Application Programming Interfaces (APIs) are the backbone of software development, including data processing. In the context of AWS, APIs are how services communicate with each other and how users can interact with and manipulate these services. AWS provides numerous APIs for various services, and data processing APIs form part of this vast collection.

Understanding how to make API calls for data processing is crucial if an AWS Certified Data Engineer – Associate (DEA-C01) candidate wishes to pass the exam. This article will explore some essential APIs for data processing on AWS.

Table of Contents


AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data for analytics. It provides both a visual interface to create ETL jobs and a complete API.

The AWS Glue API is necessary for manipulating AWS Glue resources, such as databases, tables, jobs, and crawlers. For instance, using this API, you can create and manage jobs that transform the data, or you can implement crawlers that read data from your data store and write AWS Glue Data Catalog metadata.

API commands include:

  • CreateJob to define a new job
  • StartJobRun to run a specific job
  • GetJobRun to check the status of a job

Amazon Kinesis Data Streams API

Amazon Kinesis Data Streams (KDS) is a real-time data streaming service. It can continuously capture gigabytes of data per second from hundreds of sources.

KDS API provides access to Kinesis data streams, allowing you to ingest, process, and analyze data in real-time. With the KDS API, you can create a data stream, put records into the stream, retrieve records from the hitting stream, and more.

Key API operations include:

  • CreateStream to create a new Kinesis data stream
  • PutRecord to write a single record into the stream
  • GetShardIterator and GetRecords to read data from the stream

AWS Lambda API

AWS Lambda allows the running of code without provisioning or managing servers. It executes your code only when required and scales automatically.

Lambda’s API provides methods to create, update, delete, and invoke functions.

API calls examples include:

  • CreateFunction to create a new function
  • UpdateFunctionCode to update the function’s code
  • Invoke to run a function

AWS Data Pipeline API

AWS Data Pipeline is a service that helps you process and move data between different AWS compute and storage services and on-premises data sources.

Common operations include:

  • CreatePipeline to create a new data pipeline
  • ActivatePipeline to start a pipeline
  • DeactivatePipeline to stop a pipeline
  • PutPipelineDefinition to upload a pipeline definition


Using APIs to interact with AWS services forms a significant part of a Data Engineer role. By understanding APIs’ basic usage, you’ll be more adept at data processing tasks required for the AWS Certified Data Engineer – Associate (DEA-C01) exam. Always try out various API calls within these services to ensure a practical understanding.

Practice Test

True or False: AWS provides data processing APIs that can be used to interact with services like Amazon Redshift and Amazon S

  • True

Answer: True.

Explanation: AWS does indeed provide multiple APIs for various services that include Amazon Redshift and Amazon S3 for data processing tasks.

Which AWS service provides APIs allowing developers to write applications that process real-time streaming data?

  • a) Amazon Redshift
  • b) Amazon RDS
  • c) Amazon Kinesis
  • d) Amazon EC2

Answer: c) Amazon Kinesis

Explanation: Amazon Kinesis is the AWS service that is designed to handle real-time streaming data and it provides APIs for developers to write applications for such data.

Which AWS service provides a set of APIs to extract, transform, and load (ETL) data in a secure and managed way?

  • a) AWS Glue
  • b) AWS Lambada
  • c) AWS EC2
  • d) AWS Kinesis

Answer: a) AWS Glue

Explanation: AWS Glue is a managed ETL service that provides ready-to-use APIs for the extraction, transformation, and loading of your data in a secure and managed format.

True or False: Using an API Gateway in AWS can increase the security of your API calls.

  • True

Answer: True

Explanation: An API Gateway helps to manage and secure your APIs effectively by enabling features like throttling, authorization and access control, and API version management.

Multiple select: Which among the following services’ APIs help in data processing in AWS?

  • a) Amazon RDS
  • b) Amazon S3
  • c) Amazon EMR
  • d) Amazon SQS

Answer: a) Amazon RDS, b) Amazon S3, c) Amazon EMR

Explanation: APIs of Amazon RDS, S3, and EMR are specifically designed to support data processing tasks in AWS.

True/False: All AWS API calls are synchronous.

  • False

Answer: False

Explanation: Not all API calls are synchronous. AWS supports both synchronous and asynchronous API calls depending upon the services.

