API Data Integration – Key Factors While Choosing a Platform

API data has become particularly relevant as more organizations use data feeds from third-party services to implement their Gen AI applications and other ML use cases. API data integration involves fetching data from APIs, transforming it, and pushing the transformed data to downstream APIs or exposing it as new data product APIs. Data API Integration is particularly challenging because of the variable nature of the URLs, API protocols, and authentication mechanisms. 

This article explores typical challenges in API data integration and the best options to get past them. 

Summary of key API data integration concepts

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Understanding API Data Integration

Data integration is ingesting data from multiple internal and external sources to make it available to downstream systems for building reports, analytics subsystems, self-service BI, AI/ML models, Gen AI applications, etc. API data integration involves doing the same but with the caveat that the source or destination systems use web APIs to enable access to data.

In practice, data sources and destinations within an organization have a variety of access patterns, including databases, filesystems, web APIs, etc. Hence, the typical data integration process has to deal with all these data access methods, including APIs.

External use cases

The most common use case of API data integration is to access third-party data feeds to create AI-ready data. With the increased reliance on data products for running businesses, organizations often have to pull data from several APIs and combine them with their data to build reports and predictions. For example, a logistics company may pull weather and news data from external sources and join them with route waypoints before feeding them to a machine-learning model that calculates the estimated arrival time. The advent of Gen AI has led to many more such use cases that involve pulling data from the financial market, social media, etc. Even accessing an externally hosted LLM, like Open AI or Mistral, would require one to access a third-party API. In such cases, the API data integration process has to ingest data, run complex joins, and then feed the transformed data as input to a prediction model. 

In some cases, the downstream systems may be unable to pull data through an API. Instead, they may expose their API, and the upstream system must upload data whenever a change occurs. This usually happens with legacy vendors or an adamant customer with whom the organization does not have much negotiating power. 

API data integration high-level view

Internal use cases

Accessing APIs for data ingestion is not limited to external sources alone. The organization’s internal teams expose data as an API in several scenarios. For example, constantly changing smaller data sets are best exposed as APIs. 

Consider a product information dataset maintained by the catalog team. Information about the products on sale can change frequently, even on the same day, and it may be tedious to keep updating a data file. A data process that requires up-to-date information about the product catalog is best served if the data is exposed as an API. 

Data integration also involves feeding or pushing data to downstream systems. In the above example, exposing the product catalog information as an API also comes under the purview of data integration. Another example is the deployment of AI models as APIs. Since accessing machine learning models for inference will require custom libraries and code, they are mostly exposed as APIs to keep the client application simpler. Such tasks are handled by the team that owns that specific data product or model with the help of an integration platform. 

Challenges in API Data Integration

Variety in implementation architectures

Even though web APIs are meant to be a common standard, there are several ways in which they can be implemented. The client that ingests data from an API needs to be aware of the implementation architecture to integrate it. For instance, accessing data from a REST API differs greatly from a SOAP API. Most data APIs are guarded using an authentication protocol like OAuth, HMAC, JWT, etc. Developing client applications to ingest data by manually coding logic is tedious. Using an API data integration platform with out-of-the-box tools to easily connect to any system through different authentication mechanisms and unique data retrieval flavors greatly reduces the time needed for implementation. 

Accessing paginated and asynchronous APIs

APIs that provide a large amount of data are often paginated. In such cases, the client applications cannot fetch the complete data in a single API call. Instead, the ingestion process must keep the previously accessed page offset in memory and explicitly mention the next offset it wants to fetch with every API call. 

Accessing paginated APIs

An asynchronous API is another pattern that makes implementing the ingestion process difficult. This pattern is found mainly in APIs that internally run high throughput data processing or AI model inferences. These APIs do not provide the data in real-time in a single call. Instead, the first API call triggers a job in the background, and the client application has to use a different URL to check the job status. Once the data is ready, the client application must access another URL to get hold of the data. 

Handling delta data

When it comes to large data feeds, data owners often expose separate APIs to retrieve the updated data since the last fetch. This is to help client applications to reduce the amount of data transfer during their periodic fetch routine. It also helps the data owners themselves to reduce the load on their servers. Handling delta data often requires manual coding of time or status identifier based logic. 

Chaining API calls

In many use cases, the integration will not end when accessing a single API to get data. Using the output from the first endpoint to trigger a subsequent one and API chaining across multiple hops is typical in API-first organizations For example, consider a product information API that provides product identifiers based on filtering criteria like category, color, etc. The ingestion process may have to access another API exposed by the inventory platform using the product identifiers fetched in the first step to get further details of the products like remaining inventory, details of similar products that need to be recommended as part of promotion etc.

API Chaining   

Capturing Data Lineage

Data lineage is a record of how data moves from one stage to another through several transformations and ends up in its final data product form. It includes information on the origin of data as well as all the changes that happened to the data element. The information about the origin of data is extremely important while investigating issues related to data quality. Comprehensive lineage also improves the authenticity of data and is important in ensuring regulatory compliance.

