Establishing a Basic Data Extraction, Transformation, and Loading (ETL) Pipeline Using AWS Lambda for Data Science Operations

Establishing a Basic Data Extraction, Transformation, and Loading (ETL) Pipeline Using AWS Lambda for Data Science Operations

AWS Lambda is a serverless compute service that runs small, event-driven code functions without the need for managing servers. This service is ideal for lightweight, short-duration tasks, such as triggering ETL steps on new files or streaming data events.

Understanding AWS Lambda

AWS Lambda is a piece of code that is executed in response to an event in AWS. You can deploy the function using the AWS CLI and a script that packages the Python libraries. The function can take a DataFrame, the type of data, and the IMDB ID as parameters.

The function's timeout can be configured, depending on the execution time of the function. It can also access query string parameters via the event object in the Lambda function. The function can write data to JSON files in an S3 bucket, and extended logging can be added to CloudWatch by modifying the Log Format for the API Gateway Stage.

Serverless Computing in ETL Pipelines

In the context of ETL pipelines, serverless computing is an architectural approach where you build and run ETL workflows without provisioning or managing servers at all. It leverages services like AWS Lambda, but also others such as AWS Kinesis, AWS Glue, and managed transient clusters (e.g., EMR launched via Lambda), orchestrated to process, transform, and move data seamlessly at scale and often in real-time or near-real-time.

Key differences in ETL context:

| Aspect | AWS Lambda | Serverless Computing in ETL Pipelines | |-------------------------|-----------------------------------------------|-------------------------------------------------------| | Definition | A serverless function service | An architectural pattern using multiple serverless services to build ETL pipelines | | Scope | Executes discrete pieces of code (functions) triggered by events | End-to-end data pipeline management including ingestion, transformation, orchestration, and storage | | Runtime Limits | Max 15 minutes execution, limited memory & CPU | Depends on underlying services; can include batch jobs, streaming services, and orchestration tools without such strict limits | | Use Cases in ETL | Triggering jobs, micro-batches, lightweight transformations | Full pipelines handling streaming data ingestion, real-time transformations, orchestration of resources (e.g., launching EMR clusters), batch jobs | | Examples | Parsing logs on file upload, triggering Spark clusters | Real-time fraud detection with Kinesis + Lambda, launching transient EMR clusters via Lambda, IoT data processing with Lambda + IoT Core | | Management | Automatically scales code function executions | Uses multiple managed services to build fully managed pipelines without servers | | Limitations | Not suited for long-running or heavy compute ETL jobs alone | Serverless pipelines can integrate services to overcome individual limits (e.g., longer jobs run on EMR) |

Using AWS Lambda for ETL Jobs

AWS Lambda can be a useful tool for simple ETL jobs. For example, it can be a wonderful way to think about ETL for smaller jobs that need to run frequently. To add support for Pandas in a Lambda function, a layer needs to be added that supports the AWS SDK for Pandas.

Events that trigger an AWS Lambda function can be API requests, file uploads to an S3 bucket, or scheduled events. The cost of AWS Lambda is based on the computing time consumed, with no charge when the code is not running.

From the AWS Console, navigate to the Lambda service to create a new Lambda function. When creating a Lambda function, you can choose to create a new role or select an existing one. CloudWatch monitoring is enabled by default when using an API Gateway. The Parameters and Secrets Extension can be used to store sensitive data in the AWS Secrets Manager and access it in the Lambda function.

In summary, AWS Lambda is a single service within the serverless computing model, designed for running short, event-driven code snippets. Serverless computing in ETL pipelines refers to building entire data processing workflows without managing servers, often combining Lambda with other AWS managed services to achieve scalable, flexible, and cost-efficient ETL systems. Lambda functions often act as the glue or event handlers in a larger serverless ETL architecture. The full code for this project can be found on GitHub.

• The use of AWS Lambda in an ETL pipeline context is an architectural approach called serverless computing, which involves building and running ETL workflows without managing servers. This method leverages AWS Lambda, along with other services like AWS Kinesis, AWS Glue, and EMR launched via Lambda, to process data seamlessly at scale.
• In serverless computing for ETL pipelines, AWS Lambda serves as a valuable tool for simple ETL jobs, particularly those that need to run frequently. For example, it can be an effective way to approach smaller jobs requiring the usage of Pandas, with the AWS SDK for Pandas layer added to provide support.

Read also: