Contributed by: Komal Khullar
A data pipeline is the movement of data to a destination for storage and analysis, involving a set of actions that ingest raw data from disparate sources. It is a group of data processing elements connected during a series where the output of 1 element is an input to the subsequent one. In other words, the most purpose of the data pipeline is to determine the flow of data between two or more data sources. Businesses generate enormous amounts of data that must be analyzed to derive business value. Analyzing the data within the systems where it is created is not ideal, so data pipelines become important.
The data pipeline involves these steps:
- Copy data from the source to destination
- Moving the data from an on-premise location into the cloud
- Reformatting the data or joining the data with other data sources
The data pipeline is a summation of all the above steps, and its job is to make sure that these steps are performed in a reliable way on all or any data involved. A properly managed data pipeline provides organizations with a well-structured dataset for performing analytics.
The various technology tools available for data pipeline include the below:
- Data warehouses
- ETL tools
- Data prep tools
- Programming languages like Python, Java, Ruby to write processes
- AWS data pipelines which is a workflow management service to schedule and execute data movement and processes
- Kafka, a real-time event streaming platform to move and transform data
Having understood the concept of a data pipeline in the paragraphs above, we will use this blog as a learning exercise to learn about the data pipeline with a primary focus on AWS data pipeline. This blog will broadly cover the below topics:
- About AWS Data Pipeline
- Need for AWS Data Pipeline
- Advantages of AWS Data Pipeline
- Components of AWS Data Pipeline
- How to create an AWS Data Pipeline
About AWS Data Pipeline
AWS data pipeline is a web service that helps in the movement of data, at specified intervals, between different AWS compute and storage services and on-premise data sources. AWS data pipelines help regularly access the data at the destination where it is stored, transform and process the data, and efficiently transfer it to various AWS services like Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR.
Need for AWS Data Pipeline
Data is increasing at a rapid pace. Data processing, storage, management, and migration is becoming very complex and time-consuming than it used to be in the past. The data is getting complicated to deal with due to the below-listed factors:
- Bulk data getting generated which is mostly in raw form or is unprocessed.
- Different formats of data- the data being generated is unstructured. It is a tedious task to convert the data to compatible formats.
- Multiple storage options- there are a variety of data storage options. These include data warehouses or cloud-based storage options like those of Amazon S3 or Amazon Relational Database Service (RDS)
Advantages of AWS Data Pipeline
AWS data pipeline comes with multifold advantages and has several benefits attached to it. In the below section, we will consolidate the main reasons why data pipelines are so important. Let us discuss these benefits one by one.
Reliable:
AWS data pipeline is fault-tolerant, reliable, and easy to build and troubleshoot. It can recover from bugs and failures without requiring much manual intervention.
Easy to Use:
There is a provision of a drag and drop console in AWS to creating a pipeline. This can be done in a fast and simple manner. No additional logic has to be written to use the data pipeline in the AWS; instead, the preconditions are existing and configured in the AWS system. To get started with the AWS data pipeline, you just need to access the AWS management console and create a pipeline using a graphical editor. Also, the Amazon data pipeline has an available library consisting of data pipeline templates which makes it extremely simple to use this service.
Flexible:
AWS data pipeline offers a lot of flexibility. The data pipeline of AWS can be used to configure and run tasks like those of Amazon EMR or to run SQL queries directly on the databases. AWS data pipelines also assist in executing custom applications running at the organizations’ data centers or on Amazon EC2 instances. This helps in the easy analysis and processing of data when dealing with the complexities that may otherwise be involved when handling the data.
Scalable:
AWS data pipeline has a flexible design. The feature of flexibility extends to making the data pipelines highly scalable. AWS data pipeline enables processing a million count of files and makes it as easy as processing a single file. AWS data pipelines make it easy to move work to one machine or many, in serial or parallel.
Low Cost:
AWS data pipeline is available to be tested on a trial basis in the free usage service offered by AWS. Otherwise, data pipeline is a low-cost service offered by AWS, and billing happens for it on a monthly basis. The offering is highly economical.
Transparency:
Full control is available over the resources used to execute the logic or to rectify the logic used. The logs are saved in the storage service Amazon S3, a record of the pipeline that can be used to check what is happening or had happened in a pipeline.
All the benefits of AWS data pipelines ensure that the data pipelines are robust and highly available.
Components of AWS Data Pipeline
AWS data pipeline consists of the following components.
- Pipeline Definition- it is about how data must interact and communicate with the data pipeline. This would include the following details or information:
- Data Nodes- which refers to the name, format, and location of the data source and is a representation of the business data. A data node can be used to reference a specific path. For example, you could specify that your Amazon S3 data format is s3://example-bucket/my-logs/logdata-#{scheduledStartTime(‘YYYY-MM-dd-HH’)}.tgz.
