Data API Integration for maximizing your ROI

Introduction

Web API or API in short is not a new technology. It has been there for more than a decade and today Programmable- Web directory lists more than 20,000 public APIs available for consumption. Transactions over billions of dollars are executed successfully in this technology every day and the volume is growing every passing quarter. But seamless API integration with the different upstream and downstream applications is always a challenge. However, a large number of on-prem, cloud solutions are helping this to be achieved in an efficient and effective manner. The theoretical definition of API is as follows-

?An application programming interface (API) is a messenger that processes requests and ensures the seamless functioning of enterprise systems. API enables interaction between data, applications, and devices. It delivers data and facilitates connectivity between devices and programs.

What is the benefit

Undoubtedly API helps to expand your business through sharing data with the business partner without exposing the underlying data warehouse of the organization. It is a lightweight data sharing mechanism that is completely technology and platform agnostic. ?The data coming from API can be leveraged by interested third parties that can consume and monetize the same. Organization use this data for operational insights, research, and development initiatives, and many more.

What are the use cases?

·??????Google API integration to create Analytics Dashboards that allow organization executives to centralize data from multiple sources including Analytics data, Adwords/Paid Search data, and data from other software like accounting systems, CRM systems and more.

·??????Build APIs to PULL data from Social Media Platforms (Google, Facebok, Twitter) for marketing strategy efficiency

·??????Integrate disparate data across from different third-party upstream and downstream applications to partner with a different organization

·??????Develop API to expose data to different downstream systems for B2C and B2B business collaboration

Challenges-

Network: Although a database can now be hosted in the cloud, previously we were dealing with a database within our network (locally). Local network speeds are faster and generally more reliable when compared to communications going through the internet. We need to take this factor in consideration when building our data pipelines. The internet sometimes can be not as reliable as we would like and lots of things can go wrong when our data needs to go through a network of networks.

API Contracts: Although some APIs may provide an API specification. Normally, these aren’t as simple to navigate/interpret as a traditional database schema or as simple to generate an entity relationship diagram. It is important to get?very familiar?with the structure of the API response and also with the functionality that the API offers.

Authentication: Different services provide different forms of authentication for their APIs. Some examples would be basic authentication, certificate-based authentication, JWT or OAuth 2.0. In the case of a service that uses JWT or OAuth 2.0 authentication we normally retrieve a token which will be used as part of the authentication of our requests to the API server. This token is valid for a period of time and this will vary across services, e.g. 20 minutes. Depending on how long an access token is valid for, it is possible that you will need to deal with a refresh token mechanism as part of your data pipeline, e.g.?Authentication & refresh tokens with SAP Ariba APIs. This will be required if the running time for a data pipeline is greater than the time a token is valid for. Depending on the tool used for the data pipeline, this can be managed by the connector/adapter configured to retrieve data from the service.

How it works

• Ingest?raw data from any API?by specifying your data source(s) via our no-code UI, low-code YAML spec, or full-code API & SDKs
• Unify and join your API data with other datasets from your data lake, warehouse, and elsewhere with simple transforms in?SQL, Python, Java, or Scala
• Write the resulting data directly to your favorite database, warehouse, blob store, and more.
• handles non-functional cases?— orchestrating runtimes, handling retries/errors, managing parallel jobs, and persisting copies of your data.

Conclusion

The challenges are inevitable but as we start leveraging API more for building the solutions, there will be an overall improvement in the design and integration approach in the API ecosystem. The future trend will be more and more API driven solutions and architect job to find integration patterns suitable for current and future challenges to handle

?References

Data pipelines and APIs – Consider this when building your next data pipeline - (lwmeta.com)

ETL for API | How it Works & What to Expect [Use Case] (ascend.io)