The majority of this post will focus on using AWS Data Pipeline and AWS Glue, two essential tools in AWS for managing data workflows.
AWS Data Pipeline Process
AWS Data Pipeline is a web service that you can use to automate the movement and transformation of data. With AWS Data Pipeline, you can define data-driven workflows, so that tasks can be dependent on the successful completion of previous tasks.
Define and Configure the Data Pipeline
- The first step involves defining the pipeline by identifying the data source, the data processing activities, and the data output location.
- Once you have created a pipeline definition file in JSON format, you can use the AWS Management Console, AWS CLI, or AWS SDKs to configure AWS Data Pipeline.
Schedule Data Pipeline Activities
You can configure AWS Data Pipeline to execute activities at specific times by setting “period” and “startDateTime” fields in your pipeline definition.
Example:
{
“id” : “Activity1”,
“type” : “CopyActivity”,
“schedule” : { “ref” : “DefaultSchedule” },
…
},
{
“id” : “DefaultSchedule”,
“startDateTime” : “2012-12-12T00:00:00”,
“period” : “1 day”,
“type” : “Schedule”
}
This example sets the pipeline to run daily (1 day) starting from 12 December 2012.
Managing Dependencies
AWS Data Pipeline allows you to control the sequence of your data processing by managing dependencies. When you add a new Activity in your pipeline definition file, you can specify its “dependsOn” field to ensure it only starts once a certain other Activity has completed.
AWS Glue
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for users to prepare and load their data for analytics. AWS Glue can create, run, and manage jobs on a schedule or based on an event.
Setup ETL job using AWS Glue
- The first step involves defining the ETL job. You need to indicate the data source, data target, and the type of transformations that need to be performed.
- Once the job is defined, you can then schedule it. AWS Glue allows two types of scheduling: time-based and event-based. In the latter, the job can be triggered based on events in another AWS service.
Example of creating a job:
aws glue create-job –name “My job” –default-arguments {…}
Scheduling AWS Glue ETL jobs
You can control how frequently your ETL jobs run and when they start by creating a schedule when you create an ETL job.
Example:
{
“Schedule”: {
“ScheduleExpression”: “cron(15 12 * * ? *)”
}
}
This example sets up a job to run daily at 12:15 PM (UTC).
Managing Dependencies
To manage dependencies in AWS Glue, you can configure job bookmarks. AWS Glue tracks data that has already been processed during a previous run of an ETL job by storing state information from the job run.
To summarize, both AWS Data Pipeline and AWS Glue offer sophisticated methods to schedule tasks and manage dependencies. Understanding them enhances your ability to harness AWS services effectively and takes you one step closer to obtaining the AWS Certified Data Engineer – Associate (DEA-C01) accreditation. However, AWS Glue offers greater flexibility and functionality, especially in its ability to integrate with other AWS services and its advanced job scheduling features.
Practice Test
True or False: The AWS Data Pipeline service allows you to configure and automate the movement and transformation of data between different AWS services.
- True
- False
Answer: True
Explanation: AWS Data Pipeline is a web service that helps you reliably process and move data between different AWS compute and storage services.
True or False: It is not possible to make AWS Data Pipelines react to data-driven events.
- True
- False
Answer: False
Explanation: AWS Data Pipelines can be configured to react to data-driven events using Amazon EventBridge, allowing automation of workflows based on changes in data.
What services can be integrated when creating a data pipeline on AWS?
- A. Amazon S3
- B. Amazon Redshift
- C. Amazon DynamoDB
- D. All of the above
Answer: D. All of the above
Explanation: AWS Data Pipeline supports different AWS storage and compute services including Amazon S3, Amazon Redshift, Amazon DynamoDB, and several others.
True or False: AWS Data Pipeline allows you to schedule data transfer only on a daily basis.
- True
- False
Answer: False
Explanation: AWS Data Pipeline allows you to schedule data transfer and transformations on an hourly, daily, weekly, or monthly basis, or based on a specific time interval.
Which AWS service would you use to schedule executables or scripts?
- A. AWS Glue
- B. AWS Lambda
- C. Amazon EC2 Auto Scaling
- D. Amazon EventBridge
Answer: B. AWS Lambda
Explanation: AWS Lambda is a serverless compute service that allows you to run code without provisioning or managing servers. You can trigger AWS Lambda to run a script or an executable at set schedule.
True or False: AWS Glue cannot be used to schedule ETL jobs.
- True
- False
Answer: False
Explanation: AWS Glue is a fully managed ETL (extract, transform, and load) service that is able to schedule and run ETL jobs.
Which of the following is not a feature of AWS Data Pipelines?
- A. Error handling
- B. Scheduling
- C. Instant data transformation
- D. Dependent chaining of activities
Answer: C. Instant data transformation.
