An API, or Application Programming Interface, is a set of rules and protocols that allow different software systems to communicate with each other. Put simply, APIs define how software components should interact and allow developers to create new applications by leveraging existing functionality from other systems.
Research shows that business investment in API has boomed in recent years, and significant API investment is linked with greater business growth and productivity. For data engineers, in particular, learning about APIs and building simple APIs with frameworks like Flask are valuable projects in the portfolio of any aspiring data engineer.
APIs are typically built by software companies and organisations to allow other developers to access and use their services, data, or functionality in their own applications. Some examples of commonly used APIs include:
- Social media platforms like Facebook and Twitter provide APIs allowing developers to access user data, post content, and interact with other users.
- Payment processors like Stripe and PayPal provide APIs that allow developers to process payments and manage transactions in their own applications.
- Weather services like OpenWeather and DarkSky provide APIs that allow developers to access current and forecasted weather data in their own applications.
- Google Maps API, allows developers to incorporate map and location data in their applications.
- In e-commerce, Amazon Product Advertising API allows developers to access Amazon’s product catalogue, retrieve product information, and make programmatic calls to place an order.
This article is about APIs, how they work, the types of APIs and their interaction with data science and engineering.
Table of Contents
Types of API’s
Firstly, there are a few different types of use classes of APIS. For example, an API could be open, meaning it’s publicly available and free to use, or internal, allowing a business to connect its internal systems.
- Open API: it is free for anyone to use and allows developers to access the functionality of a system without any restrictions.
- Internal API: These are used within an organisation and are not exposed to the public. They may be used to allow different teams within an organisation to share data and functionality.
- Partner API: These are used to allow specific partners to access a company’s data or functionality.
- Composite API: These allow developers to access multiple endpoints or services with a single API call.
An integral component of an API is the API key. API keys are unique identifiers that are used to authenticate and authorise access to an API.
They are typically issued by the API provider and are required to be included in every API request made by the developer. API keys are used to track and control access to an API and can limit the number of API calls a developer can make or track usage for billing purposes.
API keys are usually generated in the developer portal of the API provider’s website, where developers can also manage their API keys and view usage statistics. Some API providers also offer the option to generate multiple API keys, which can be useful for different environments (e.g. development, staging, and production) or for different developers working on the same project.
Further, API keys are typically passed in the request header but can also be passed as a query parameter or in the request body. They are often used in conjunction with other authentication methods, such as OAuth or JSON Web Tokens, to provide an additional layer of security.
It’s essential to keep API keys secure and not to share them with unauthorised parties. If an API key is compromised, it should be revoked, and a new one should be generated.
- In order to access an API, developers must typically register for an API key.
- API keys are used to track and control access to an API.
- They can be used to set limits on the number of API calls a developer can make, or to track usage for billing purposes.
API’s can be accessed through a variety of methods, including REST (Representational State
Transfer), SOAP (Simple Object Access Protocol), and XML-RPC (XML Remote Procedure Call).
Here’s a brief summary of each:
REST (Representational State Transfer)
REST is an architectural style for building web services that are lightweight, scalable, and easy to implement. RESTful web services use the HTTP protocol for communication and rely on the use of standard HTTP methods like GET, POST, PUT, and DELETE to perform operations.
RESTful web services typically return data in a format such as JSON or XML. RESTful web services are often used to build APIs for web and mobile applications.
SOAP (Simple Object Access Protocol)
SOAP is a messaging protocol for exchanging structured data between applications over the internet. It is an XML-based protocol and requires a separate messaging protocol, such as HTTP or SMTP, to transport the message.
SOAP also requires a separate set of rules for encoding data in the message, called the SOAP encoding rules. As a result, SOAP web services are typically more robust and secure than RESTful web services, but can also be more complex to implement.
XML-RPC (XML Remote Procedure Call)
XML-RPC is a protocol for making remote procedure calls (RPC) over the internet, using HTTP and XML to encode the data. It’s a simple, lightweight protocol often used for small, specialised tasks. XML-RPC is similar to SOAP, but is simpler and less feature-rich.
In summary, REST, SOAP, and XML-RPC are all different types of web service protocols that can be used to build web services and APIs. REST is an architectural style that is lightweight and easy to implement, SOAP is a messaging protocol that is more robust and secure, and XML-RPC is a simple and lightweight protocol that is often used for small, specialised tasks
API security protects web APIs from unauthorised access, use, disclosure, disruption, modification or destruction.
API hacking is extremely common, with 41% of organisations suffering API security incidents in one study. Furthermore, 20% of IT security teams and developers report that API breaches occur monthly. When APIs solicit sensitive data transfer, this is a big problem.
As more and more organisations rely on web APIs to share data and functionality, the need for robust API security measures becomes increasingly important.
API security threats can come in many forms, including injection attacks, DDoS attacks, and data breaches. In order to protect against these threats, developers should implement the following security measures:
- Authentication: This ensures that only authorised users can access the API. This can be achieved by using API keys, OAuth, or JSON Web Tokens.
- Authorisation: This controls access to specific resources or functionality within an API. Developers can use access controls to limit access to certain users or groups or to limit the number of API calls that a single user can make.
- Encryption: This protects data transmitted over the network from being intercepted and read by unauthorised parties. Developers should use HTTPS and SSL/TLS to encrypt all data transmitted to and from the API.
- Validation: This ensures that input data passed to the API is valid and conforms to the expected format. Input validation helps to prevent injection attacks, such as SQL injection and XSS (Cross-site scripting) attacks.
- Logging: This records information about API usage, including user identities, resource access, and error messages. Logging can be used to detect and investigate security breaches.
- Monitoring: This tracks the performance and usage of the API, and can be used to detect abnormal activity that could indicate a security breach.
