AWS Data Pipeline is a powerful online tool that lets you transport data between AWS services and on-premise data sources. The service is tailored to present you with a fault-tolerant and highly available platform on which you may build and implement your own bespoke data migration operations.
AWS Data Pipeline also includes optional features like error handling, dependency tracking, and scheduling, so you don’t have to waste time and effort creating them yourself.
When would you use AWS data pipeline?
Because of its ease of use and flexibility, as well as its low running costs, the AWS Data Pipeline service is perfect for use cases like:
- Data from an Amazon DynamoDB table is backed up to Amazon S3.
- Data from an Amazon MySQL database is transferred to an Amazon Redshift cluster.
- Data from files stored in Amazon S3 is incrementally loaded into an Amazon RDS database.
- Data migration from an Amazon EMR cluster to Amazon Redshift for data warehousing on a regular basis
- Periodic backups of files stored in an Amazon S3 bucket, and much more
AWS Data Pipeline is built around a pipeline, as the name suggests. Pipelines can be used to schedule and execute data migration and transformation processes. Each pipeline is based on a pipeline definition, which is effectively the business logic that drives the data migration processes.
Is AWS data pipeline ETL?
The term “data pipeline” denotes the overall process of moving data from system A to system B on the Amazon Cloud Service. Data pipelines of this type are known as ETL pipelines. The following are three important distinctions between the two:
To begin with, data pipelines do not need to be executed in batches. Data is often moved to the target system in batches on a regular basis through ETL processes. Certain data pipelines, on the other hand, can do real-time processing with streaming computation, allowing data sets to be updated in real-time. This allows for real-time analytics and reporting, as well as the activation of additional apps and systems.
Secondly, data pipelines do not require data transformation. Before data is loaded to the target system, it is transformed by ETL pipelines. However, data pipelines have the option of transforming data after it has been loaded into the destination system (ELT) or without transforming it at all.
Thirdly, data pipelines do not have to come to a halt once the data has been loaded. After importing data into the target repository, ETL pipelines come to an end. Data pipelines, on the other hand, can stream data, allowing them to activate processes in other systems or provide real-time reporting.
Why you should use AWS Data Pipeline
Whenever it comes to data management or administration, there are three crucial elements to consider. There’s data generation, also known as online transaction processing (OLTP). There’s also data analysis, which is known as online analytical processing (OLAP). Both can entail a number of different systems.
Then there’s the process of transferring data from one system to another. Copying data, moving it from on-premises to the cloud, reformatting it, merging it with other sources of data, and other actions are all possible. Each step may necessitate the use of distinct applications. This is where the data pipeline comes into the equation.
A data pipeline allows data to flow smoothly and automatically from one point to the next. It specifies what data is gathered, where it is collected, and how it is collected. The process of obtaining, processing, merging, validating, and loading data that can be analyzed and visualization is then automated. It also ensures end-to-end speed by removing errors and reducing bottlenecks or latency.
AWS Data Pipeline Vs Glue
AWS Glue is frequently compared to the Data Pipeline as one of the greatest ETL solutions available. To make an accurate comparison, we must first understand what AWS Glue is.
AWS Glue is a service from Amazon Web Services that makes it easier to create, transform, and load datasets. It primarily functions as an ETL (Extract, Transform, and Load) tool. In big data, it falls under the “Data Catalog” category. We will differentiate these terms under 2 major categories:
Sources of Data
You can’t add more data sources to the AWS Data Pipeline as a data transfer tool. You must operate with the data sources that have been defined.
AWS Glue, on the other hand, allows you to define custom sources to link data that isn’t synchronized with AWS.
Both AWS Glue and Data Pipeline have different pricing mechanisms. AWS Data Pipeline bills on an activity basis, whereas AWS Glue bills on an hourly basis.
You have two options for purchasing the AWS Data Pipeline, depending on your needs.
Low-frequency and high-frequency models are the two types of models. Low-frequency plans cost roughly $0.6 per month per activity, whereas high-frequency plans cost around $1 each month per activity.
On the other hand, you’ll have to pay $0.44 per hour per DPU for AWS Glue. This works out to $21 each day. It also provides some freebies. The first 1 million objects are free to store, and the first 1 million instances are also free to access.