Concepts

AWS Data Pipeline is a web service designed to facilitate the process of moving and processing data. To create a data pipeline based on schedules or dependencies, you can follow these steps:

  1. Create a new pipeline:
    • Open the AWS Data Pipeline console.
    • Click on “Create new pipeline.”
    • Enter a name for your pipeline and a unique pipeline identifier.
  2. Define data nodes:
    • Data nodes are endpoints in your pipeline, such as Amazon S3 buckets, DynamoDB tables, or RDS databases.
    • Choose the source and destination data nodes.
  3. Set up a schedule:
    • To run the pipeline on a schedule, you can define the schedule within the pipeline definition.
    • You can set the pipeline to execute on a specific schedule using cron expressions.
  4. Configure activities and dependencies:
    • Within the pipeline, you’ll define activities, such as data copy or SQL activity.
    • If activities have dependencies, ensure to specify this in the pipeline definition. One activity can depend on the success of another.
  5. Activate the pipeline:
    • Save your pipeline definition and activate it.
    • AWS Data Pipeline will automatically run your activities based on the schedule or resolve the dependencies you’ve defined.

For example, a copy activity in AWS Data Pipeline could look similar to this:

{
“id”: “DataCopyActivity”,
“type”: “CopyActivity”,
“schedule”: {
“ref”: “DefaultSchedule”
},
“input”: {
“ref”: “InputDataNode”
},
“output”: {
“ref”: “OutputDataNode”
}
}

Using AWS Glue

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load data. To set up a scheduled or event-triggered job in AWS Glue:

  1. Define a Glue ETL job:
    • Navigate to the AWS Glue Console and click on the Jobs tab to define a new job.
    • Choose an IAM role that has the necessary permissions.
    • Specify the source from where to read the data and the target where to write it.
  2. Set up a trigger:
    • You can create a new trigger by selecting “Triggers” from the left-hand pane.
    • Create a schedule-based trigger using cron expressions or choose to make it event-based, triggering off the success or failure of another job.
  3. Script editing:
    • AWS Glue generates a PySpark or Scala script which can be further configured or edited to meet the requirements of your data processing workflow.
  4. Run and monitor the job:
    • Once configured, the job can be manually run or will be triggered based on the defined conditions.
    • Monitor the job execution within the AWS Glue console.

For instance, a scheduled trigger in AWS Glue using cron syntax may look like this:

glue.create_trigger(
Name=’DailyETLTrigger’,
Schedule=’cron(0 12 * * ? *)’, # This means the job will run daily at 12:00 UTC
Type=’SCHEDULED’,
Actions=[{‘JobName’: ‘MyGlueETLJob’}]
)

Using Amazon EventBridge

Amazon EventBridge (formerly CloudWatch Events) is a serverless event bus that makes it easier to build event-driven applications at scale. It can also be leveraged to trigger AWS services according to a schedule or system events.

  1. Create an EventBridge rule:
    • Go to the EventBridge console.
    • Create a new rule with a detailed schedule expression or link it to specific AWS service event patterns.
  2. Define the target:
    • The target could be another AWS service that you would like to invoke when the rule is triggered.
    • For data pipelines, the target can be a Lambda function, ECS task, or Step Functions state machine, among others, to carry out the data pipeline activities.
  3. Set input parameters:
    • Depending on the target, you can specify input parameters that will dictate the operation to be executed when the rule is triggered.
  4. Enable and monitor events:
    • Once configured, enable the rule to start observing for the defined pattern or schedule.
    • Monitor the rule’s execution and associated targets from the EventBridge console.

For example, a cron-based EventBridge rule to trigger a Lambda function could be established as follows:

{
“ScheduleExpression”: “cron(15 10 ? * MON-FRI *)”, // Triggers at 10:15 AM (UTC) on weekdays
“Name”: “MyDataPipelineScheduler”,
“Targets”: [
{
“Arn”: “arn:aws:lambda:region:account-id:function:MyDataFunction”,
“Id”: “myLambdaFunction”
}
]
}

By leveraging AWS services such as AWS Data Pipeline, AWS Glue, and Amazon EventBridge, you can create robust data pipelines that operate on a schedule or based on interdependent events, thus fulfilling diverse data processing needs for your applications. Remember to always ensure that your IAM roles are properly configured and that you monitor the cost and execution of your pipelines to maintain efficiency.

Answer the Questions in Comment Section

True or False: Amazon Simple Storage Service (Amazon S3) is an appropriate tool to manage scheduled or dependency-based data pipeline workflows.

