AWS Data Pipeline is a web service that lets you process and moves data at regular intervals between AWS computing and storage services, as well as on-premises data sources. It enables you to develop fault-tolerant, repeatable, and highly available complex data processing workloads.
This article will give you a comprehensive guide to AWS Data Pipeline. You will get to know about the architecture and key features of the AWS Data Pipeline. You will also explore the Features, Pricing, Advantages, Limitations, and many more in further sections. Let’s get started.
Table of Contents
- Prerequisite: What is Data Pipeline?
- What is AWS Data Pipeline?
- Why understanding the AWS Data Pipeline is important?
- Benefits of AWS Data Pipeline
- Key Features of AWS Data Pipeline
- Core Concepts and Architectures of AWS Data Pipeline
- Pros and Cons of AWS Data Pipeline
- Alternatives to AWS Data Pipeline
- Pricing of AWS Data Pipeline
- Working with AWS Data Pipeline
- Deleting AWS Data Pipeline
Prerequisite: What is Data Pipeline?
A data pipeline is a means of moving data from one location (source) to a destination (such as a data warehouse). In the process, the data is transformed and optimized to obtain a state that can be used and analyzed to develop business ideas. Essentially, a data pipeline is a stage involved in aggregating, organizing, and moving data. Modern data pipelines automate many of the manual steps involved in transforming and optimizing continuous data loads.
What is AWS Data Pipeline?
AWS Data Pipeline offers a web service that helps users define automated workflows for the movement and transformation of data. In other words, it offers extraction, load, and transformation of data as a service.
Users need not create an elaborate ETL or ELT platform to use their data. With proper AWS training, they can exploit the predefined configurations and templates provided by Amazon. Most operations performed by the Data pipeline involve using computing power other than the source and target database and this power comes from Amazon’s computing services like EMR.
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Why understanding the AWS Data Pipeline is important?
Data is growing at a massive rate. Data processing, storage, management, and migration are more complex and time-consuming than ever before. The following factors make data processing difficult:
- Mostly raw or unprocessed bulk data is generated. Generating various data types Data is unstructured.
- Converting data to a compatible format is a tedious task. Multiple Save Options There are many saving options available.
- This includes data storage or cloud storage such as Amazon S3 or Amazon Relational Database Service (RDS).
AWS Data pipeline is one of the solutions for the ETL tool. It supports multiple Amazon cloud storage.
Benefits of AWS Data Pipeline
The benefit of an ETL platform stems from the fact that data in a typical organization is scattered across multiple sources in multiple formats. To make any use of this data to improve business, it needs to be cleaned and transformed into actionable forms.
This is not a one-time process and needs to be repeated periodically as the data in these sources grow with every single business activity.
Traditionally organizations used to build complex internal on-premise networks to accomplish this activity. This meant a lot of effort was spent on developing and maintaining this platform, distracting the workforce from actually creating value from this data.
This is where services like Data pipeline comes in, offering all the convenience of a complete ETL platform as a web service.
Key Features of AWS Data Pipeline
AWS Data Pipeline has gained wide popularity in the market. Some of the key features of AWS Data Pipeline include:
- As mentioned earlier, the AWS Data pipeline allows automating workflows between different sources and targets. It supports most of the AWS sources as well as typical on-premise sources like JDBC-based databases.
- Data pipelines allow users to schedule these operations or chain them based on the success or failure of upstream tasks.
- It supports comprehensive transformation operations through different service activities like HiveActivity, PigActivity, and SQLActivity. Option for a custom code-based transformation is supported through HadoopActivity with its ability to run user-supplied code in an EMR cluster or on-premise cluster.
- Customers can choose to start an EMR cluster only when required using the EMR activity and then use a HadoopActivity to run their processing or transformation jobs.
- It allows the customers to make use of their on-premise system for data sources or transformation, provided these compute resources are set up with data pipeline task runners.
- It provides a very flexible pricing regime with the user only having to pay for the time when the compute resources are being used and a flat fee for periodic tasks.
- It provides a very simple interface that enables the customers to set up complex workflows with just a few clicks.
Core Concepts and Architectures of AWS Data Pipeline
Conceptually AWS data pipeline is organized into a pipeline definition that consists of the following components.
- Task runners: Task runners are installed in the computing machines which will process the extraction, transformation, and load activities. Task runners are responsible for executing these processes as per the schedule defined in the pipeline definition.
- Data nodes: Data nodes represent the type of data and the location from which it can be accessed by the pipelines. This includes both input and output data elements.
- Activities: Activities represent the actual work that is being performed on the data. The data pipeline supports multiple activities that can be chosen according to the workloads. Typical activities are listed below.
- CopyActivitiy: Used when data needs to be copied from one data node to another.
- EmrActivity: Activity for starting and running an EMR cluster.
- HiveActivity: Runs a hive query.
- HiveCopyActivity: Runs a pig script in the AWS EMR cluster.
- RedshiftCOpyActivity: Runs a copy operation to the Redshift table.
- ShellCommandActivity: For executing a Linux shell command or a script.
- SQLActivity: Runs an SQL command on supported databases. The data pipeline supports JDBC databases, AWS RDS databases, and Redshift.
- Preconditions: These are pipeline components with conditional statements which must be true for the next pipeline activity to start. These are used for chaining pipeline activities based on custom logic.
- Resources: Resources for an AWS Data pipeline are usually an EMR or an EC2 instance.
- Actions: Data pipelines can be configured to execute certain actions when specific conditions meet or certain events occur. These are typically notifications or termination requests.
