Organizations generate large volumes of data from various sources in structured and unstructured forms. However, raw data is of little value. Sorting, filtering, and pattern analysis are all necessary before the information may be useful to the organization. A data pipeline allows you to move data from a source to a destination (data warehouse). During this procedure, the data undergoes transformation and optimization to improve its usefulness. It can then be analyzed to understand its worth and applied to business ideas. Modern data pipelines automate nearly all the manual steps in transforming and optimizing the regular flow of data loads.
What Does AWS Data Pipeline Do?
Firstly, AWS Data Pipeline can be handy in defining programmed workflows to ensure the movement and transformation of data. This data as a service makes extraction, loading, and transformation of data accessible.
With AWS Data Pipeline, you don’t need an elaborate ETL or ELT platform to utilize data. Amazon provides established setups and templates. If taught properly, they are easy to master. In addition, the data pipeline relies heavily on computational power to complete its tasks.
Why Is AWS Data Pipeline Important For Businesses?
Data is growing at a colossal pace for nearly all business types across various sectors. It is increasingly becoming a huge challenge for organizations to process, store, manage, and migrate data.
Data processing is a complex task because:
- The bulk of the data generated is generally raw or unprocessed.
- It is a time-consuming task to convert data to a compatible format.
- There are many saving options available.
- The cloud storage options are Amazon Relational Database Service (RDS) or Amazon S3.
Why do Businesses Prefer AWS Data Pipeline?
Information in any given business often exists in several, disparate forms across a wide variety of departments and repositories. Data must be converted into a simple, usable format to improve and manage business operations. The ETL platform is designed to make this happen. It cleans, structures, and transforms data into an actionable format.
Due to the diversity of company activities, each generates its own unique data sets and volumes. Therefore, data cleaning and restructuring is an ongoing process and must be repeated with every new business activity.
In the past, data management was achieved by building on-premise networks. It entailed the hiring of specialists and high costs. It also drew the workforce away from their core value creation tasks from this data.
Additionally, services like Data pipeline helps in putting a full stop to such hassles by providing the convenience of a complete ETL platform as a web service.
Key Features
The key features of the AWS Data Pipeline are:
- The AWS Data Pipeline helps automate workflows between different sources and targets.
- It offers a code-based data transformation option and supports the transformation of operations through different service activities.
- With HadoopActivity, it creates the ability to run user-supplied code in an EMR cluster or on-premise cluster.
- An EMR cluster can be used on a need basis using the EMR activity. The HadoopActivity can be applied to run their processing or transformation jobs.
- Organizations can utilize their on-premise system for data sources or transformation. However, these resources must comply with data pipeline task runners.
- The pricing is very flexible. You use the resources. You pay for time. For other tasks, you just have to pay a flat fee.
- The simple interface enables customers to set up complex workflows quickly.
The Core Concepts and Architectures of AWS Data Pipeline
AWS data pipeline consists of the following components.
Task runners:
As an essential element of computing machines, they perform the crucial task of processing the extraction. Also, they transform and load activities.
Data nodes:
Data nodes identify the data type and the location from which the pipelines can access it. Additionally, it helps identify the input and output data elements.
Activities:
Activities represent the type of work performed on the data. Additionally, the data pipeline supports multiple activities based on the workloads.
Preconditions:
These components provide conditional statements to be complied with for the next pipeline activity to start. Also, they help create a chain of pipeline activities based on custom logic.
Resources:
Resources can be either an EMR or an EC2 instance.
Actions:
It is possible to set up data pipelines such that they only take action under certain situations.
Pros and Cons
Lastly, the pipeline helps businesses leverage the power of the ETL platform via a web service delivered through a well-designed control panel. However, AWS Data Pipeline has its pros and cons.
Pros
- User-friendly control panel with predefined templates
- Need-based spawning of clusters and resources
- Allows scheduling of jobs only on specific periods.
- Keeps data protected at all times – while in transit and rest.
- You need not worry about system stability activity thanks to the fault-tolerant architecture.
Cons
- The data pipeline works for the AWS world and integrates with AWS components. However, it won’t work if you want to bring data from third-party services.
- Multiple installations and configurations can make working with the AWS data pipeline challenging.
Conclusion
To conclude, you don’t need your own ETL infrastructure to leverage the Amazon Data pipeline and seamlessly perform ETL processes if your company employs an ETL that involves the components of the AWS ecosystem. Otherwise, it may be better to go for any other powerful data pipeline platform.
