This post is to show users how to use a docparser.com rule to move data from a pdf document into a database.
The long-tail keywords required in the brief defined the focus to a MySQL database, but the product is database-independent. The original work submitted is included here, but my accompanying notes suggested that the scope be broader to align with the product capabilities. The client accepted my suggestions and made additional edits in-house to include that information. Those edits are reflected in the website.
URL: https://docparser.com/blog/pdf-to-database/
The long-tail keywords required in the brief defined the focus to a MySQL database, but the product is database-independent. The original work submitted is included here, but my accompanying notes suggested that the scope be broader to align with the product capabilities. The client accepted my suggestions and made additional edits in-house to include that information. Those edits are reflected in the website.
URL: https://docparser.com/blog/pdf-to-database/
EXTRACT DATA FROM PDF TO A DATABASE
You have business documents you get in pdf format: invoices, work orders, purchase orders and others. Sometimes data is in the pdf as a table or documents were scanned into a pdf. They hold data you need to process in your ERP or other database-driven information system.
How to convert these PDF documents into usable data in your database?
You use Docparser, that’s how!
Docparser is a leading PDF converter with some processing muscle and a few friends to get the heavy-lifting of data intake done for you.
YOUR CHOICES FOR CONVERTING THE PDF DATAThis post refers to MySQL database, where Docparser is the first step to building your PDF to MySQL database converter.
Keep in mind that Docparser has no requirements on database vendors or scripting languages. You are free to use the database and language of your choice.
THE FIRST OPTION REQUIRES THE MOST MANUAL INTERVENTION, BUT IS QUICK TO IMPLEMENT.
Use Docparser functionality to convert the PDF data to a csv file or Google Sheet ready for upload into the database. You build a parser for each different type of document you process and assign a specific method for handling the data.
Docparser can create csv files direct from PDF documents and has a built-in method for exporting data to a Google Sheet. Either one can be the default processing method for your parser. The data moves as soon as file processing completes.
MySQL has an upload function and will require you to download the file and then import to the database.
While this does require manual intervention, it is a good way to move information to your MySQL database while you are building all the parsers for your different types of documents.
IN THE NEXT CHOICE WE ADD A WEBHOOKS INTEGRATION TO OUR WORKFLOW TO MOVE THE PDF DATA INTO OUR MYSQL DATABASE. As soon as Docparser processes the incoming file, data posts to the integration platform you have identified for that parser. The information loads to your MySQL database through the Integration Partner.
IN THE THIRD METHOD, USE THE DOCPARSER API TO EXTRACT PDF DATA TO A MYSQL DATABASE.
A developer can build a custom script to pull the parsed data and move it into the correct database location. This API has your own signature and sign on, tied to your data and files. The API offers a great deal of functionality including:
WHERE TO START?Some clients start with one method and build their next iteration to a different method. This is a good way to expedite your data capture while leveraging available tools and testing your process.
These are the different ways to convert a pdf to data in a MySQL database and Docparser can help simplify this process. The Docparser team is always here to help you get up and running as quickly as possible. Quit re-entering your data! Isn’t it time you got some automation into your data intake?
Sign up for a free 30-day trial now and see how much easier your workday can be with Docparser.
You have business documents you get in pdf format: invoices, work orders, purchase orders and others. Sometimes data is in the pdf as a table or documents were scanned into a pdf. They hold data you need to process in your ERP or other database-driven information system.
How to convert these PDF documents into usable data in your database?
You use Docparser, that’s how!
Docparser is a leading PDF converter with some processing muscle and a few friends to get the heavy-lifting of data intake done for you.
YOUR CHOICES FOR CONVERTING THE PDF DATAThis post refers to MySQL database, where Docparser is the first step to building your PDF to MySQL database converter.
Keep in mind that Docparser has no requirements on database vendors or scripting languages. You are free to use the database and language of your choice.
- Set up parsing rules and import your files for each type of document you want to bring in. This step is required no matter where data goes after capture. You will need Docparser to get the data out of the PDF and ready for your database.
- Determine which method you will use to move PDF data to the MySQL database.
- Google Sheet or csv file manual download then import to the MySQL database.
- Partner Integration to move the data from Docparser to your database. This can be through Zapier, Stamplay, or Workato, each allowing you to create the workflows you need.
- Build a custom script via the API to move data from Docparser to your database.
THE FIRST OPTION REQUIRES THE MOST MANUAL INTERVENTION, BUT IS QUICK TO IMPLEMENT.
Use Docparser functionality to convert the PDF data to a csv file or Google Sheet ready for upload into the database. You build a parser for each different type of document you process and assign a specific method for handling the data.
Docparser can create csv files direct from PDF documents and has a built-in method for exporting data to a Google Sheet. Either one can be the default processing method for your parser. The data moves as soon as file processing completes.
MySQL has an upload function and will require you to download the file and then import to the database.
While this does require manual intervention, it is a good way to move information to your MySQL database while you are building all the parsers for your different types of documents.
IN THE NEXT CHOICE WE ADD A WEBHOOKS INTEGRATION TO OUR WORKFLOW TO MOVE THE PDF DATA INTO OUR MYSQL DATABASE. As soon as Docparser processes the incoming file, data posts to the integration platform you have identified for that parser. The information loads to your MySQL database through the Integration Partner.
- Here's information on the Zapier and Docparser workflow.
- Workato has several pre-built recipes available for Docparser.
- Stamplay uses a Webhook Integration to get the Docparser data.
IN THE THIRD METHOD, USE THE DOCPARSER API TO EXTRACT PDF DATA TO A MYSQL DATABASE.
A developer can build a custom script to pull the parsed data and move it into the correct database location. This API has your own signature and sign on, tied to your data and files. The API offers a great deal of functionality including:
- Identify all defined parsers, by ID and name.
- Upload documents for parsing, via HTML form or an accessible URL
- Apply a unique identifier to any document which you submit
- Receive data via a Webhook Integration to your application, a permanent download link, or by polling and fetching from your API
- Fetch data for your API in single or multiple data sets
WHERE TO START?Some clients start with one method and build their next iteration to a different method. This is a good way to expedite your data capture while leveraging available tools and testing your process.
These are the different ways to convert a pdf to data in a MySQL database and Docparser can help simplify this process. The Docparser team is always here to help you get up and running as quickly as possible. Quit re-entering your data! Isn’t it time you got some automation into your data intake?
Sign up for a free 30-day trial now and see how much easier your workday can be with Docparser.