Which of the following is the managed service that provides real-time processing of streaming data at massive scale in AWS?

  • a) AWS Glue
  • b) AWS Lambda
  • c) Amazon Kinesis
  • d) Amazon SQS

Answer: c) Amazon Kinesis

Explanation: Amazon Kinesis is a managed service that can process streaming data in real time with high throughput.

True or False: API calls to AWS services are free of charge.

  • False

Answer: False

Explanation: While it varies between different AWS services, most calls to AWS services via API incur costs.

Which of the following AWS service is used to process large volumes of data in parallel?

  • a) AWS Glue
  • b) Amazon EC2
  • c) Amazon EMR
  • d) Amazon RDS

Answer: c) Amazon EMR

Explanation: Amazon EMR is a cloud-native big data platform, allowing processing vast amounts of data quickly, and cost-effectively at scale using popular distributed frameworks such as Apache Spark.

True or False: If you intend to make high request rates to a single partition in an Amazon Kinesis data stream you should use SDK call directly without using Kinesis Data Streams API.

  • False

Answer: False

Explanation: To ensure that the request rate is spread evenly across all shards in the data stream, you should use the Kinesis Data Streams API for high request rates instead of making SDK calls directly to a single partition.

Interview Questions

What is an API call in the context of AWS and data processing?

An API (Application Programming Interface) call is a procedure where a software app interacts with another software, a server, or an operating system. In the context of AWS and data processing, an API call is a process allowing a developer to execute certain functions or retrieve specific data from AWS services such as AWS Lambda, DynamoDB, S3, etc., using standard HTTPS requests.

In which AWS service would you use the AWS Glue API?

You would use the AWS Glue API to interface with Glue, a fully-managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics.

How would you limit the number of API calls made to AWS as a part of your data processing architecture?

This can be achieved by implementing batching and using the bulk operations supported by certain AWS services. Also, caching results can limit repeat requests, thereby reducing the number of API calls.

Why might you increase your API call rate limit in AWS?

You might need to increase your API call rate limit if your application scales up and requires more requests per second than the current limit allows.

What is the primary function of the Amazon DynamoDB API?

The primary function of the Amazon DynamoDB API is to provide developers with an interface to interact with DynamoDB to create, read, update, and delete items in a DynamoDB table.

What Amazon S3 API operation would you use to download an object?

The Amazon S3 API operation “GetObject” would be used to download an object.

How would you use API Gateway in the context of data processing in AWS?

AWS API Gateway could be used for creating, deploying, and managing a RESTful API to expose data processing functions or services like AWS Lambda, or the backend data storage like DynamoDB.

What is an immediate benefit of making asynchronous API calls in AWS Data Pipeline?

Asynchronous API calls allow your application to perform other tasks without waiting for the AWS service to respond, improving the efficiency and performance of the application.

How can you secure your AWS API calls?

AWS API calls can be secured using AWS Identity and Access Management (IAM) for access control and AWS Signature Version 4 for message signing.

When working with Amazon Kinesis Data Streams, which API operation allows you to send data into the stream?

The “PutRecord” API operation allows you to send data into the stream.

What AWS service would be best for monitoring your data processing application’s API calls and associated latency?

Amazon CloudWatch would be the best service for monitoring your data processing application’s API calls and associated latency.

What do you need to include in an API request to AWS services?

An API request to AWS services minimally needs to include the AWS endpoint, the operation name or the API action, and the necessary parameters for that action. It should also be signed with your AWS security credentials.

What is an advantage of using AWS SDKs for making API calls?

Using AWS SDKs eases the process of making API calls by providing a set of libraries that are consistent, simplistic, and compatible with various programming languages. It also automatically handles error retry and exponential backoff logic.

Which feature of AWS would you use to track API calls to data services for auditing purposes?

You would use AWS CloudTrail to track API calls to your data services for auditing purposes.

In an AWS architecture, how can you efficiently handle sudden spikes in API requests from your application?

Sudden spikes can be handled efficiently by using services that offer auto-scaling features, like EC2 Auto Scaling, or by distributing traffic using load balancers. In addition, it’s useful to design systems with scalability and elasticity in mind.

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