AWS Data Pipeline 101 What it is, and How it Keeps Data Organized

AWS Data Pipeline 101: What it is, and How it Keeps Data Organized

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As large volumes of data move through a business, it’s essential that it stays organized. AWS data pipeline is used by organizations that have chosen the AWS ecosystem for their data processing, storage, transformation, and distribution needs, as it works seamlessly with other AWS services and systems.

In this article, we’ll explore the importance and role of data pipelines for facilitating the movement of clean datasets throughout an organization and discuss the role of AWS data pipelines technology as it pertains to a modern organization’s data handling protocol.

Importance and Role of Data Pipelines

The simplest definition of a data pipeline is that it’s a technology that moves data between systems, programs, and applications. As data moves through the pipeline, it’s transformed and optimized so that when the data arrives at its destination, it’s ready to use.

Modern data pipelines, such as AWS data pipelines, have the ability to automate many of the processes of data processing and transformation before the data reaches its intended target, according to the needs of the system, program, or application the data is going to.

At a high level, data pipelines consist of three key elements:

  1. A source
  2. Processing step(s)
  3. A Destination

The source and destination also don’t necessarily have to be different in every case. The data pipeline can extract data from a source, process it accordingly, and then feed it back to the source. This would be useful if the purpose of data stored in a source location would need to be used for a different purpose than its original configuration can allow and needs to be changed to suit that new purpose.

Streaming Data Pipelines

With the rise of organizations using data streaming to gain real-time insights, streaming data pipelines that are able to handle a constant flow of large volumes of data are required. These pipelines need to be flexible enough able to route data across a variety of different data science architectures, including cloud, on-premises, or serverless. Combined with cloud-based data warehouses, organizations can easily scale computing and storage resources as needed.

Cloud-based data warehouses are a cost-effective solution for handling large volumes of real-time data because it allows organizations to bypass preload transformations and simply load all the raw data into the warehouse. The streamlining data pipeline also doesn’t require complex transformations to be written. Instead, data analysts are free to develop ad-hoc transformations for the pipeline to utilize to suit their needs without first needing to wait for data to be processed, transformed, stored, or mapped.

AWS Data Pipeline

The AWS pipeline works by providing the ability to create data-driven workflow definitions, allowing logic-based data transformations to take place. In other words, you define how data moves through your pipeline and how it should be transformed or optimized in different situations, and the AWS data pipeline executes it.

AWS Data Pipeline

Source

Even highly-complex data processing workloads can be created and executed through an AWS data pipeline without needing to spend time managing things like resource availability, inter-task dependencies, and more. With the AWS pipeline, data can be extracted from any source, including on-premises silos, and prepared and transformed into AWS services like Amazon S3, RDS, DynamoDB, and Amazon EMR.

It’s important to note that AWS is a data ecosystem that’s designed its technologies and services to work within that ecosystem, which is why the AWS pipeline is optimized for use with Amazon data storage solutions.

How the AWS Pipeline Keeps Data Organized

Data organization is handled by the AWS data pipeline architecture through the following features:

  • Data automation workflows allow seamless data transfer between sources and targets, eliminating the need for manual intervention and significantly reducing or eliminating instances of human error. Workflows are executed via scheduled operations where the success or failure of tasks within the workflow determines paths.
  • Comprehensive transformations are supported through services like HiveActivity, PigActivity, and SQLActivity. EMR cluster or on-premise cluster code-based transformations are also supported through HadoopActivity.
  • Allows the use of on-premises system(s) as a data source, or to assist with the transformation of data, assuming that the system is equipped with data pipeline task runners.

AWS Data Pipeline Architecture Core Components

AWS Data Pipeline Architecture Core Components

An AWS pipeline architecture consists of three core components that work in tandem to support effective data management from source to destination:

  1. Pipeline definitions, which control how data flows through the pipeline, including how it’s processed and transformed, and under what circumstances.
  2. Pipeline schedules, which automatically run and execute tasks via Amazon EC2 instances. The pipeline definition is uploaded to the AWS data pipeline and then activated. Edits to the pipeline’s processes and data source can also be done to a running pipeline; simply make the necessary changes and then restart the pipeline for them to take effect.
  3. Task runners, which run automatically and are responsible for executing tasks according to your pipeline definition. Custom task runners can be coded manually, or you can use the pre-existing Task Runner application provided by the AWS data pipeline.

For accessing and managing your AWS data pipeline, The following interfaces can be used:

  • AWS management console, which is a web-based interface that allows direct access to your AWS data pipeline environment.
  • AWS command line interface (AWS CLI), which provides commands for various AWS services, including AWS data pipeline (e.g., active-pipeline, add-tags, create-pipeline, and more) and has support for Windows, macOS, and Linux.
  • AWS SDKs, which provide language-specific APIs and handle connection details like signature calculation, request retries, error handling, and more.
  • Query API, which provides low-level APIs that can be called with HTTPS requests that use the HTTP verb GET or POST and a Query parameter named Action. Using the Query API provides the most direct access to the AWS pipeline, but because of the nature of the Query API, low-level details like generating the hash to sign the request and error handling need to be handled by your application.

Overall, the AWS data pipeline is relatively easy to use and allows effective management of data flow via predefined templates.

AWS Data Pipeline and Revelate

Revelate is a platform-agnostic solution, which means that it will work with any data source and be able to transfer data to any target (depending on the configuration of the dataset). The Revelate data platform supports hosted AWS S3 buckets, and data providers can either upload data to the bucket directly, through the Revelate Provider SFTP or via external buckets. However, depending on the region the data provider is located in, data transfer costs associated with this process may differ.