Capturing lineage in API Data Integration is particularly difficult because it involves complex data flow logic and several schema types across intermediate stages. For example, in the chained API logic explained above, the result of the first product information API call will have a simpler schema with only product identifiers and filtering criteria applied. The second API call that joins the product identifiers with the rest of the product information will have a much more complex schema. Keeping track of different data outputs and their schema and tracing them through several stages of API chains is not an easy task. The ideal API data integration platform automatically keeps track of this, along with the schema changes across stages. 

Error handling

Handling error responses is a key aspect of API data integration. API availability is generally defined by metrics like uptime, latency, etc. While providers aim for high 9s of uptime and lower latency, a few requests always error out or go beyond the time-out values. APIs also have rate limits and well-defined fallback mechanisms like exponential backoff to protect against denial of service attacks. The API integration platform must be able to track errored requests, log them, and retry them adhering to the defined fallback protocols. 

Requests that provide an error response and status code are often the easier ones to handle. In some cases, the APIs will respond with a successful status code but will return corrupted data. Handling such errors is difficult and can only be done by manual validation of schema or underlying patterns in data. 

Choosing an API data integration platform

Ease of API data access 

Ease of implementation is the most important criterion when selecting an API integration platform. Ideally, the platform must enable the implementation of straightforward use cases without coding while also providing the flexibility to address complex use cases. 

For example, Nexla provides templates for implementing the most common API integrations. If one needs to address a bit more complexity, it provides the ‘advanced’ mode, allowing users to customize the template further according to their needs. Nexla users can leverage the built-in universal connector if a connector is unavailable in the connector list. The universal connector offers very granular configuration options, including authentication protocols, pagination options, etc. 

Comprehensive support for authentication protocols

APIs differ regarding their unique URLs, authentication protocols, input parameters, implementation architecture, etc. Ideally, the API data integration platform must allow pluggable components to be stitched together to address any common API access pattern. Platforms like Nexla support all the common authentication protocols like OAuth, JWT, HMAC, etc, for both SOAP and REST APIs.

Pagination and API chaining 

Handling paginated APIs and chaining together APIs are very common requirements in enterprise data integration. Even though it is possible to do this by manually coding the logic, it is error-prone and time-consuming. An integration platform that allows configuring the pagination and chaining logic goes a long way in ensuring the development timeline and quality.  Nexla’s universal API connector helps developers configure pagination rules and subsequent API calls based on success and the frequency of data ingestion. Once configured, Nexla will automatically create a Nexset that encapsulates all the metadata. Any new data with a similar schema is automatically added to the same dataset. 

Ease Of Transformation

Data fetched from the APIs often needs to be transformed before it is usable. An ideal API integration platform must have options to transform the API data easily. No Code/Low code platforms can help even analysts without technical knowledge to manipulate API data to create transformations. Nexla has a long list of pre-configured transformation templates that can be integrated without coding. The built in AI Agent – Nexla Orchestrated Versatile Agent (NOVA) can generate transformations through natural language prompts by reducing development time. Developers can modify the generated transformations to improve them before deploying them. The agent can also suggest transformation logic based on context.  For example, NOVA can help a developer working with a restricted data set by suggesting a PII Data masking transformation step. 

Serving Data As APIs 

With organizations moving to data products as a service paradigm, exposing data as APIs has gained importance. Exposing models as API is another key use case typical in modern Gen AI-enabled organizations. Using APIs helps data owners enforce schema and bundle required metadata along with the response. Enforcing role-based access control is also easy with authenticated APIs. Nexla’s visual interface helps to expose Nexsets as an authenticated API automatically without any coding. The data source for the API can be files, databases, or even an SQL query. You can read more about it here. 

Automated Lineage Capture

While implementing complex data flows involving several stages through API integration, it is difficult to keep track of schema changes and trace the origin of the final data product. API Integration platforms with automated lineage capture makes it easy to understand the origin and various stages through which the data has moved before ending up as the product. Nexla provides complete record level lineage to easily troubleshoot issues and locate corrupt data records to its source. 

Automated Error handling

Handling errors for API data integration involves automatically detecting errors and retries based on configurable frequencies. The records that caused errors must be logged for further analysis. Errors must not disturb the overall pipeline and should let the process continue without stalling. Another aspect of error handling is notifications when errors happen. Nexla’s automated error handling can detect errors based on schema changes or API failures. It quarantines error records and makes them available for investigation and recovery later. Nexla continuously learns about the data characteristics and can even detect errors based on the quantity, quality, or timeliness of data. 

Conclusion

API Data Integration is about fetching data from internal or external data feeds, transforming them, and exposing them to your downstream systems. Being a versatile instrument of communication, APIs come with a lot of variability within themselves. Unique URLs, input parameters, authentication protocols, and implementation architectures make them difficult to work with. Add to the challenges of accessing paginated APIs or chaining APIs together in typical enterprise scenarios. A data integration platform with comprehensive support for variables within API access can drastically reduce your time to production. 

This is where platforms like Nexla can help. Nexla embraces the concept of data product paradigms and comes with a templated API integration mechanism. Its visual interface helps one to access paginated APIs, chain them together, or expose data products as APIs in a few clicks.