- Activity- refers to performing SQL queries on the database, command-line scripts, and transformation of data from one source to another.
- Schedule- It is about defining the run and frequency with which the data should be available for the service. Every schedule has a start date and frequency. For instance, every day, starting Jan 1, 2021, at midnight. It is also possible to have an end date tagged to the schedule, after which no activity will be performed or executed.
- Preconditions- there are certain pre-conditions where the AWS data pipeline provides the built-in support. These preconditions are listed as below:
- DynamoDBDataExists: This precondition looks for the presence of data inside a DynamoDB table.
- DynamoDBTableExists: This precondition looks for the presence of a DynamoDB table.
- S3KeyExists: This precondition looks for the presence of a specific AmazonS3 path.
- S3PrefixExists: This precondition finds out for at least one file present within a specific path.
- ShellCommandPrecondition: This precondition runs an arbitrary script on your resources and checks that the script succeeds
- Resources- there must be computed resources available like those of Elastic Cloud Compute (EC2)
- Actions-refers to update on the pipeline via an alarm or an email
- Pipeline- it contains the below:
- Pipeline Components- refers to the communication of the data pipelines with AWS resources.
- Instances- when compilation happens across all components, then an instance is created to perform a specific task.
- Attempts- this is a feature of the AWS data pipeline that attempts retrying when the operation fails.
- Task runner- as the name suggests, is an application that takes out tasks from the data pipeline for execution. It updates the status when the task is completed. In case the task is completed, then the process ends while if the task has failed, the task runner checks and makes re-try attempts and perform the process for completion of the pending tasks.
The components defined above reflect on the flow involved using the components of the AWS data pipeline.
How to create an AWS Data Pipeline
The snapshots and steps will take you through the demonstration and show how an AWS data pipeline can be created.
- The first and foremost step required in this direction is to sign in to the AWS management console.
- Our first step would be to create a DynamoDB table and S3 buckets
- Our final step will be to learn about how to create an AWS data pipeline
Step 1: To create a DynamoDB table, click on create table (as shown in the picture below)
Step 2: Assign a name to the table and define a primary key to create a table.
The below snapshot is to shows that a table has been created. The name of the table is “student.” There is an “items” tab in the below snapshot. “items” tab is to be clicked to create an item. For our example, we will consider 3 items to be added, which will be shown in the subsequent snapshot.
Three items i.e., ID, name, and gender are added. The snapshot will show the same for reference and understanding.
Step 3: Creating an item in a table
Step 4: The below image is telling us how the data is inserted in a DynamoDB table.
Step 5: Creating S3 buckets: The creation of S3 buckets is our next step. It is a two-step process. In the first step, we will store the data being exported from DynamoDB. In the second step, we will have a look at how to store the logs.
Two S3 buckets are created. The name of these buckets is showing in the image below as “logstoredata” and “studata”. The “studata” contains the data being exported from DynamoDB, while “logstoredata” has the logs.
Step 6: We are now getting closer to creating the data pipeline, and the below steps will show you the process and methods to proceeding with creating a data pipeline.
In the services section of the AWS management console, move to the “AWS Data Pipeline” service as demonstrated in the snapshot appended.
While creating a pipeline and also to get started, details have to be filled in. The details can be modified anytime using the “edit on architect” option, as displayed in the image below.
In case the “edit architect” option is to be used to modify the details, the below screen will show up. The details can be modified, and also, the warning being shown “TerminateAfter is missing” has to be addressed. Once the new field in TerminateAfter is added using the “Resources” tab, press the “Activate” button to complete the process.
Initially, WAITING_FOR_DEPENDENCIES status will appear on the screen. When the screen is refreshed, it will change the status to WAITING_FOR_RUNNER. As soon as the Running state appears, you can check your S3 bucket. The data will be stored there.
The step-by-step procedure has taken you through the complete end-to-end visual representation of how an AWS Data Pipeline is created.
AWS Data Pipeline is an excellent option for the implementation of ETL workflows without the need to maintain a separate ETL infrastructure. The important thing to note here is that ETL should involve AWS components only. The experience with the use of AWS can be fulfilling. AWS data pipeline can help businesses in a big way to automate the movement and transformation of the data.
This blog has tried to explain to its reader about the AWS data pipeline in the simplest possible manner and includes the demonstration for better understanding. The readers who are willing to understand about AWS data pipeline in greater detail can look forward to learning more about cloud platforms, particularly AWS cloud services. There are professional courses available, and opting for one will be a good decision to educate yourself more.
