
Businesses transfer data for many reasons — migrating from outdated databases, sharing information between operational partners, or integrating systems from different verticals are just a few examples.
Companies often use tools built piecemeal over time, and no one wants to overspend — but old methods can’t meet today’s security and regulatory standards.
The downstream costs of mishandling data can be astronomical. While efficiency matters, you must think more broadly than compute speed and storage costs. Your business needs high-quality, readily usable data; more crucially, it needs a secure pipeline.
What Makes A Good Data-Transfer Method?
All data transfer is an exchange: A producer on one end, and a consumer on the other. You should consider their unique needs, even when the same people play both roles.
A data exchange system must meet a few criteria to serve all stakeholders:
- User authentication and access control: Producers know who’s using their data and can control what they can access.
- Data segregation: Producers separate data so privacy and security measures can be set programmatically.
- Application-ready data: Consumers benefit from data designed around use cases and delivered in a predictable, modern format like JSON, XML, or GraphQL.
- Rate limiting and throttling tools: These protect infrastructure from excess traffic, keeping resources available.
- Good quality metadata: Detailed, real-time information on consumer usage patterns allows producers to provide better services.
Below, we’ll examine two common methods for transferring data—data scrapers and APIs—and see how they perform.
Is Data Scraping Ever a Good Choice?
Data scrapers are a fast, easy way for developers to collect data. They’re also one of the most abused and riskiest tools. While they have legitimate uses, they’re inadequate when transferring unique or sensitive data between business partners or within an organization.
What Is Data Scraping?
A data scraper is a program that extracts data from human-readable content hosted or produced by other computers. It is usually an automated script that collects data from HTML pages.
Examples of data scraper usage:
- Collecting training data for machine learning and AI: Scrapers take data from publicly available sources for tasks like sentiment analysis or creating a tool for summarizing research findings.
- Transferring data from legacy sources to a new database: For example, migrating old payroll and employee data or pulling details from old financial reports to support budget forecasting.
- Gathering information: With the assistance of AI, data scraping can often be used to pull information from sites that do not currently have a publicly available API.
Both uses are valid, but the data is easy to exploit. While scraping public data isn’t a huge concern (with the right guardrails!), the transfer example exposes companies to serious risk.
How Data Scraping Stacks Up
Data scraping can’t deliver the trust and reliability businesses need when transferring important data. Here’s why:
1. User authentication and access control:
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- Knowing who’s scraping your website(s) is hard. Controlling what they access is even harder.
- Blunt instruments like IP block lists and user-agent blocking can make life harder for legitimate users.
2. Data segregation:
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- Security is all-or-nothing — scraping treats all data the same.
- Producers must choose: Should all data and consumers be held to the highest level of security, or should only offer data that’s safe for public use?
3. Application-ready data:
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- Scraped data is unformatted and full of duplication.
- Cleaning and filtering scraped data can eat into efficiency gains.
4. Rate limiting and throttling:
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- Hackers use data scrapers for DoS (denial-of-service) attacks.
- Even well-intentioned web scrapers can be harmful if the scraper or the site is not adept at distributing requests.
5. Good quality metadata:
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- Data scrapers muddle metadata, and it’s not useful if you can’t distinguish between human and automated consumers.
- Most scrapers are anonymous, making it difficult to discover who’s behind a scraper.
Web scraping is inappropriate if you’re dealing with any proprietary or sensitive data. No amount of monitoring can make it trustworthy enough to handle the data that fuels your business.
Why APIs?
The best choice for transferring data is APIs. Usage is increasing, and for good reason.
Application programming interfaces (APIs) allow programmatic communication between two software applications, a producer and a consumer. The producer hosts data at URL endpoints, and the consumer sends HTTP requests to read or write data at a specific endpoint.
APIs support consumer applications, microservices architecture, backend data sharing, and many other functions. Increasingly, they’re replacing data scraping to handle data transfers, even one-time projects like database migrations. Here’s how they’re the better tool for the task.
APIs Help Build Efficient Data Pipelines
APIs deliver benefits for producers and consumers and measure up well to our criteria:
1. User authentication and access control:
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- Building access control into your API is easy with industry-standard auth protocols and well-designed tools.
- APIs offer different data to different users so consumers see only what they need.
2. Data segregation:
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- APIs are programmatic, so you only expose the data you actively share.
- Keep sensitive data out of public resources by hosting separate APIs for data at different levels of risk.
- Consumers know what they’re getting and can prepare to handle it.
3. Application-ready data:
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- APIs deliver data in code-ready, predictable formats, usually JSON. The shape of data objects at an endpoint is consistent and predictable.
- Producers can design endpoints around use cases. API consumers get higher-quality data that serves their needs.
- Data is on demand. Consumers get what they ask for.
4. Rate limiting and throttling tools:
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- Producers have many choices for controlling request traffic to manage server load. Huge requests don’t swamp producers.
- Consumers get a more reliable resource. High-availability services mean users can get data when they want it.
5. Good quality metadata:
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- Authenticated API consumers leave metadata trails. You see the data that’s requested and by whom.
- Producers design better services when they understand consumers. Better metadata benefits both ends of the API pipeline.
APIs Deliver Reliability
There’s no shortage of data out there, and it’s growing by the second. But there’s a serious shortage of trust.
It’s important to note that the key aspect of a pipeline’s security and trustworthiness is how data transfers within the pipeline take place. Many folks focus on ensuring their data is secure at rest but then often overlook security risks when they are in transit through the pipeline. Plus, producers tossing valuable data into the sea of information for scrapers to discover are taking a gamble. They fail to honor or build consumer trust and often fall victim to exploits themselves.
In the end, APIs are the better solution over data scraping because they provide structured, reliable and secure access to data, ensuring consistent integration without risking website disruptions or legal issues. Unlike scraping, APIs are designed for developer use, offering clear documentation and support for efficient and scalable interactions.
To build a successful business, you need reliable infrastructure. Mitigate your risks and treat both your internally and externally available data like the precious resource it is. The right tools — including APIs — help restore trust in the information economy so you can build your pipeline to supply the next wave of explosive growth.