Analytics | AWS Data Pipeline Flashcards
What is AWS Data Pipeline?
General
AWS Data Pipeline | Analytics
AWS Data Pipeline is a web service that makes it easy to schedule regular data movement and data processing activities in the AWS cloud. AWS Data Pipeline integrates with on-premise and cloud-based storage systems to allow developers to use their data when they need it, where they want it, and in the required format. AWS Data Pipeline allows you to quickly define a dependent chain of data sources, destinations, and predefined or custom data processing activities called a pipeline. Based on a schedule you define, your pipeline regularly performs processing activities such as distributed data copy, SQL transforms, MapReduce applications, or custom scripts against destinations such as Amazon S3, Amazon RDS, or Amazon DynamoDB. By executing the scheduling, retry, and failure logic for these workflows as a highly scalable and fully managed service, Data Pipeline ensures that your pipelines are robust and highly available.
What can I do with AWS Data Pipeline?
General
AWS Data Pipeline | Analytics
Using AWS Data Pipeline, you can quickly and easily provision pipelines that remove the development and maintenance effort required to manage your daily data operations, letting you focus on generating insights from that data. Simply specify the data sources, schedule, and processing activities required for your data pipeline. AWS Data Pipeline handles running and monitoring your processing activities on a highly reliable, fault-tolerant infrastructure. Additionally, to further ease your development process, AWS Data Pipeline provides built-in activities for common actions such as copying data between Amazon Amazon S3 and Amazon RDS, or running a query against Amazon S3 log data.
How is AWS Data Pipeline different from Amazon Simple Workflow Service?
General
AWS Data Pipeline | Analytics
While both services provide execution tracking, retry and exception-handling capabilities, and the ability to run arbitrary actions, AWS Data Pipeline is specifically designed to facilitate the specific steps that are common across a majority of data-driven workflows – inparticular, executing activities after their input data meets specific readiness criteria, easily copying data between different data stores, and scheduling chained transforms. This highly specific focus means that its workflow definitions can be created rapidly and with no code or programming knowledge.
What is a pipeline?
General
AWS Data Pipeline | Analytics
A pipeline is the AWS Data Pipeline resource that contains the definition of the dependent chain of data sources, destinations, and predefined or custom data processing activities required to execute your business logic.
What is a data node?
General
AWS Data Pipeline | Analytics
A data node is a representation of your business data. For example, a data node can reference a specific Amazon S3 path. AWS Data Pipeline supports an expression language that makes it easy to reference data which is generated on a regular basis. For example, you could specify that your Amazon S3 data format is s3://example-bucket/my-logs/logdata-#{scheduledStartTime(‘YYYY-MM-dd-HH’)}.tgz.
What is an activity?
General
AWS Data Pipeline | Analytics
An activity is an action that AWS Data Pipeline initiates on your behalf as part of a pipeline. Example activities are EMR or Hive jobs, copies, SQL queries, or command-line scripts.
What is a precondition?
General
AWS Data Pipeline | Analytics
A precondition is a readiness check that can be optionally associated with a data source or activity. If a data source has a precondition check, then that check must complete successfully before any activities consuming the data source are launched. If an activity has a precondition, then the precondition check must complete successfully before the activity is run. This can be useful if you are running an activity that is expensive to compute, and should not run until specific criteria are met.
What is a schedule?
Functionality
AWS Data Pipeline | Analytics
Schedules define when your pipeline activities run and the frequency with which the service expects your data to be available. All schedules must have a start date and a frequency; for example, every day starting Jan 1, 2013, at 3pm. Schedules can optionally have an end date, after which time the AWS Data Pipeline service does not execute any activities. When you associate a schedule with an activity, the activity runs on it. When you associate a schedule with a data source, you are telling the AWS Data Pipeline service that you expect the data to be updated on that schedule. For example, if you define an Amazon S3 data source with an hourly schedule, the service expects that the data source contains new files every hour.
Does Data Pipeline supply any standard Activities?
Functionality
AWS Data Pipeline | Analytics
Yes, AWS Data Pipeline provides built-in support for the following activities:
CopyActivity: This activity can copy data between Amazon S3 and JDBC data sources, or run a SQL query and copy its output into Amazon S3.
HiveActivity: This activity allows you to execute Hive queries easily.
EMRActivity: This activity allows you to run arbitrary Amazon EMR jobs.
ShellCommandActivity: This activity allows you to run arbitrary Linux shell commands or programs.
Does AWS Data Pipeline supply any standard preconditions?
Functionality
AWS Data Pipeline | Analytics
Yes, AWS Data Pipeline provides built-in support for the following preconditions:
DynamoDBDataExists: This precondition checks for the existence of data inside a DynamoDB table.
DynamoDBTableExists: This precondition checks for the existence of a DynamoDB table.
S3KeyExists: This precondition checks for the existence of a specific AmazonS3 path.
S3PrefixExists: This precondition checks for at least one file existing within a specific path.
ShellCommandPrecondition: This precondition runs an arbitrary script on your resources and checks that the script succeeds.
Can I supply my own custom activities?
Functionality
AWS Data Pipeline | Analytics
Yes, you can use the ShellCommandActivity to run arbitrary Activity logic.
Can I supply my own custom preconditions?
Functionality
AWS Data Pipeline | Analytics
Yes, you can use the ShellCommandPrecondition to run arbitrary precondition logic.
Can you define multiple schedules for different activities in the same pipeline?
Functionality
AWS Data Pipeline | Analytics
Yes, simply define multiple schedule objects in your pipeline definition file and associate the desired schedule to the correct activity via its schedule field. This allows you to define a pipeline in which, for example, log files are stored in Amazon S3 each hour to drive generation of an aggregate report one time per day.
What happens if an activity fails?
Functionality
AWS Data Pipeline | Analytics
An activity fails if all of its activity attempts return with a failed state. By default, an activity retries three times before entering a hard failure state. You can increase the number of automatic retries to 10; however, the system does not allow indefinite retries. After an activity exhausts its attempts, it triggers any configured onFailure alarm and will not try to run again unless you manually issue a rerun command via the CLI, API, or console button.
How do I add alarms to an activity?
Functionality
AWS Data Pipeline | Analytics
You can define Amazon SNS alarms to trigger on activity success, failure, or delay. Create an alarm object and reference it in the onFail,onSuccess, or onLate slots of the activity object.