  • A) True
  • B) False

Answer: B) False

Explanation: Amazon Simple Storage Service (Amazon S3) is a storage service and does not manage workflows or pipelines. AWS Data Pipeline or AWS Step Functions would be appropriate for managing workflows.

In AWS Data Pipeline, which AWS resource can you use as a Scheduler?

  • A) AWS Lambda
  • B) Data Pipeline
  • C) Amazon EC2
  • D) AWS Glue

Answer: B) Data Pipeline

Explanation: AWS Data Pipeline has a built-in scheduler that can run tasks based on defined schedules.

What AWS service can you use to run serverless ETL jobs on a schedule?

  • A) AWS Lambda
  • B) AWS Batch
  • C) AWS Glue
  • D) Amazon RDS

Answer: C) AWS Glue

Explanation: AWS Glue can be scheduled to run ETL jobs without provisioning any servers, making it a serverless solution for ETL workloads.

True or False: AWS Step Functions can be used to define and coordinate a sequence of AWS Lambda functions in a serverless workflow.

  • A) True
  • B) False

Answer: A) True

Explanation: AWS Step Functions is used to coordinate multiple AWS services into serverless workflows, and it can sequence AWS Lambda functions.

Which feature allows AWS Data Pipeline to activate tasks only after certain preconditions are met?

  • A) Task Runner
  • B) Preconditions and dependencies
  • C) Schedulers
  • D) Data nodes

Answer: B) Preconditions and dependencies

Explanation: Preconditions and dependencies in AWS Data Pipeline enable you to specify conditions that must be true before a task executes.

True or False: Amazon CloudWatch Events can trigger AWS Lambda functions based on a time schedule.

  • A) True
  • B) False

Answer: A) True

Explanation: Amazon CloudWatch Events can be used to trigger AWS Lambda functions on a specified schedule, such as every hour or daily.

When defining an AWS Data Pipeline, which field allows you to specify an execution schedule using a cron expression?

  • A) scheduleInterval
  • B) period
  • C) schedule
  • D) cron

Answer: C) schedule

Explanation: The ‘schedule’ field in AWS Data Pipeline allows you to specify an execution schedule using cron expressions for time-based automation.

True or False: Amazon EventBridge (formerly CloudWatch Events) cannot integrate with AWS Step Functions.

  • A) True
  • B) False

Answer: B) False

Explanation: Amazon EventBridge can start AWS Step Functions state machines in response to events, enabling integration between these two services.

Which AWS service is designed specifically for workflow-driven data processing?

  • A) AWS Lambda
  • B) AWS Batch
  • C) AWS Data Pipeline
  • D) Amazon S3

Answer: C) AWS Data Pipeline

Explanation: AWS Data Pipeline is specifically designed for workflow-driven data processing and supports both schedule- and dependency-based workflows.

What is the main advantage of using AWS Step Functions for managing dependencies in data pipeline workflows?

  • A) Cost
  • B) Scalability
  • C) Ease of monitoring
  • D) Visual workflow management

Answer: D) Visual workflow management

Explanation: AWS Step Functions provides a visual interface to manage complex workflows and dependencies, helping users to visualise and debug the workflow executions.

True or False: AWS Glue workflows can automatically trigger jobs based on the completion of a preceding job without any external schedulers.

  • A) True
  • B) False

Answer: A) True

Explanation: AWS Glue workflows support dependency resolution where a job can be automatically triggered after the successful completion of a previous job, eliminating the need for an external scheduler.

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Chris Mills
8 months ago

Great post on configuring AWS services for data pipelines. It clarified a lot of things for me. Thanks!

Michaele Stroh
7 months ago

Fantastic tutorial! Could you elaborate more on using AWS Step Functions for managing dependencies in a data pipeline?

Silvia Peña
6 months ago

I tried following the steps but got stuck at setting up the IAM policies for different AWS services. Any advice?

Özsu Arıcan
7 months ago

This guide is really helpful. I’m particularly interested in the use of Lambda for data cleansing. Could you expand on that?

Chloe Walker
8 months ago

I appreciate the detailed explanation on using CloudWatch for scheduling tasks. It was spot on!

Carol Rose
6 months ago

Just a quick thank you for this comprehensive guide. It answered so many of my questions!

Kenan Elçiboğa
8 months ago

Using Glue for ETL jobs is a bit confusing for me. Any more insights?

Nathan Anderson
7 months ago

This post didn’t cover error handling in data pipelines. Any thoughts?

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