Pros and Cons of AWS Data Pipeline
AWS Data pipeline unleashes the full power of an ETL platform in the form of a web service with a very comprehensive control panel. That said, it is not without its cons. The below section details the pros and cons of the service from an ETL developer’s point of view.
- Simple to use control panel with predefined templates for most AWS databases.
- Ability to spawn clusters and resources only when needed.
- Ability to schedule jobs only on specific time periods.
- Full security suite protecting data while in motion and rest. AWS’s access control mechanism allows fine-grained control over who can use what.
- Fault-tolerant architecture – Relieves users of all the activities related to system stability and recovery.
- The data pipeline is designed for AWS services or in other words AWS world and hence integrates well with all the AWS components. AWS Data Pipeline is not the right option if you need to bring data from different third-party services.
- Working with data pipeline and on-premise resources can be overwhelming with multiple installations and configurations to be managed on the compute resources.
- The data pipeline’s way of representing preconditions and branching logic can seem complex to a beginner and to be honest, there are other tools out there that help to accomplish complex chains more easily. An example is a framework like Airflow.
Alternatives to AWS Data Pipeline
As mentioned above, the AWS data pipeline is not without its cons and can make easier jobs seem complex if there are components outside the AWS universe. In such cases, your needs may be better served by a fully managed data integration platform like Hevo.
Simplify ETL Using Hevo’s No-code Data Pipeline
Hevo Data helps you directly transfer data from 100+ data sources (including 30+ free sources) to Business Intelligence tools, Data Warehouses, or a destination of your choice in a completely hassle-free & automated manner. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Hevo takes care of all your data preprocessing needs required to set up the integration and lets you focus on key business activities and draw a much more powerful insight on how to generate more leads, retain customers, and take your business to new heights of profitability. It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination.Get Started with Hevo for Free
Check out what makes Hevo amazing:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and your data volume grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
2) AWS Glue
AWS Glue is a fully-managed Extract, Transform, and Load (ETL) service that makes it easy and cost-effective to categorize your data, clean, enrich, and move it between reliably disparate data stores and data streams. AWS Glue is a tool in the Big Data Tools category of a technology stack.
Apache Airflow is a workflow engine that makes it easy to plan and run complex data pipelines. This ensures that each job in the data pipeline runs in the correct order and each job gets the resources it needs. It provides an amazing user interface to monitor and troubleshoot problems.
4) Apache NiFi
Apache NiFi is open source software for automating and managing the flow of data between source and destination. A robust and reliable system for data processing and distribution. Provides a web user interface for creating, monitoring, and managing data flows. You have a dataflow process that you can easily customize and modify to change data at runtime.
Pricing of AWS Data Pipeline
The data pipeline is priced in terms of activities and preconditions that are configured in the console and their frequency of executions. AWS classifies the frequency of executions as low in the case of activities that are executed up to once per day.
All activities that are executed more than once per day are high-frequency activities. The low-frequency one on AWS is charged at $.6 per month and the one on on-premise systems is charged at $1.5 per month. High-frequency activities start at $1 per month and go up to $2.5 per month for on-premise systems.
All the resources used in the pipeline activity like EC2 instances, EMR clusters, Redshift databases, etc are charged at the normal rates and come above the pipeline pricing. Above mentioned charges are only for the pipeline features.
Working with AWS Data Pipelines
Working with the AWS pipeline is all about pipeline definitions. Let us look into setting up a simple AWS pipeline for copying data from RDS to Redshift.
You need to have an AWS account before you can proceed with the working of the AWS data pipeline. This can be done based on predefined templates from AWS, saving us quite a lot of configuration effort.
- From the AWS console, go to the data pipeline and select the ‘Create new pipeline’. This will take you to the pipeline configuration screen.
- Enter the name and description of the pipeline and choose a template. Here we choose ‘incremental copy of MySQL RDS to Redshift’. There is also another option to configure the pipeline using the Architect application for more advanced use cases.
- After selecting the template, it is time to fill in the parameters for the data nodes we are using in this case. Fill up the parameters for the RDS MYSQL instance.
- Configure the Redshift connection parameters.
- Select the schedule for the activity to run. You can either select a schedule or enable a one-time run on activation.
- The next step is to enable the logging configuration. We suggest you enable this for any kind of pipeline activity and point the login directory to an S3 location. This can be very useful for troubleshooting activities later. Click ‘Activate’ and you are good to go.
Deleting AWS Data Pipeline
Deleting your AWS data pipeline will delete your pipeline definition and its associated objects. Let’s look at the steps to delete your AWS data pipeline:
- Click on the List Pipelines and then select the pipeline which you want to delete.
- Click Actions and select Delete.
- Confirm your delete operation by clicking on Delete again.
If your ETL involves AWS ecosystem components only, then the AWS Data pipeline is an excellent choice for implementing ETL workflows without having to maintain an ETL infrastructure on your own. That said, it is not without its quirks and we have attempted to explain the less elegant bits in the above sections.
If your use spans beyond AWS components or if you are looking to implement a fuss-free ETL, it may be better to use robust data pipeline platforms such as Hevo Data that provide much more flexibility along with an enterprise-grade data migration experience.Visit our Website to Explore Hevo
Businesses can use automated platforms like Hevo Data to set the integration and handle the ETL process. It helps you directly transfer data from 100+ free sources of your choice to a Data Warehouse, Business Intelligence tools, or any other desired destination in a fully automated and secure manner without having to write any code and will provide you a hassle-free experience.
Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.
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