Explanation: While AWS Data Pipelines supports scheduling, dependent chaining of activities and error handling, instant data transformations are not directly supported. Transformations take place via activities within the pipeline.
True or False: AWS Data Pipeline allows us to perform operations only on the data stored in AWS Storage services.
- True
- False
Answer: False
Explanation: AWS Data Pipeline doesn’t just work with data in AWS services. It can access and perform operations on on-premise data or data in other cloud platforms as well.
Which of the following AWS services can be used to react based on specific state changes to your AWS resources?
- A. AWS Lambda
- B. AWS Glue
- C. Amazon RDS
- D. Amazon EventBridge
Answer: D. Amazon EventBridge
Explanation: Amazon EventBridge is a serverless event bus service that you can use to connect your applications with data from a variety of sources and send that data to your AWS resources.
True or False: Data transformation and data movement in AWS Data Pipelines can be done on a visual interface.
- True
- False
Answer: True
Explanation: AWS Data Pipelines provides a drag-and-drop console to visually create and manage complex data processing workflows.
True or False: AWS Glue is a fully managed Extract, Transform, and Load (ETL) service that makes it easy to categorize your data, clean it, enrich it, and move it reliably between various data stores.
- True
- False
Answer: True
Explanation: AWS Glue indeed is a ETL service that facilitates various operations like moving the data among data stores, data cleansing, and enrichment.
In AWS Glue, the scheduler relies on which of the following?
- A. A time-based schedule
- B. A job bookmark
- C. The successful completion of another job
- D. All of the above
Answer: D. All of the above
Explanation: In AWS Glue, you can configure the trigger for a job or a crawler on a time-based schedule, on job bookmark, or based on the successful completion of another job.
Interview Questions
What AWS service would you use to trigger a process in your data pipeline based on a schedule?
AWS provides a service known as AWS Glue that can be utilized to schedule and trigger ETL jobs.
How do AWS Data Pipeline Dependencies work?
AWS data pipeline ensures that the dependencies that you define as preconditions are met before the actions defined in your pipeline definition are executed. This can include checking whether the defined S3 paths exist or that a certain amount of data is present.
What is AWS Glue and how is it used in creating data pipelines?
AWS Glue is a fully-managed service that provides a data catalog to make data in the data lake discoverable. It has the ability to schedule, orchestrate, and execute ETL workloads. AWS Glue can be used in data pipelines to automate the time-consuming data preparation steps.
How can you schedule AWS Glue ETL jobs?
AWS Glue ETL jobs can be scheduled using cron expressions that AWS Glue understands. You can schedule jobs on AWS Glue console, CLI or SDK.
How does AWS Lambda function help in triggering a data pipeline?
AWS Lambda can be used to respond to events in AWS Data Pipeline. For example, when a precondition fails, AWS Lambda can respond by triggering another Data Pipeline.
What AWS service can be used to coordinate multiple AWS services into a serverless workflow?
AWS Step Functions can be used to coordinate multiple AWS services into serverless workflows enabling the building and updating of applications quickly.
What is a Data Pipeline in the context of AWS?
AWS Data Pipeline is a web service for orchestrating and automating the movement and transformation of data between different AWS services and on-premise data sources.
What is the role of Amazon S3 in AWS data pipelines configuration?
Amazon S3 is often used as the destination and the source for data pipelines. It serves as a highly durable and scalable storage service for data pipeline originating from or destined for other AWS services.
What AWS service can be used to monitor the execution of AWS Data Pipelines?
Amazon CloudWatch can track metrics, collect and monitor log files, set alarms, and automatically react to changes in your AWS resources.
How can dependencies be defined in the AWS Data Pipeline?
Dependencies can be defined in AWS Data Pipeline through a series of preconditions like the presence or absence of specific S3 objects.
How does AWS Glue treat a failure of an ETL job?
AWS Glue sets the JobRunState to FAILED status if the ETL job fails – this can be because of an invalid script, a missing script, or a failure to retrieve the script from Amazon S3.
How can you trigger an action on AWS based on new data arrival in S3 bucket?
You can make use of Amazon S3 event notifications. You can set up a notification to trigger a Lambda function or AWS Glue ETL job as soon as new data gets uploaded on the S3 bucket.
How can you orchestrate conditional branching in a data pipeline based on the outcome of an earlier action?
AWS Step Functions can be used to implement complex workflows including conditional branching, parallel execution, and error handling in a pipeline.
How to improve data processing time in AWS Glue?
You can improve data processing time by increasing DPUs (Data Processing Units) for the ETL job or partitioning data into smaller chunks.
How can we retrying failed executions in AWS Glue?
AWS Glue automatically retries any non-timeout failures three times. Besides this, you can customize the retry options while defining job properties.
extutorials.com