob Storage, or Google Drive. The extracted data is converted into Markdown format to retain the structural entities, such as headings and lists from original documents.

For this tutorial, let’s scrape data from a PDF file stored in Azure Blob Storage and load it to Google Sheets. The steps for this data migration are as follows:

Step 1: Configure Source to Extract Data from PDF

• Log in or sign up for an Airbyte account if you do not already have one.

• Click the Sources option from the left navigation pane and enter Azure Blob Storage in the Search box.

• On the New Source page, authenticate your Azure Blob Storage account by selecting any Authentication method. The three authentication options are Authenticate via OAuth2, Authenticate via client credentials, or Authenticate via Storage Account Key.

• Enter the Microsoft Azure Application tenant ID in the Tenant ID field. Then, provide Azure Blob Storage Account Name and Azure Blob Storage Container (Bucket) Name.‍
• Click the Add button near The list of streams to sync section to specify which streams you want to scrape. In Airbyte, streams are groups of related data records and are called tables, files, or blobs, depending upon the destination.
• Select the Document File Type Format (Experimental) in the Format section and enter other details, including the name of the stream, in the Name field.

• Next, you can fill in the Start Date and Endpoint Domain Name.
• Finally, click the Set up source button.

Step 2: Configure Google Sheets as Destination

Once you configure the source, you can proceed to set up Google Sheets as the destination using the below-mentioned steps:

• From the left navigation pane of the Airbyte dashboard, click Destinations. Enter Google Sheets in the Search box.

• On the Create a Destination page, click on the Sign in with Google button to authorize your Google account.

• In the Spreadsheet Link field, enter your Google Sheet link.
• Finally, click Set up destination.

Step 3: Configure the Connection

• Select Connections from the left navigation pane of your Airbyte dashboard.
• Choose Azure Blob Storage as the source and Google Sheets as the destination.
• Specify the Data Sync Mode and the Streams That You Want to Replicate, and click Next.

• Then, enter the Frequency of Data Sync and click Set up Connection.
• You will be redirected to the Connection Overview page, where you can use various tabs, including Status, Timeline, and Settings, to monitor your connection.

This completes the process of scraping data from PDFs using Airbyte. Further, you can then integrate Airbyte with dbt, a command-line tool, to perform necessary transformations on your scraped data for further analysis.

Use Cases for Data Scraping From PDFs

Knowing how to scrape data from websites or documents like PDFs or Word files is useful across various industries that depend on textual data extraction. Some of the sectors that benefit from this are:

Finance

In financial institutions, you can scrape data such as invoice numbers, dates, and vendor names from PDFs for faster invoice processing. The PDF data scraping process makes bank statement analysis easier by facilitating automated extraction of transactions, balances, and account details. You can also sanction loans faster by scraping PDFs containing information on customers’ income statements and tax filings.

Legal

Using the PDF scraping technique, you can retrieve key terms, including clauses and obligations, from legal contracts for faster contract analysis. While preparing for a case, you can scrape data from numerous reference PDFs to quickly summarize relevant judgments and rulings.

Healthcare

Scraping medical records such as health history and treatment details from PDFs helps you diagnose and provide accurate medical care to your patients. During the health insurance claim process, you can quickly scrape data from PDF policy files for consent of compensation for medical treatment.

Academia

You can scrape PDFs of scientific papers to create a hypothesis, conduct citation mapping, and a comparative analysis of research methodologies. PDF scraping is also beneficial in patent mining; you can extract and analyze patent databases to study industrial trends and intellectual property.  

Why Use Airbyte to Automate Data Scraping From PDFs?

Airbyte is an effective solution for automating your PDF data scraping process. This is because the platform offers various high-performance features, such as:

• Flexibility to Develop Custom Connectors: Airbyte allows you to develop your own custom connectors using multiple options. Some of these options include Connector Builder, Low Code Connector Development Kit (CDK), Python CDK, and Java CDK.‍
• AI-powered Connector Development: While building custom connectors in Airbyte, you can utilize the AI assistant feature available in the Connector Builder. It helps you automatically prefill important connector configuration fields and also provides intelligent solutions to fine-tune the configuration process.‍
• Build Developer-Friendly Pipeline: PyAirbyte is an open-source Python library that provides you with a set of utilities to use Airbyte connectors in the Python ecosystem. You can use PyAirbyte to extract data from a variety of sources and load it to SQL caches like Postgres. This data is compatible with other Python libraries like Pandas and can be manipulated for further use cases.‍
• Streamline GenAI Workflows: If your extracted data is semi-structured or unstructured, you can load it directly into vector databases using Airbyte. It supports vector databases like Pinecone, Weaviate, and Milvus, enabling you to perform semantic and contextual searches for efficient GenAI workflows.

Conclusion

Scraping data from PDFs is essential for better document management, research, and improving workflow efficiency. This blog describes how to automate data scraping from PDFs in detail. You can use the scraped data for various purposes across different sectors, such as healthcare, finance, and law. By using automated data scraping in these domains, you can retrieve useful information faster for well-informed decision-making.