For more info on protecting APIs and data science from security breaches, read this post.
Additionally, it is important to have a security incident response plan in place in case of security breaches. This includes procedures for reporting and containing a security incident, as well as guidelines for communication and recovery.
In summary, API security protects web APIs from unauthorised access, use, disclosure, disruption, modification, or destruction. To protect against security threats, developers should implement authentication, authorisation, encryption, validation, logging, and monitoring measures.
Moreover, it’s important to have a security incident response plan in place to be able to react and recover from security breaches.
Case Study: Uber API
There are so many excellent examples of APIs in use. In fact, many of us interact with multiple APIs everyday in the form of web services and mobile apps.
Here’s how Uber uses APIs to leverage their technologies:
- Uber has an API that allows developers to access its ride-hailing service in their own applications.
- The Uber API allows developers to access information such as driver and vehicle information, ride status, and fare estimates.
- Many third-party developers have used the Uber API to create many apps, including ride-hailing and dispatch systems, transportation management systems, and more.
API development can become the chief income stream for businesses like Uber. Rather than profiting entirely from their consumer-facing products, they also profit by monopolising APIs in their industry and getting other businesses to use their B2B technology products.
The Relationship Between Data Science and APIs
The relationship between APIs and data science is close, as APIs provide a way for data scientists to access and manipulate data from various sources. Data engineers should familiarise themselves with APIs and build them with frameworks like Flask for their portfolio.
By using APIs, data scientists can easily combine data from multiple sources, such as databases, web services, and other external systems, and use it for analysis. APIs move data from A to B and enable simplified data ingestion and management with ETL processes powered by synchronous REST API calls.
Real-Time Data Analysis
APIs also allow data scientists to access data in real-time, which is especially useful for real-time analytics and monitoring applications. This allows data scientists to quickly identify patterns and trends in the data and make decisions based on that data.
APIs also provide a way for data scientists to expose their models and insights to other systems and applications. This allows other teams and systems to consume the insights and predictions generated by the data scientists and use them to make decisions or take action.
For example, a data scientist could build a predictive model that predicts customer churn and expose that model via an API. Other systems, such as a CRM, could then consume that API and use the predictions to target at-risk customers with retention campaigns.
Additionally, APIs can also be used to enable collaboration between data scientists and data engineers.
Data engineers can use APIs to expose data stored in databases or other storage systems, making it easily accessible to data scientists. This allows data scientists to focus on analysing and modelling data while data engineers handle the underlying infrastructure and systems.
All in all, APIs play a key role in data science by providing a way for data scientists to access and manipulate data from various sources, access data in real-time, and expose their models and insights to other systems and applications. Additionally, APIs can be used to enable collaboration between data scientists and data engineers.
The Relationship Between Data Engineering and APIs
Data engineering for APIs (Application Programming Interfaces) involves several processes and processes that ensure that data is properly collected, stored, and processed for use in APIs.
Data engineers should be competent in APIs and their applications for solving problems.
The first step in data engineering for APIs is to collect the data that the APIs will use. This can involve using web scraping techniques to collect data from websites, using APIs to access data from other sources, or manually inputting data into a database.
The data collected should be relevant, accurate, and in a format that the APIs can easily use.
Once the data is collected, it needs to be stored in a way that is easily accessible to the APIs.
This can involve using a database, such as MySQL or MongoDB, or a data warehouse, such as Amazon Redshift or Google BigQuery. In addition, the data should be stored in a consistent and easily searchable way so that the APIs can easily access the data they need.
Data processing is the step where the data is cleaned, transformed, and processed to be used by the APIs. This step involves removing duplicates, correcting errors, and normalising the data.
This also involves performing data analytics and creating data models, such as data cubes or star schemas, to make the data more easily accessible to the APIs.
Building and Deploying APIs
The process of engineering an API (Application Programming Interface) involves several steps:
1. Define the API Requirements
The first step in engineering an API is to define the requirements for the API. This includes determining the purpose of the API, the data it will need to access, and the operations it will need to perform. This step also involves identifying the target audience for the API, such as developers or external partners, and their specific needs.
2. Design the API
Once the requirements are defined, the next step is to design the API. This includes determining the data models, endpoints, and methods that the API will support. It also involves defining the request and response formats, such as JSON or XML, and deciding on any security measures that need to be implemented, such as authentication and authorisation.
3. Implement the API
After designing the API, the next step is to implement it. This involves writing the code for the API, which includes the logic for handling requests and responses and any database interactions. It also involves setting up the infrastructure and servers that will host the API.
4. Test the API
Once the API is implemented, it needs to be thoroughly tested. This includes testing for functionality, performance, and security. This step also involves testing the API with different types of data and testing it with various clients to ensure that it works as expected.
5. Deploy the API
Once the API is tested and verified, it is ready to be deployed. This includes configuring the servers and infrastructure that will host the API and making it available to the target audience.
6. Monitor and Maintain the API
After deploying the API, it needs to be monitored and maintained. This includes monitoring the API’s performance and usage and addressing any issues that arise. It also includes making updates and improvements to the API as needed.
Summary: What is an API? And How Do They Relate to Data Science?
APIs have become an essential tool for data engineers to collect, store, process, and use data in various applications.
APIs allow data engineers to access data from different sources, process it in a structured way, and make it easily accessible to other systems. Understanding the different types of APIs, such as REST, SOAP, and XML-RPC, and the different use cases for each can help data engineers to choose the right type of API for their specific needs.
The data engineering process, which includes data collection, storage, processing, and security, is crucial to ensure that the APIs are able to provide accurate and relevant data to their users. As the demand for data continues to grow, APIs will play a vital role in helping data engineers to meet these demands and provide valuable insights from the data.