In addition, the AWS data pipeline is a closed data movement system for handling large amounts of data and processing complex data workloads within the AWS system only. If you’d like to move data out of the system, using the AWS data pipeline (or any AWS system) isn’t possible alone. That’s where Revelate comes in as a solution since it’s platform agnostic and can extract data from any system.

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Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

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AWS Data Pipeline vs AWS Glue

While AWS data pipeline has been a staple for managing data flow throughout an organization for years, increases in the complexity and volume of data movement, such as with data streams and the use of the cloud mean that technologies support the movement and transformation of data have had to evolve.

AWS glue handles every aspect of ETL via jobs. These jobs contain definitions, API operations, and notifications. To facilitate the requested job, AWS glue handles the infrastructure, coding, and connectivity to AWS systems. Jobs can be requested on demand or automatically occur via a specific trigger that indicates to the system that a specific job must start.

AWS Data Pipeline vs AWS Glue

The biggest difference between AWS data pipeline and AWS glue is that with AWS glue, you don’t have to manage any infrastructure.

With the AWS data pipeline, there is a reliance on EC2 instances (a virtual server in AWS cloud that provides scalable computing capacity for running applications), but since AWS glue is serverless, this reliance isn’t required. At its core, AWS glue consolidates major data integration capabilities into a single service.

AWS data pipeline vs. AWS glue can be summarized in the following table:

Category AWS data pipeline AWS glue
User interface Drag-and-drop, option for web-based management console, CLI Visual and code-based options
Batch/streaming Supports batch Supports batch and streaming
Infrastructure management Requires the uses of a server, such as EC2 instances Is a serverless solution that utilizes Apache Spark
Operational methods Allows data transformations through APIs and JSON, and supports Redshift, SQL, DynamoDB, EMR platforms, and Shell Supports the Apache Spark framework, as well as Redshift, SQL, Amazon RDS, S3, and DynamoDB
Compute engine & compatibility Can be used with a variety of engines such as HiveActivity or PigActivity Runs ETL jobs in a serverless Apache Spark environment
Pricing AWS data pipeline pricing is based on how often activities are scheduled to run, and where they run (e.g., AWS or on-premesis) Charges an hourly rate (which is billed by the second), for crawlers and ETL jobs
Compliance and Security Not natively compliant with regulations such as GDPR and HIPPA Natively compliant with GDPR and HIPPA

Table sources: 1, 2

When do you use AWS Data Pipeline?

AWS data pipeline makes the most sense to use if you want to occasionally schedule and manage data processing jobs on AWS systems that have complex requirements but don’t require the use of Apache Spark.

Some example AWS data pipeline use cases include:

  • Loading AWS log data to Redshift
  • Data loads and extracts between Redshift, RDS, and S3
  • DynamoDB backup and recovery

When do you use AWS Glue?

If you want to discover more properties of the data you own, whether structured or unstructured, then AWS glue provides a highly scalable solution with capabilities for complex data preparation.

Some example use cases for AWS glue include:

  • Running jobs on Apache Spark-based platforms
  • Processing streaming events
  • Designing complex ETL pipelines

Advantages and Disadvantages of AWS Data Pipeline

Advantages and Disadvantages of AWS Data Pipeline

The advantages and disadvantages of using AWS data pipeline largely depends on your business needs with regards to data and how well the service can fulfill them, including factors such as budget, scalability, and your organization’s current data management tech stack.

The advantages of using the AWS data pipeline are outlined below:

1. Reliability

The AWS data pipeline architecture is built on highly available infrastructure that automatically retries failed activities. You’re automatically notified if an activity consistently fails, however, allowing you to take action as needed.

2. Ease of Use

The drag-and-drop functionality of the AWS data pipeline allows quick and easy development of your pipeline environment without the need for additional coding. Thanks to built-in templates, even complex use cases can be built out using the visual pipeline creator.

3. Flexibility

The AWS data pipeline gives you a variety of built-in options for scheduling, dependency, and error handling. You can create powerful custom pipelines, for instance, to analyze and process data using the preexisting options without needing to execute your own application logic or worry about reliably scheduling tasks. You can even create your own custom activities or preconditions to suit your organization’s data needs if required.

4. Scalability

Whether you need to facilitate connections between one system or many, the AWS data pipeline is scalable to your organization’s needs. In addition, usability doesn’t become more complex the more data sources or processes you add; thanks to the pipeline’s flexible design, processing one file is just as easy as processing thousands.

The disadvantages of the AWS data pipeline include:

1. Closed Ecosystem

Amazon web services, the pipeline included, are built to be used within the Amazon ecosystem. This means that functionality with programs outside of the ecosystem is either limited or not possible. This may not pose an issue for your organization if you don’t plan on moving outside of the ecosystem, but it does provide limitations and less flexibility if the organization ever wants to transition to a different platform.

2. Requires Additional AWS Services

Because the AWS data pipeline exists within a closed ecosystem, it requires the use of additional AWS services (e.g., Amazon S3 bucket, Redshift, etc.) to support the effective movement of data.

3. Complexity of Branching Logic and Representing Preconditions

AWS data pipeline’s way of representing preconditions and branching logic might prove to be daunting to the beginner. The more complex the chain of events, even with a visual editor, the more complex it is to manage every trigger and workflow. Other tools, such as Apache Airflow, provide a more streamlined solution.

Conclusion

Managing data movement throughout an organization requires the use of powerful technology, including data pipelines. The AWS data pipeline is one example of a system that enables an organization to control data flow while at the same time supporting a democratized approach to data access.

Providing effective yet secure data access is what Revelate is all about. Discover how you can simplify your organization’s data fulfillment by Booking a Demo with us today.

Simplify Data Fulfillment with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started