Applications rely on a treasure trove of data that is constantly on the move -- known as a data pipeline. While there may be a vast amount of data, the concept is simple: An app uses data housed in one repository and it needs to access it from a different repository, or the app uses one Amazon service and needs to use a different one. It might be due to the business requirements changing or that you need to use a different database entirely. It might be due to a new reporting need or a change in the security requirements. This data pipeline can involve several steps -- such as an ETL (extract, transform, load) to prep the data or changes in the infrastructure required for the database -- but the goal is the same: the act of moving the data without any interruptions in workflows and without errors or bottlenecks along the way.
Fortunately, Amazon offers AWS Data Pipeline to make the data transformation process much smoother. The service helps you deal with the complexities that do arise, especially in how the infrastructure might be different when you change repositories but also in how that data is accessed and used in the new location. An example of this might be a specific executive summary that is needed at a certain time of the day that provides details about transactional data for an app that handles user subscriptions. Moving the data is one thing; making sure the new infrastructure supports the reporting you need to find is another.
Essentially, AWS Data Pipeline is a way to automate the movement and transformation of data to make the workflows reliable and consistent, regardless of the infrastructure of data repository changes. The service handles all of the data orchestration based on how you define the workflows and is not limited to how you store the data or where it is stored. The tool helps you manage the data dependencies and automate them and also handles the data pipeline scheduling you need to do to make sure an app, business dashboard, or reporting works as expected. The service also informs you about any faults or errors as they occur.
It won’t matter which compute and storage resources you use, and it won’t matter if you have a combination of cloud services and on-premise infrastructure. AWS Data Pipeline is designed to keep the process of data transformation straightforward, without making it more complicated due to how you have the infrastructure and the repositories defined.
Benefits of AWS Data Pipeline
As mentioned earlier, many of the benefits of using AWS Data Pipeline have to do with how it is not dependent on the infrastructure, where the data is located in a repository, or even which AWS service you are using (such as Amazon S3 or Amazon Redshift). You can still move the data, integrate it with other services, process the data as needed for reporting activities and for your applications, and perform other data transmission duties.
All of these activities are conducted within an AWS console that uses a drag-and-drop interface. This means even non-programmers can see how the data flows will operate and how to adjust them within AWS without having to know about the back-end infrastructure and how it all works. As an example of this is when data needs to be accessed within an S3 repository -- in the console, the only change to make is the name of the repository within S3. The end-user doesn’t need to adjust the infrastructure or accommodate the data pipeline in any other way.
AWS Data Pipeline also relies on templates to automate the process, which also helps any end-user adjust which data is accessed and from where. Because of this simple, visual interface, a business can meet the needs of users, executives, and stakeholders without having to constantly manage the infrastructure and adjust the repositories. It speeds up the decision-making for a business that needs to make quick, on-the-fly adjustments to how they process data and the new reporting, summaries, dashboards, and data requirements.
A monthly subscription fee for AWS Data Pipeline makes the service more predictable in terms of the expected costs, and companies can easily sign up for the free base level subscription to see how it all works using actual data repositories. And, because the service is not dependent on a set infrastructure in order to help you move and process data, you can pick and choose which services you need, such as AWS EMR (Amazon Elastic MapReduce), Amazon S3, Amazon EC2, Amazon Redshift, or even a custom on-premise database.
Related to all of this (the simple interface, low cost and flexibility) is an underlying benefit of automated scaling. Companies can run only a few data transformation jobs or thousands, but the service can accommodate any requirements and scale up or down as needed.
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